Appendix: Content Summaries of Best Papers for the Health Information Management Section
of the 2019 IMIA Yearbook
Atutxa A, Pérez A, Casillas A
Machine Learning Approaches on diagnostic term encoding with the ICD for clinical
documentation
IEEE J Biomed Health Inform 2018;22(4):1323-9
This study focuses on data mining applied to unstructured clinical text in electronic
health records (EHRs). The authors tried to improve standard machine learning techniques.
They believe that clinical text mining can efficiently leverage the encoding process
and they sought to develop computer-assisted classification tools and applications
to help coding experts. The paper focuses on developing automatic techniques to encode
diagnostic terms (DTs), focusing on Spanish language EHRs and publicly available resources
for Spanish clinical text processing and mining.
In the study, records were encoded manually by experts using the International Classification
of Diseases (ICD). The dataset consisted of spontaneous DTs extracted from over 9,000
EHRS, with 1,500 possible ICD codes. The proposed system was able to select the correct
ICD code with 92% precision for the main disease (primary class) and 88% precision
for the main disease together with the non-essential modifiers (fully specified class).
The authors note that the methodology is simple and portable with potential applicability
for documentation and pharmaco-surveillance. In a pilot study using a small sample
of records, experts from public hospitals reported an accuracy of 91.2%. The authors
have made the software publicly available so that the techniques and approach can
be used by a broader audience of both clinicians and researchers.
Cui L, Xie X, Shen Z
Prediction task guided representation learning of medical codes in EHR
J Biomed Inform 2018;84:1-10
The authors review applications using machine learning models for predictive analytics
in electronic health records (EHRs). Machine learning has been used to improve the
quality and efficiency of services. Developing machine learning models requires converting
medical codes representing diagnoses and procedures to feature vectors. The authors
recognize the importance of vector representations on the performance of machine learning
models. They sought to address shortcomings of previous efforts using representation
learning methods from Natural Language Processing (NLP) to learn vector representations
of medical codes. As stated by the authors, “the objective of the study was to develop
a representation learning model which can learn vector representations of medical
codes that have strong predictive capability for various prediction tasks and required
relatively small amounts of training data”. The researchers used a dataset that contained
750,000 cases and represented three years of records from five hospitals in China.
Researchers developed a new method that they called “Prediction Task Guided Health
Record Aggregation (PTGHRA)” which aggregates health records guided by prediction
tasks, to construct a training corpus for various representation learning models.
PTGHRA uses representation learning methods to map medical codes to continuous vectors
and combined medical code vectors with other information in health record to form
feature vectors for prediction tasks. Authors focused on the prediction of cost and
length of stay (LoS). Compared with unsupervised approaches, representation learning
models integrated with PTGHRA yielded a significant improvement in predictive capability
of generated medical code vectors. For training set sizes smaller than 20,000 records,
PTGHRA achieved up to 32% accuracy improvement.
Li F, Liu W, Yu H
Extraction of information related to adverse drug events from electronic health record
notes: design of an end-to-end model based on deep learning
JMIR Med Inform 2018;6(4):e12159
This paper focuses on how pharmacovigilance and drug-safety surveillance are crucial
for monitoring adverse drug events (ADEs) and notes challenges (such as under-reporting)
with several existing ADE-reporting systems. The main purpose of the study was to
develop a deep learning model focusing on identifying ADEs, medications, and indications.
A secondary purpose was to improve the deep learning model. The authors used the Medication,
Indication, and Adverse Drug Events (MADE) 1.0 challenge to develop both training
and testing datasets. MADE contains data (1,089 EHR notes) from cancer patients and
includes nine entity types including Medication, Indication, and ADE. It also includes
seven types of relations between these entities. The training data included 876 and
the testing dataset used for the remaining 213 notes. To extract information from
the dataset, the authors used a deep-learning model applying bidirectional long short-term
memory (BiLSTM) conditional random field network to recognize entities and a BiLSTM-Attention
network to extract relations. They enhanced their deep learning model with three multitask
learning (MTL) methods (hard parameter sharing, parameter regularization, and task
relation learning). The authors used the results of the second step of the process
of extracting ADE information (relation extraction) to compare all models. They used
micro-averaged precision, recall, and F1 as evaluation metrics. The authors compared
their model with the top three systems in the MADE 1.0 challenge. Their model achieved
state-of-the-art results (F1=65.9%). The model using hard parameter sharing further
improved the F1 by 0.8%, boosting the F1 to 66.7%. The authors concluded that the
performance of ADE-related information extraction can be improved by employing deep
learning and MTL models. They also felt that the methods, data, and other factors
influence the effectiveness of MTL models. The authors believe that this study could
be useful for further natural language processing and machine learning research for
detection of adverse drug events in clinical notes. Their annotated Dataset (Medication,
Indication, and Adverse Drug Events (MADE)) – will be publicly available to support
research on extraction of ADE-related information.
Qiu JX, Yoon H-J, Fearn PA, Tourassi GD
Deep learning for automated extraction of primary sites from cancer pathology reports
IEEE J Biomed Health Inform 2018;22(1):244-51
The paper focuses on using data extracted from pathology reports to populate cancer
registries. The authors explore using a machine learning approach to make the labor-intensive
manual process of information extraction and coding easier. In this study, researchers
investigated deep learning and a convolutional neural network (CNN) for extracting
ICD-O-3 (International Classification of Diseases for Oncology) topographic codes
from breast and lung cancer pathology reports. They compared a CNN with a term frequency
vector approach, using 942 de-identified pathology reports matched to 12 ICD-O-3 topography
codes corresponding to seven breast and five lung primary sites. They compared the
results to reviews by human (cancer registry) subject matter experts. Pathology reports
were provided from five different Surveillance, Epidemiology and End Results (SEER)
cancer registries. Cancer registry experts manually annotated the pathology reports
and their annotations served as the gold standard. Researchers observed that when
class labels were well populated, deep learning models (e.g., CNN) outperformed the
conventional approaches (term frequency vector approaches) in the studies looking
at class prevalence (micro-and macro-F score increases of up to 0.132 and 0.226).
The increase in performance of deep learning methods from transfer learning was less
strong and depended on the CNN method and cancer site. The authors believe that their
results demonstrate the potential of deep learning for automated abstraction of relevant
information for cancer registries from pathology reports.
