Keywords
Natural language processing review - medical terms - social media - mining electronic
health records
1 Introduction
The need to embrace the patient’s perspective in health-related research and quality
of care measures is one point on which all major health organizations around the world
agree. Indeed, the World Health Organization in Europe prominently lists “patient and community participation or direction” [[1]] as a practical and proven quality improvement approach in their guidelines to developing
quality and safety strategies. Relevant to the approaches we review here, guidelines
note that “work would be needed to find the best ways to introduce [patient and community participation], building on local culture and existing structures.” Around the world, numerous initiatives were launched in the last 10 years to incorporate
the patient’s perspective, including efforts such as patient survey programs by the
Care Quality Commission (CQC) in England, the “Better Together” effort of the Scottish
Government, the Institute for Quality and Efficiency in Health Care (IQWiG) in Germany,
and the Patient-Centered Outcomes Research Institute (PCORI) and the Patient-Focused
Drug Development (PFDD) programs in the United States. Patient-reported outcomes (PROs)
[[2]] and measurement instruments, which are extensively validated questionnaires to
measure patients’ symptoms and quality of life such as the PROMIS [[3], [4]] set designed by the NIH, have become the standard way to collect the patient’s
perspective. However, distributing and getting an adequate number of responses to
these questionnaires is a constant challenge, and does not necessarily build “on local culture and existing structures”.
In this paper, we reviewed the recently published literature discussing Natural Language
Processing (NLP) methods that could help go beyond structured questionnaires to find,
extract, and incorporate information related to the patient’s perspective from unstructured
fields (text) found in different sources such as social media, patient speech, patient/therapist
interactions, and the information recorded in the free text fields of clinical records.
This overcomes the problem of the targeted distribution and collection of questionnaires
by proactively extracting information reflecting the patient’s perspective where it
is already present. We also reviewed advances in the closely related field of machine
learning, which offered several milestone developments in the recent past, such as
cognitively grounded semi-supervised or unsupervised methods. In many cases, the NLP
community embraced and adapted these automated learning methods to the specifics of
NLP tasks, given that such methods are now capable of utilizing information from labeled
and unlabeled data. Relevant to the specific focus of this review, these methods enable
the use of large sets of clinical records and health-related user-generated content
from social media microblogs (Twitter, Facebook, Reddit, and Instagram, to name a
few) and health forums (such as DailyStrength, WebMD, and others).
The review by Meystre et al. [[5]] presents an excellent overview of health-related text processing and its applications
until 2007. The research presented in the present manuscript includes, for social
media and clinical records, advances to fundamental NLP methods such as classification,
concept extraction, and normalization published since 2008, mostly omitting what has
been included in similar recent reviews [[6]–[8]], except when required to complete and highlight recent advances. For each data
source, after reviewing advances in fundamental methods, we reviewed specific applications
that capture the patient’s perspective for specific conditions, treatments, or phenotypes.
For the applications, we built on the 2016 review by Demner-Fushman and Elhadad [[7]] and highlighted major achievements from 2013 to 2016 that are relevant to the patient’s
perspective focus of this review. The search and selection criteria used were similar
to the ones used by Névéol and Zweigenbaum [[8]], from January 1st, 2013 through December 31st, 2016, resulting in 464 papers. A
total of 62 papers focusing on clinical records were selected from this set. For social
media, most of the literature has been published within this same time frame; a total
of 87 papers were included in this review. Research was included if it was published
within the time window of interest and it was, to the best of our judgment, classified
as either: (1) an advance to fundamental methods for health-related natural language
processing (Health Language Processing, or HLP hereafter) and is applicable to more
than a single condition, treatment, or phenotype, or (2) an HLP method applied to
specific conditions, treatments, or phenotypes, but focused on capturing the patient’s
perspective. References dated outside the window of interest were included when required,
and their use does not imply they are part of the set of papers defined by this inclusion
criterion.
We understand the patient’s perspective to include reports of disease progression
and manifestation (symptoms) given directly by the patient, which corresponds to the
definition of patient-reported outcomes given by the FDA [[9]], although the level of detail and completeness of information collected using HLP
methods will often lag behind what can be collected with patient-reported outcome
instruments. However, we also consider of interest for most secondary use of heath
data the collective patient perspective, “an understanding of the disease experiences of many patients” [[10]] that share specific characteristics. Thus, either written by the patients themselves
as in social media, or extracted from clinical notes written by a health provider,
unstructured text captures the health narrative from the patient and reflects the
characteristics shared by a group of patients (such as suffering from a specific disease
or symptom, or smoking status). HLP allows the collection of aspects of the patient’s
perspective that are more complex than what surveys are designed to collect. Consider
for example the nuanced descriptions of quality of life and mental state, such as
the ones in [Figure 1], which are postings in social media from someone with inflammatory bowel disease/colitis.
The postings speak about general routine exercise, well-being, and attitude, in a
way that would be difficult to capture in a survey, and they are potentially available
in much greater volume, over longer periods of time, and for a large number of patients
in social media. Going beyond the structure of surveys greatly increases the number
of reports (and derived knowledge) that can be incorporated, aggregating many more
patient reports of cohorts with some unifying characteristic, such as a specific condition.
Finally, we include time as a unique dimension of the patient’s perspective, and review
HLP methods tailored to capturing events related in time (for example, the patient
reporting a pre-existing condition, or a recent treatment with antibiotics), or specific
moments in a “health timeline” (such as when pregnancy was revealed, or when the flu
shot was taken).
Fig. 1 Postings in a timeline progressing after a colitis flare-up that lasted 6 weeks.
The paper is organized in two main parts. Part 1 focuses on NLP of electronic health
records (EHRs) and Part 2 focuses on NLP of health-related social media text. Within
each part, we describe the developments in fundamental tasks and various applications.
Our intent is to provide the reader with a scope of the trends and advances in capturing
the patient’s perspective on health within the last three years, as outlined before.
2 Mining Electronic Health Records
2 Mining Electronic Health Records
While one could argue that the EHR does not provide a true “patient perspective” according
to the FDA’s definition [[9]], as its contents are invariably captured by a health care provider, we posit that
it contains some unique aspects of the patient perspective: the manifestation of symptoms
unique to the patient, or a narrative of what the patient expressed (i.e., “patient
complains of vertigo…”). In aggregate, EHRs constitute an important source of the
collective patient perspective, as defined before. In order to capture this, however,
it is necessary to pinpoint the concepts of interest, map them to standardized vocabularies,
and put them in temporal context (a timeline) in order to then derive further knowledge.
We outline the advances on each of these tasks next.
2.1 Concept Extraction and Normalization
The field has long advanced beyond the simple keyword search functionalities that
are widely implemented in commercial EHR systems as well as research platforms such
as i2b2 [[11]]. The limitations of keyword search strategies are well-known. A keyword-based retrieval
will only match documents that contain the keyword exactly as typed (with some limited
flexibility allowed – such as suggestions when alternate spellings are found). Advanced
NLP enables finding concepts rather than keywords. A concept-based retrieval system
can identify a concept expressed in many different ways (including synonyms as well
as generalizations or specifications of a concept). For example, if EHRs mentioning
adverse reactions to a specific medication are needed, the researcher would have to
include keywords to cover all variants and specific types of reactions in order to
use a keyword-based search. The situation is even worse if, for instance, a cohort
of people involved in a motor vehicle accident is the desired set. In a keyword-based
system, the user would need to specify one by one all motor vehicles, while an efficient
concept-based system will retrieve them automatically if “motor vehicle” is specified
as the query. Only a handful of systems are currently generally available to automatically
find concepts in free text from clinical narratives, the most established being MedLEE
(Medical Language Extraction and Encoding) [[12]], and cTAKES [[13]]. Other systems are Noble Coder [[14]], CLAMP (http://clamp.uth.edu/), MetaMap Lite (https://metamap.nlm.nih.gov/download/new/), and DNorm adaptation for clinical text [[15]].
Normalization of concept mentions involves matching the extracted concept to a unique
identifier from a medical ontology, usually one from the UMLS Metathesaurus [[16]]. The UMLS provides a vast coverage of medical terminology in English, and it has
been used previously for disease normalization [[17]–[19]]. Recently, other lexicons have been developed for normalization in other languages,
for example the QUAERO French medical corpus [[20]], the CLEF eHealth information extraction tasks of 2015 and 2016 (clinical entity
and concept recognition from French, ICD-10 coding of causes of death for French),
and the NTCIR [[21]] 2015 and 2016 tasks on ICD-10 coding in Japanese. Regardless of the language, most
of the research to date on concept normalization has used some variations of dictionary
lookup techniques and string matching algorithms.
With the advances in machine learning techniques and the increased availability of
annotated data, recent approaches have used learning-based algorithms to improve basic
dictionary matching techniques. For tasks such as gene name normalization, some of
these works have involved list-wise learning, which learns the best list of objects
associated with a concept and returns the list rather than a single object [[22], [23]], graph-based normalization [[24]], conditional random fields [[25]], regression-based methods [[26]], and semantic similarity techniques [[27]]. Leaman et al. [[28]] applied pairwise learning from a specialized disease corpus for disease name normalization.
Mapping free text to concepts in an ontology has been done by Gobbels et al. [[29]] who used a Naïve Bayes machine learning system to match phrases from clinical records
to SNOMED ontology terms, whereas Kate [[30]] used learned edit distance patterns to normalize clinical text to UMLS IDs. Other
approaches have used tools such as MetaMap [[31]] or cTAKES [[13]], to extract and map terms to concepts in the UMLS [[32], [33]]. The tools are generally effective for clinical text, but are not portable: they
miss relevant information when applied to colloquial language as the one used in social
media [[34]].
Several shared tasks have taken on the challenge of normalization using clinical texts.
Using the ShARe corpus, participants in the 2014 ShARe/CLEF Task 2 were tasked with
normalizing semantic modifiers related to disease mentions in clinical texts [[35]]. The submitted systems obtained accuracies ranging from 0.769 to 0.868 [[36]]. The SemEval 2014 Task 7 challenged participants to automatically identify and
normalize diseases and disorders in clinical texts [[37]]. The approaches used ranged from rule-based classifiers to hybrid rule-based/machine
learning classifiers. The latter approach typically led to a higher performing system.
For the top performing system, Zhang et al. [[38]] developed a machine learning NER module and an ensemble learning module used as
a binary classifier to determine if the NER module output was a true positive for
disorder entity recognition. For normalization, they developed a Vector Space Model
to assign the most appropriate UMLS Concept Unique Identifier (CUI). The 2015 SemEval
task related to clinical texts again presented the task of disorder entity recognition
and normalization as well as template slot filling [[39]]. For disorder span recognition, CRF approaches were the most utilized. Pathak et
al. [[40]] developed a CRF system to locate contiguous disorder mentions and for disjointed
disorder mentions, a support vector machine (SVM) binary classifier was developed
to determine if the two mentions were related or not. This approach allowed them to
detect disjointed mentions with about 70% accuracy. For the normalization, they divided
the task into three parts: a direct dictionary match, a dictionary match on a modified
UMLS that was parsed into phrases, and a string similarity algorithm. Xu et al. [[41]] extended the work they had done in the prior year [[38]], including the training of a deep neural network using the unlabeled MIMIC II corpus
to obtain word embeddings that were used as a feature in their system. Results are
summarized in [Table 1].
Table 1
Relevant shared tasks results, information extraction, and normalization
|
Shared Task Results
|
|
IE and Normalization
|
|
Challenge
|
Year
|
Corpus
|
Task Description
|
Number of Participants
|
Results Range / Measurement
|
|
ShARe/CLEF
|
2014
|
ShARe[*]
|
Normalization values of 10 attributes
|
10
|
0.769-0.768/Accuracy
|
|
Sem-Eval
|
2014
|
ShARe
|
Identification of disorder mentions
|
21
|
Strict: 0.787-0.448/F1
|
|
Relaxed: 0.975-0.717/F1
|
|
SemEval
|
2014
|
ShARe
|
Normalization of mentions to SNOMED-CT
|
18
|
Strict: 0.716-0.299/ accuracy
|
|
Relaxed: 0.923-0.584/ accuracy
|
|
SemEval
|
2015
|
ShARe
|
Identification of disorder mentions and normalization to CUI
|
16
|
Strict: 0.757-0.093/F1
|
|
Relaxed: 0.787-.0364/F1
|
* http://share.healthnlp.org/ [accessed: April 1st, 2017]
2.2 Timeline Extraction
The 2015 Clinical TempEval challenge presented the task of extracting temporal information
from clinical notes. The participants were tasked with identifying time and event
expressions and attributes of those expressions, and the temporal relations between
them [[42]]. Three teams participated, with one participating in all tasks and subtasks. All
systems employed supervised classifiers for the challenge and all outperformed the
rule-based systems that were used as baselines. The top performing system [[43]] utilized a combination of CRF, SVM, and rule-based approaches. The 2016 Clinical
TempEval presented similar tasks and utilized the same corpus as the prior year. However
the number of participants increased [[44]]. The submitted systems used differing machine learning approaches with the top
systems utilizing HMM SVM [[45]] and CRF [[46]]. As in the prior year, the top systems achieved a high level of performance for
the time and event identification tasks, reaching F-scores close to those of human
annotators. The temporal relation tasks proved to be more difficult; however, marked
progress was made from the prior year’s systems. A summary of the results with the
ShARe corpus is included in [Table 1].
2.3 Knowledge Discovery
The fundamental methods outlined before are generally a basic requirement in any HLP
pipeline for secondary use of health data. Recently, interesting directions have been
explored where the collective patient perspective data from EHRs has been used to
create disease progression and medical decision-making models, to assist in reaching
potential diagnoses and assessing risk factors. We review here some of these HLP applications.
Disease Progression Models
To better understand disease evolution patterns, Lui et al. [[47]] proposed a temporal graph-based representation of patient EHR data. The graph framework’s
predictive power was tested on the onset risk of heart failure and on the risk of
heart-related hospitalizations for COPD patients. On each of these two tasks the system
obtained an AUC of 0.72. Using an EHR database of 300,000 patient records, Wang et
al. [[48]] developed an unsupervised disease progression model using a combination of statistical
methods. Applying the model to chronic obstructive pulmonary disease (COPD), they
identified comorbidities and inferred a progression trajectory model of the disease.
Pham et al. [[49]] created a system, DeepCare, to predict the next stage of disease progression and
the risk of unplanned readmissions. The system models illness trajectories and predicts
future outcomes. Based on a deep dynamic neural network, it incorporates irregular
timing and interventions to infer future prognosis. Tested on a cohort of 12,000 diabetic
patients, the system prediction of readmissions achieved an F-score of 0.791, which
was an improvement over the baseline method score by 0.077.
Decision Support
For creating a decision-making model, Liang et al. [[50]] proposed a modified deep belief network to simulate human thinking procedures.
Features were extracted through an unsupervised method and a supervised method, SVM,
was used for final decision-making. This semi-supervised approach outperformed standard
SVM and decision tree methods on EHR data. Wang et al. [[51]] used clinical notes of the Maine Health Information Exchange (HIE) EMR database
to train and test an NLP system to find uncoded cases of congestive heart failure
(CHF). The classifier identified 2,411 instances of CHF out of 253,804 cases. The
system had and F-score of 0.753, which outperformed prior methods.
Risk Assessment
Karmaker et al. [[52]] used machine learning to analyze EHRs to assess the risk of suicide based on physical
illness. After extracting illnesses based on ICD-10 codes, they developed six modules,
differentiated by range of time of included illnesses, to predict suicide risk. The
maximum AUC, 0.71, was obtained by incorporating illnesses across all time in the
model. Overall, improvements in risk assessment were seen over increasing time ranges
and all models were an improvement over the AUC of the clinically assessed score.
In assessing the risk of coronary artery disease (CAD), Jonnagaddala et al. [[53]] developed a rule-based text mining system to identify and extract Framingham risk
factors from the unstructured text of EHRs in a cohort of diabetic patients. The results
were used to calculate the Framingham risk score (FRS). This approach showed the feasibility
of extracting such information, however, it did have some limitations including the
lack of temporal information and a lack of clinical context for the information extracted.
An extension of this study [[54]] employed machine learning components to assign an indicator and time attributes,
where applicable. A Naïve Bayes supervised classifier was developed to assign the
time attribute. The system achieved an overall micro-averaged F-score of 0.83. Chen
et al. [[55]] developed a hybrid system based on machine learning and a rule-based approach to
identify risk factors for heart disease in the same cohort of patients. A pipeline
system that included the use of SSVMs, SVMs, and CRF was used to extract information.
Overall, this system achieved an F-score of 0.9268.
Risk Prevention
Due to the readmission penalty program initiated by the Centers for Medicaid and Medicare,
providers have attempted to assess patients at high risk for readmissions. These patients
are targeted to receive enhanced interventions. In an effort to automate the detection
of such patients, Zheng et al. [[56]] compared neural networks, random forests, and a hybrid model of intelligent swarm
heuristics and support vector machines (PSO-SVM) in a cohort of heart failure patients.
The PSO-SVM outperformed the other methods with an accuracy of 78.4%. It improved
upon the currently used LACE score which has an accuracy of 43.5%. Futoma et al. [[57]] tested five models to determine which would perform best at predicting patients
at high risk for readmissions. The models were tested on 280 patient cohorts determined
by diagnosis-related groups (DRG). They found that deep neural networks outperformed
regression models and had consistently better AUCs over the cohorts. They also observed
a slightly better AUC for predictions based on individual DRGs rather than on the
data as a whole.
Adverse Drug Effects Discovery (ADE)
Drug safety studies have also used EHR data. From the free text of psychiatric EHRs,
Iqbal et al. [[58]] used the GATE NLP framework to identify possible extrapyramidal side effects with
dictionary matching and a rule-based approach. ADEs were detected with an 85% precision
and 86% recall, and the results were used to perform a secondary analysis of the prevalence
of ADEs based on subgroups of patients. However, they did not attempt to assess causality
to determine the probability of discovering true drug-ADE pairs. To address the problem
of confounders, Li et al. [[59]] used the NLP system MEDLee to structure and encode narrative notes. Temporal information
was also extracted. A statistical method used penalized logistic regression to estimate
confounder-adjusted ADE associations. This method identified several drug safety signals
that warranted further clinical review. Wang et al. [[60]] developed a discriminative classifier to automatically detect potential drug-ADE
pairs. After constructing a set of features, three classifiers were tested and the
random fields classifier was determined to be superior. The classifier achieved an
AUC of 0.94, which exceeded 0.79, the AUC of the method used by the FDA’s Adverse
Event Reporting System (FAERS).
Off-label Use Discovery
Clinical notes have also been used to detect off-label drug use by using NLP methods
to extract used-to-treat mentions. Jung et al. [[61]] trained an SVM classifier to detect potential off-label usage from 9.5 million
free text clinical notes. After filtering for known drug-indication pairs and drug-adverse
event pairs, the system identified 403 novel off-label usages. Drug repurposing signals
have also been validated using EHR data. To validate reports that metformin improves
cancer survival and reduces cancer risk, Xu et al. [[62]] automatically extracted information from a cohort of cancer patients, including
diabetes status and other covariates including drug exposure. A stratified Cox regression
model was used to assess metformin influence on cancer survival probabilities and
confirmed reports associating its use with lower cancer mortality.
Cancer-related Information Extraction
A number of studies use various techniques to extract information from cancer-relevant
clinical text. A group of studies employ document classification techniques to discover
various oncology categories. This approach is taken mainly because there is document-level
gold data already available for the purpose of cancer registry abstraction. Yala et
al. [[63]] extracted breast cancer-related information from pathology notes for the following
types: (a) Diseases/disorders such as Ductal Carcinoma In Situ (DCIS), Invasive Lobular Carcinoma (ILC), carcinoma, Lobular Carcinoma In Situ (LCIS),
Atypical Ductal Hyperplasia (ADH), lobular neoplasia, flat epithelial atypia, blunt
adenosis, atypia, (b) positive and negative lymph nodes, (c) biomarkers/receptors and their values
such as Estrogen Receptors (ER), Progesterone Receptors (PR), and HER2, and (d) breast side/laterality. They used machine learning where the features were
the n-grams from the pathology reports and the classification label was one of (a)-(d).
The gold classification labels were created in previous breast cancer studies. Weegar
and Dalianis [[64]] created a pilot rule-based system for information extraction from breast cancer
pathology notes in Norwegian – sentinel nodes, axillary nodes, tumor size, histological
grade, ER, PR, Ki67, pT. The system was trained and tested on a very small dataset
and conceived as a pilot study to generate fodder for a more sophisticated system
whose architecture is presented by the authors. Ou and Patrick [[65]] built a system for information extraction from melanoma pathology reports. The
system passed noun phrases produced by the GENIA tagger in a pre-processing step to
a conditional random fields classifier that detected named entities. Reported performance
was on-par with that of humans. However, it is not clear whether the named entities
were further linked by relations, e.g., whether a named entity mention of type site and laterality was linked to a specific tumor. The very recent work of the DeepPhe team (cancer.healthnlp.org)
moves beyond entity mention-level recognition to episode- and patient- levels over
the entire set of patient records (pathology, oncology, clinical, and radiology reports)
where multiple tumors associated with types of cancer are described [[66]]. These tasks require sophisticated extraction techniques such as coreference resolution,
relation extraction, and temporal relation extraction to achieve reliable summarization.
Temporal Data (see [Table 2])
Table 2
Results from shared tasks related to temporal relation extraction from clinical records
|
Temporal Extraction
|
|
Challenge
|
Year
|
Corpus
|
Task Description
|
Number of Participants
|
Results Range / Measurement
|
|
i2b2
|
2012
|
i2b2[1]
|
Identification of time expressions
|
10
|
0.92-0.83/F1[a]
|
|
Identification of event expressions
|
10
|
0.66-0.45/F1[a]
|
|
Identification of temporal relations
|
10
|
0.69-0.43/F1
|
|
SemEval
|
2015
|
THYME[2]
|
Identification of time expressions
|
3
|
0.725-0.404/F1[a]
|
|
Identification of event expressions
|
1
|
0.875/F1[a]
|
|
Identification of temporal relations
|
1
|
Document Time: 0.702/F1
|
|
Narrative Containers: 0.102
|
|
SemEval
|
2016
|
THYME[3]
|
Identification of time expressions
|
10
|
0.795-0.118/F1[a]
|
|
Identification of event expressions
|
10
|
0.903-.0755/F1[a]
|
|
Identification of temporal relations
|
10
|
Document Time: 0.756-0326/F1
|
|
Narrative Containers: 0.479-0.017/F1
|
1 https://ww-healthnlp.org/ [accessed: April 1st, 2017]
2 http://share.healthnlp.org/ [accessed: April 1st, 2017]
3 https://github.com/stylerw/thymedata/ [accessed: April 1st, 2017]
a Results reported are for span extraction only.
Using a temporal database to discover the possible patterns that might point to cause
and effect events, Wang et al. [[67]] created a system to align and visualize multiple patients’ records by event, allowing
for a way to more readily find precursor, co-occurring, and after-effect events. To
extract relevant temporal and event information from clinical narratives in an EHR,
NLP and machine learning techniques have been employed. Nikfarjam et al. [[68]] proposed using an SVM classifier with a graph-based approach to find temporal relations.
Kovecevic et al. [[69]] proposed to use conditional random fields (CRF) and post-processing rules to identify
mentions of clinical events and temporal expressions. Longitudinal data has also been
explored as a resource to detect drug safety information. Schumie et al. [[70]] used Bayesian methods to detect possible drug safety signals in longitudinal observational
healthcare records. They reported that the use of temporal data enables their method
to distinguish between false drug-event associations, such as a protopathic bias,
and genuine adverse effects. To find unreported ADEs in EHRs, Zhao [[71]] proposed using time-stamped clinical events as a feature in a supervised machine
learning algorithm. Assigning weights to temporal relationships between the clinical
event and an ADE increased the predictive power of the system over no weighting at
all. Chen et al. [[72]] combined the temporal information of structured EHRs with that automatically extracted
from the clinical text through state-of-the-art NLP techniques [[73]] to identify rheumatoid arthritis patients with liver toxicity side effects as a
result of the administration of methotrexate. This innovation sets their work apart
from the computable phenotyping work done within national initiatives such as eMERGE
and i2b2 where the phenotypes are temporally non-sensitive.
3 Mining Social Media
Social media has seen a massive growth, perhaps the greatest among all health-related
information sources in HLP research in recent years [[74]], primarily driven by the rapid increase of the number of users. According to the
latest Pew report [[75]], nearly half of the adults worldwide and two-thirds of all American adults (65%)
use social media, including over 90% of 18–29-year old persons. The number continues
to rise every year as larger numbers of older people commence interacting on social
media. Earlier research from the same organization suggests that seven out of ten
adult Internet users adults search health-related information on the Internet, one
in four read about others’ health experiences, and 16% go online to find users with
similar health-related experiences [[76]]. It has been realized that because of the large user-bases that popular social
networks have, there is an abundance of health-related knowledge contained within
this domain in the form of text. Crucially, social media has opened up unique opportunities
in patient-oriented health care—by allowing the access of information directly from
users about various health-related topics and empowering the development of data-centric
NLP techniques that can take into account patients’ perspectives in unprecedented
ways [[77]]. From the perspective of health, there are two broad categories of social media
sources—generic social networks such as Facebook, Twitter, and Instagram, and domain-specific
social networks such as PatientsLikeMe[1] and DailyStrength[2]. While generic social networks (GSNs) contain information about a range of topics,
do-main-specific networks, often referred to as online health communities (OHCs),
are dedicated exclusively for discussions associated with health. Thus, GSNs typically
provide access to data from large groups of users and may provide access to patients’
perspectives that are not available from any other sources, on a wide range of topics
[[78]], and in distinct languages [[79]]. For example, GSNs such as Twitter provide unique windows for researchers to study
population-level attitudes and behaviors regarding prescription and illicit drug abuse
and misuse [[80]], data that may not be available from traditional sources such as published literature,
surveys, or EHRs. OHCs, in contrast, have much smaller user bases, but they include
users with common interests/problems/objectives and usually provide cleaner and more
targeted data (e.g., breast cancer forums).
Despite the profusion of health-related information available from social media, automatic
processing of this data has made relatively slow progress. Social media text is generally
noisy and unwieldy [[81]], given the presence of domain-specific terminologies, semantic information and
complex language usage [[82]–[84]], the challenges are exacerbated when extracting health information from social
media [[85], [86]]. Recent NLP research suggests that when used to solve specific clinical or public
health associated tasks, only small proportions of social media data selected for
the given task are useful. A significant proportion of the data is noise or unreliable,
even when collected using carefully designed queries [[87]]. Health chatter on social media is also jam-packed with colloquialisms and misspellings,
making detection and machine-level understanding of important concepts difficult.
Furthermore, particularly for GSNs, user posts often lack contextual information,
aggravating the difficulty of machine-level semantic understanding of the texts. NLP
research in this domain has thus primarily been targeted towards discovering knowledge
from the abundance of noisy, imbalanced data. Social media NLP research has focused
on a variety of techniques such as query formulation and keyword selection for the
detection of targeted content, generative techniques such as Latent Dirichlet Allocation
(LDA) and variants of topic modeling techniques to identify common themes across large
data sets, and supervised text classification techniques for filtering out noise and
irrelevant data. Innovative entity recognition and extraction techniques have been
proposed for health concept detection/identification tasks. More recent research contributions
have made progress in concept normalization, signal generation, and predictive analytics
via the application of sophisticated text mining pipelines. We review the most recent
of these advances in this section and discuss some of the data, resources, and tools
that are currently available for NLP of social media health data. We begin by discussing
some of the most commonly addressed topics that social-media-based health text mining
has addressed.
3.1 Social Media Sources, Topics, and Data Acquisition
Within the two broad categories of social networks (i.e., GSNs and OHCs), there are
notable differences in many key attributes of the individual sources. Within GSNs,
for example, Facebook is a broad coverage social networking site, Twitter is for microblogging,
and Instagram is for photo sharing [[88]]. The data sharing and privacy policies of these media influence how widely they
are used for research, with Twitter being the most popular because of its public streaming
API. A recent systematic review by Sinnenberg et al. [[74]] identified 137 peer-reviewed research articles, which utilized Twitter for health-related
research, with 57% of them focusing on contents while the rest focused on recruitment
or interventions. Over 80% of the identified publications were either focused on lexical
content analysis or surveillance, and 108 of the articles that focused on the contents,
represented 5.1 billion tweets—a monumental amount of information. Population/public
health topics are most commonly addressed within the Twitter articles identified and
the same trend can be seen for Facebook and Instagram, although one key difference
we observed is that a significant portion of public health research using Facebook
focused on communication rather than on lexical content processing [[89]–[91]]. For monitoring and surveillance research from social media, the most common topic
has been influenza surveillance [[92], [93]]. Data collection strategies for this task and other similar tasks (e.g., identifying
health threats [[94]], drug interactions [[95]], smoking patterns [[96], [97]], pharmacovigilance [[98], [99]]) have been typically employing simplistic NLP techniques, relying on keywords and/or
hashtags as queries or on the direct selection of users via network links (e.g., followers
of a brand of e-cigarettes). While for some research tasks such querying techniques
suffice, other recent studies, particularly in the field of medication-effect analysis
where the recall is generally low, have devised techniques to address the social media
specific challenge of misspellings [[100]]. Pimpalkhute et al. [[101]] proposed a phonetic spelling variant generator that automatically generates common
misspellings given a term. While the system has been used for collecting medication-related
chatter from Twitter [[102]] and personal health messages [[103]], it may be applied to a variety of other data collection tasks. Data collection
from OHCs has not faced similar challenges, as posts are usually categorized/structured,
and the strategies employed have been simpler. However, because of the privacy policies
of such sources, publicly available NLP data sets are scarce.
3.2 Content Analysis and Text Classification
Over half of the studies involving social media data that are cited in this paper
employ lexical content analysis. A large subset of these studies has merely relied
on collecting data in bulk using appropriate queries, and then deriving conclusions
using simple statistical models directly based on the volume of data [[104]]. Sometimes, the information is coupled with available metadata (e.g., geolocation
for flu surveillance) [[105],[106]].
In some cases, studies involving big data from social media have overcome the challenges
faced by studies using big data from other sources (e.g., Google Flu Trends, its shortcomings,
and the use of Twitter for flu surveillance [[107]]).
Early health-related NLP research utilizing social media data suggests that rule-based
approaches perform particularly poorly in this domain because of the nature of the
data, resulting in a shift towards machine-learning-based approaches in recent years
[[100]]. Text classification techniques have been applied to extract high quality information
from noisy, health-related social media data [[108]] or for other downstream tasks in both GSNs (e.g., [[109]]) and OHCs (e.g., [[110]]). These downstream tasks include, but are by no means limited to, sentiment analysis
[[111]–[113]], adverse drug reaction detection [[114], [115]], antibiotic discussion characterization [[116]], prescription medication abuse detection [[117]], substance use (e.g., tobacco products) classification [[96], [118]], personal event detection [[119]–[121]], and user behavior analysis [[122]]. Sentiment analysis/classification is a well explored NLP task, and, because of
its particular suitability to large social media data and the availability of annotated
resources (e.g., data from the SemEval task [[123]]), it has found applications in an assortment of inter-domain tasks. For example,
recently, sentiment analysis/classification techniques have been applied to understanding
user sentiments associated with drugs [[124], [125]], including non-medical use of drugs [[126]] and vaccinations [[127]], treatments [[111], [128]], and diseases [[129]].
In terms of methods for content analysis and filtering, supervised learning techniques
that incorporate informative features have been the most popular, although many studies
still rely on a manual analysis for deriving final conclusions [[130]]. Many early studies discovered that the high amount of noise present in social
media data posed an important problem for mining knowledge, and they attempted to
address the issue by designing supervised classification techniques [[131]]. Traditional text classification features such as bag-of-words and n-grams are
most commonly used and social media specific features such as emoticons have also
been found to be informative for particular tasks [[132]]. Sarker and Gonzalez showed that social media text classification, particularly
for short text nuggets such as Twitter posts, benefits from the generation of large
numbers of semantic features representing information about the texts from multiple
aspects such as topics, polarities, sentiments, and others. The short nature of Twitter
posts along with the presence of frequent misspellings commonly result in extremely
sparse vectors, and thus generating many features to represent diverse properties
of the text enables classifiers to learn better fitting models. Another interesting
aspect of social-media-based health text classification is that medical domain-specific
tools such as MetaMap [[133]], which have been used in the past for generating features for text classification,
are not very useful when it comes to social media text. This is primarily because
such tools are designed for formal medical text only, and are incapable of understanding
social media expressions. Unsurprisingly, SVMs have obtained the best performance
for a long time and are still very difficult to beat in terms of performance [[108], [114], [117], [134]–[136]]. Very recently, deep neural-network-based models have been designed and employed
for social media health text classification, with very promising results [[137]].
Another effective and popular social media lexical content analysis approach has been
topic modeling. Unlike text classification algorithms, which require supervision/
annotated data, topic models utilize large volumes of unlabeled data to identify topics
that represent that data. Therefore, topic modeling techniques can be readily employed
to identify and analyze the contents of targeted social media health chatter. Paul
and Dredze [[138]], for example, proposed the Ailment Topic Aspect Model (ATAM) to identify health-related
topics from Twitter. Using this technique, the authors discovered some of the most
popular health-related topics of Twitter conversations, such as influenza, allergies,
and obesity. The approach has been replicated, modified, and other topic modeling
techniques have been employed to find health-related topics in languages other than
English (e.g., Chinese [[139]]), and for focused tasks such as analyzing suicide-related content on social media
[[140]] and characterizing discussions about vaccines [[141]]. Li and colleagues [[142], [143]] proposed topic models for identifying clusters of ‘life events’, and using them
for building user timelines and other downstream applications. Other variants of topic
models have also been applied for deriving task-specific knowledge from large, unlabeled
social media data sets—such as the use of keyword-biased topic models for predicting
the safety of dietary supplements [[144]] and time-sensitive probabilistic topic models for capturing changing user interests
and topics over time from health-related chat logs [[145]]. Recent advances in deep neural-network-based models have seen the application
of such models for the generation of topical content, which may then be applied to
downstream tasks [[146]].
3.3 Information Extraction and Normalization
It was obvious early on in information extraction research from social media health
text that traditional approaches performed poorly in this domain. Social media and
health are both complex lexical domains and the intersection of the two particularly
aggravates challenges associated with text mining. Rare and non-standard contents
are specifically difficult for systems to extract and aggregate [[78],[79]] particularly for pharmacovigilance, via the use of NLP. Because of the relatively
new research focus on social media mining for health-related tasks, challenges associated
with the extraction of pertinent, task-specific content have only been discovered/realized
in the recent past. In line with early biomedical NLP approaches, information extraction
approaches specific to the social media domain mostly employed lexicon-based techniques
to solve many problems, such as detecting adverse drug reactions [[147]], identifying users making pregnancy announcements [[148]], and mining opinions [[149]]. However, with the evolving nature of language usage on social media and the unconstrained
number of ways in which the same information can be expressed in this domain, developing
thorough lexicons is a difficult task. Additionally, even the most thorough lexicons
may get outdated within a relatively short period of time. Recent efforts have attempted
to combine multiple task-specific lexicons for improving information detection/extraction
performance on target datasets, but these lexicon-based approaches are easily outperformed
by learning-based approaches in the presence of expert annotated data [[99]]. Denecke’s qualitative analysis [[150]] on concept extraction from social media details the specific problems associated
with the use of standardized lexicons in this domain. The author explains that knowledge-bases/
lexicons such as MetaMap and cTakes fail to identify common language or consumer health
vocabulary. In particular, verbs, personal pronouns, adjectives, and connecting words
present problematic contents.
With emerging efforts in the preparation of annotated social media based datasets
for a variety of health-related and public health monitoring tasks, recent benchmark
systems have employed supervised learning for concept extraction/sequence labeling.
Conditional random fields (CRFs) currently produce the best performance on annotated
data for the task in English and other languages [[99], [112], [151], [152]]. The primary reason behind the success is the ability of CRFs to incorporate contextual
information when determining whether a given token should be classified as relevant
or not. For example, concerning disease, disorders, or adverse drug reaction mentions
in social media, while users may use a variety of creative terms to express their
minds, similar concepts are likely to occur in similar contexts. In addition to context
terms, such context-incorporating learning-based algorithms have shown improvements
when generalized representations of the context tokens are provided. Recent advances
in learning semantic representations from large unlabeled datasets [[153]] have aided social media mining research by allowing systems to identify semantically
similar terms. Nikfarjam et al. [[99]], for example, learnt distributed representations of social media terms, as used
within a domain, clustered the vectors, and used cluster numbers as features. The
clusters contain the terms that are found to be close to each other in the semantic
space, and so, terms that are used in the same contexts are clustered together. Such
features are particularly attractive because they don’t require human annotations
or the manual creation of resources, but can be learned automatically in the presence
of large amounts of unlabeled data—of which there is abundance in social media. Therefore,
such approaches are easily portable to other tasks or even to other languages. Morlane-Hondère
et al. [[152]], for example, used the same features for French social media data.
One of the least explored topics in social media health language processing is perhaps
normalization. As discussed in the previous sections, normalization is the task of
grouping together lexical items that essentially represent the same concepts. Within
the domain of medical NLP, tools based on lexicons and knowledge bases such as MetaMap
[[133]] have been used for identifying and grouping distinct lexical representations of
the same concepts. Learning-based approaches for normalization have been proposed
and employed for tasks within the medical domain [[21], [22], [23], [25], [27], [147], [148], [149]], but research on this topic within the domain of social media is still in its infancy.
While medical text is itself complex, social media text presents additional challenges
due to non-standard expressions and spellings. Typos, ad hoc abbreviations, phonetic
substitutions, use of colloquial language, ungrammatical structures, and even the
use of emoticons make social media text significantly different from texts from other
sources [[154]]. While these properties present NLP challenges, these also constitute the primary
motivation for building normalization systems.
Some research on normalization of social media text focused at the lexical level,
and has similarities to spell checking techniques with the primary difference that
out-of-vocabulary terms in social media text are often intentionally generated. Text messages have
been used as input data for normalization models, and various error models have been
proposed, such as Hidden Markov Models [[155]] and noisy channel models [[156]]. Similar lexical normalization techniques have been evaluated on social media texts
as well [[157], [158]]. For concept normalization of social media texts, approaches still mostly rely
on custom-built lexicons [[159], [160]] and lexical matching. Metke-Jimenez and Karimi [[159]] presented a large, compiled lexicon of adverse drug reaction terms and employed
term weighting techniques for retrieving relevant concepts. Very recently, with the
development of new, efficient techniques for learning vector representations of terms/phrases,
new mechanisms for performing normalization using vector representations of words
have been proposed. In a typical normalization pipeline, word vector representations
are generated from large unlabeled data sets and then similarity measurements, such
as cosine similarity, are utilized to identify semantically similar concepts [[103], [161]]. In terms of methods, techniques adapted from the field of machine translation
[[161]] and neural-network-based approaches [[103], [162]] are currently being explored. Although progress in this field is very limited,
the importance of such techniques has been realized and there is currently a drive
from the research community to release more data sets for evaluating social-media-based
health text normalization systems [[87], [159]]. Considering the necessity of high-performance normalization systems for health
text mining tasks from social media, the severe lack of research on this topic, and
the recent advances in semantic representations of text nuggets, NLP research in the
near future should, and inevitably will, focus on the development of more advanced
concept normalization techniques.
3.4 Individual and Temporal Data in Social Media
Very recently, the detection of personal health-related life events and their extraction
have received some attention as researchers are starting to focus on using social
media data for precision medicine. Simple bag-of-words and n-gram models have been
combined with temporal information from individuals’ social media timelines to perform
personal life event detection [[119]]. Unsupervised LDA models and human annotations have also been used to detect personal
events [[142]]. Li and Cardie [[143]] proposed using an unsupervised Dirichlet Process Mixture Model to capture temporal
and personal event mentions to create a chronological timeline from a user’s Twitter
feed. Kiciman and Richardson [[120]] combined several supervised learning methods to classify and extract personal event
information mentions in Twitter. Combining this information with temporal information,
they propose to create a system for creating action—outcome relationships from a user’s
timeline. Wen et al. [[121]] designed a temporal tagger to extract temporal expressions from sentences containing
predefined event words from a patient’s profile on an online cancer support community.
A trained classifier was then used to normalize dates and sort mentions of the event
in a chronological timeline. Chandrashekar et al. [[163]] attempted to combine query formulation, supervised classification, and rule-based
information extraction techniques to identify and study pregnancy timelines. These
studies have suggested that individuals belonging to specific cohorts can be detected
from social media, and specific health-related events over time can be mined from
the information posted by individual users. Future research will inevitably build
on these studies and utilize social media data for personalized tasks.
3.5 Shared Tasks
There has been a move in recent times to design shared tasks and release data for
social media health NLP tasks. Although, compared to the general health NLP domain,
such efforts in the social media domain are very limited and relatively new. They
present some of the first opportunities to compare distinct approaches and systems
on social media tasks. [Table 3] presents four such tasks that we decided to include in this review. A ffth task,
on normalization of social media concepts, was proposed in 2016, but did not receive
any participating systems [[87]]. Three of the tasks were on text classification and one on information extraction.
Class-specific F-scores were typically used as the evaluation metrics, except in one
task (Depression and Post-traumatic Stress Disorder (PTSD) detection). Unsurprisingly,
the performances of the best performing systems on social media texts are generally
lower than the performances of similar systems in generic health-related texts. Despite
this, with the growth of public release of annotated and targeted, unlabeled data
sets, the development of better performing and more applicable systems is inevitable.
Table 3
Performance of NLP systems on common data sets: best performances and types of tasks
are shown.
|
Task
|
Task type
|
Score type
|
Performance upper limit/ range
|
|
Triaging mental health posts [[164]]
|
Classification
|
F-score (macro-averaged)
|
Shared task best: 0.42 [[165]]
|
|
Depression and PTSD detection [[166]]
|
Classification
|
AUC
|
Shared task best: 0.84 [[167]]
|
|
Adverse drug reaction mention detection [[87]]
|
Classification
|
F-score
|
Shared task best: 0.42 [[168]] Upper limits:
0.54 (Twitter)
0.68 (DailyStrength)
|
|
Adverse drug reaction extraction [[87]]
|
Information Extraction
|
F-Score
|
Shared task best: 0.61 [[169]] Upper limits:
0.72 (Twitter)
0.82 (DailyStrength)
|
4 Conclusion
In this survey, we reviewed recent main developments in the field of health text processing.
The survey focused on fundamental NLP tasks as well as higher-level applications related
to extracting the patient’s perspective from clinical records and social media.
One of the main challenges in the field is the availability of data that can be shared
and which can be used by the community to push the development of methods based on
comparable and reproducible studies. Shared data and open-source state-of-the-art
methods go hand in hand in the quest of reproducible scientific discoveries.
From the applications point of view, NLP approaches that use EMR data would benefit
from the direct use by clinical investigators for biomedical discoveries such as very
large scale PheWAS/GWAS studies based on patient cohorts collected through automatic
computable phenotypes. From a methods point of view, the field is now well aligned
with the developments in the general NLP area (which was not the case a decade ago).
State-of-the-art technologies from machine learning have infiltrated the domain. The
next frontier tasks are the ones that require higher-level discourse processing such
as co-reference and temporal relation extraction, as well as normalization.
With respect to the use of social media, despite the great interest it has elicited
in the research community, when used to solve specific clinical or public health associated
tasks, only small proportions of the data selected for the given task are actually
useful. A significant proportion of the data is noise or unreliable, even when collected
using carefully designed queries [[87]]. In order to obtain enough data, information retrieval methods with adaptable queries
are needed, along with greater flexibility in concept extraction, and reliable normalization.
While other state-of-the-art information extraction/identification approaches have
been proposed in the machine learning and NLP literature, the scarcity of annotated
social media data is still a major obstacle in assessing the true value of social
media for the various health-related tasks. Novel, generic extraction algorithms for
various health-related tasks have been proposed in the recent past [[170]], but their performance in real-life social media data have not been evaluated.
This obstacle has been discussed in recent reviews [[100]] and there has been a greater urgency for creating and releasing annotated datasets
and targeted unlabeled data sets in distinct languages [[102], [159], [171]].
Not until all of the fundamental tasks are readily available will knowledge discovery
applications that use social media as a source flourish and be reliable enough to
be used by regulatory agencies.
