Progress in Biomedical Knowledge Discovery: A 25-year Retrospective

Summary

Objectives: We sought to explore, via a systematic review of the literature, the state of the art of knowledge discovery in biomedical databases as it existed in 1992, and then now, 25 years later, mainly focused on supervised learning.

Methods: We performed a rigorous systematic search of PubMed and latent Dirichlet allocation to identify themes in the literature and trends in the science of knowledge discovery in and between time periods and compare these trends. We restricted the result set using a bracket of five years previous, such that the 1992 result set was restricted to articles published between 1987 and 1992, and the 2015 set between 2011 and 2015. This was to reflect the current literature available at the time to researchers and others at the target dates of 1992 and 2015. The search term was framed as: Knowledge Discovery OR Data Mining OR Pattern Discovery OR Pattern Recognition, Automated.

Results: A total 538 and 18,172 documents were retrieved for 1992 and 2015, respectively. The number and type of data sources increased dramatically over the observation period, primarily due to the advent of electronic clinical systems. The period 1992-2015 saw the emergence of new areas of research in knowledge discovery, and the refinement and application of machine learning approaches that were nascent or unknown in 1992.

Conclusions: Over the 25 years of the observation period, we identified numerous developments that impacted the science of knowledge discovery, including the availability of new forms of data, new machine learning algorithms, and new application domains.

Through a bibliometric analysis we examine the striking changes in the availability of highly heterogeneous data resources, the evolution of new algorithmic approaches to knowledge discovery, and we consider from legal, social, and political perspectives possible explanations of the growth of the field. Finally, we reflect on the achievements of the past 25 years to consider what the next 25 years will bring with regard to the availability of even more complex data and to the methods that could be, and are being now developed for the discovery of new knowledge in biomedical data.

Keywords

Algorithms - artificial intelligence - databases - factual - data mining - knowledge discovery in databases

 

• References

• 1 Ackoff RL. From Data to Wisdom. J Appl Syst Anal 1989; 16: 3-9.
• 2 Analytics, Business Intelligence and Data Management | SAS [Internet].. [cited 2016 Jan 25]. Available from: http://www.sas.com/en_us/home.html
• 3 IBM SPSS software [Internet].. [cited 2016 Jan 25]. Available from: http://www-01.ibm.com/software/analytics/spss/
• 4 Surveillance, Epidemiology, and End Results Program [Internet].. [cited 2016 Jan 25]. Available from: http://seer.cancer.gov
• 5 NHANES - National Health and Nutrition Examination Survey Homepage [Internet].. [cited 2016 Jan 25]. Available from: www.cdc.gov/nchs/nhanes.htm
• 6 CDC - BRFSS [Internet].. [cited 2016 Jan 25]. Available from: http://www.cdc.gov/brfss
• 7 Fair ME, Lalonde P, Newcombe HB. Application of exact ODDS for partial agreements of names in record linkage. Comput Biomed Res 1991; Feb 24 (Suppl. 01) 58-71.
• 8 Shannon HS, Jamieson E, Walsh C, Julian JA, Fair ME, Buffet A. Comparison of individual follow-up and computerized record linkage using the Canadian Mortality Data Base. J Public Health 1989; Feb 80 (Suppl. 01) 54-7.
• 9 Shapiro S. Automated record linkage: a response to the commentary and letters to the editor. Clin Pharmacol Ther 1989; Oct 46 (Suppl. 04) 395-8.
• 10 SEER-Medicare Linked Database [Internet].. [cited 2016 Jul 19]. Available from: http://healthcaredelivery.cancer.gov/seermedicare
• 11 Blumenthal D, Tavenner M. The “Meaningful Use” Regulation for Electronic Health Records. N Engl J Med 2010 Aug 5 363 (Suppl. 06) 501-4.
• 12 Ohno-Machado L. Mining electronic health record data: finding the gold nuggets. J Am Med Inform Assoc 2015; Sep 22 (Suppl. 05) 937.
• 13 De Moor G, Sundgren M, Kalra D, Schmidt A, Dugas M, Claerhout B. et al. Using electronic health records for clinical research: the case of the EHR4CR project. J Biomed Inform 2015; Feb 53: 162-73.
• 14 Ross MK, Wei W, Ohno-Machado L. “Big Data” and the Electronic Health Record. Yearb Med Inform 2014 Aug 15 9 (Suppl. 01) 97-104.
• 15 Hripcsak G, Albers DJ. Next-generation phenotyping of electronic health records. J Am Med Inform Assoc 2013; 20 (Suppl. 01) 117-21.
• 16 Soulakis ND, Carson MB, Lee YJ, Schneider DH, Skeehan CT, Scholtens DM. Visualizing collaborative electronic health record usage for hospitalized patients with heart failure. J Am Med Inform Assoc 2015 Mar 1 22 (Suppl. 02) 299-311.
• 17 Warner JL, Denny JC, Kreda DA, Alterovitz G. Seeing the forest through the trees: uncovering phenomic complexity through interactive network visualization. J Am Med Inform Assoc 2015 Mar 1 22 (Suppl. 02) 324-9.
• 18 Perer A, Wang F, Hu J. Mining and exploring care pathways from electronic medical records with visual analytics. J Biomed Inform 2015; Aug 56: 369-78.
• 19 Viangteeravat T, Nagisetty NSVR. Giving Raw Data a Chance to Talk: A Demonstration of Exploratory Visual Analytics with a Pediatric Research Database Using Microsoft Live Labs Pivot to Promote Cohort Discovery, Research, and Quality Assessment. Perspect Health Inf Manag [Internet] 2014 Jan 1 [cited 2015 Nov 15];11(Winter). Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3995483/
• 20 Hripcsak G, Albers DJ, Perotte A. Parameterizing time in electronic health record studies. J Am Med Inform Assoc 2015 Jul 1 22 (Suppl. 04) 794-804.
• 21 Hajihashemi Z, Popescu M. A Multidimensional Time Series Similarity Measure with Applications to Eldercare Monitoring. IEEE J Biomed Health Inform 2015; PP (Suppl. 99) 1-1.
• 22 Pivovarov R, Albers DJ, Sepulveda JL, Elhadad N. Identifying and Mitigating Biases in EHR Laboratory Tests. J Biomed Inform 2014; Oct 0: 24-34.
• 23 Gotz D, Wang F, Perer A. A methodology for interactive mining and visual analysis of clinical event patterns using electronic health record data. J Biomed Inform 2014 Apr 1 48: 148-59.
• 24 Hripcsak G, Albers DJ. Correlating electronic health record concepts with healthcare process events. J Am Med Inform Assoc 2013; Dec 20 e2 e311-8.
• 25 Warner JL, Zollanvari A, Ding Q, Zhang P, Snyder GM, Alterovitz G. Temporal phenome analysis of a large electronic health record cohort enables identification of hospital-acquired complications. J Am Med Inform Assoc 2013; Dec 20 e2 e281-7.
• 26 Sengupta D, Naik PK. SN algorithm: analysis of temporal clinical data for mining periodic patterns and impending augury. J Clin Bioinform 2013 Nov 28 3: 24.
• 27 Batal I, Valizadegan H, Cooper GF, Hauskrecht M. A Temporal Pattern Mining Approach for Classifying Electronic Health Record Data. ACM Trans Intell Syst Technol [Internet] 2013 Sep [cited 2015 Nov 15];4(4). Available from: www.ncbi.nlm.nih.gov/pmc/articles/PMC4192602
• 28 Hanauer DA, Ramakrishnan N. Modeling temporal relationships in large scale clinical associations. J Am Med Inform Assoc 2013; 20 (Suppl. 02) 332-41.
• 29 Wang F, Lee N, Hu J, Sun J, Ebadollahi S, Laine AF. A framework for mining signatures from event sequences and its applications in healthcare data. IEEE Trans Pattern Anal Mach Intell 2013; Feb 35 (Suppl. 02) 272-85.
• 30 Hripcsak G, Albers DJ, Perotte A. Exploiting time in electronic health record correlations. J Am Med Inform Assoc 2011; Dec 18 (Suppl. 01) i109-15.
• 31 Chen Y, Lorenzi N, Nyemba S, Schildcrout JS, Malin B. We Work with Them? Healthcare Workers Interpretation of Organizational Relations Mined from Electronic Health Records. Int J Med Inform 2014; Jul 83 (Suppl. 07) 495-506.
• 32 Murphy DR, Laxmisan A, Reis BA, Thomas EJ, Esquivel A, Forjuoh SN. et al. Electronic health record-based triggers to detect potential delays in cancer diagnosis. BMJ Qual Saf 2014; Jan 23 (Suppl. 01) 8-16.
• 33 Perimal-Lewis L, Teubner D, Hakendorf P, Hor-wood C. Application of process mining to assess the data quality of routinely collected time-based performance data sourced from electronic health records by validating process conformance. Health Informatics J 2015 Oct 11;
• 34 Ben-Assuli O, Shabtai I, Leshno M. Using electronic health record systems to optimize admission decisions: the Creatinine case study. Health Informatics J 2015; Mar 21 (Suppl. 01) 73-88.
• 35 Sung S-F, Hsieh C-Y, Kao Yang Y-H, Lin H-J, Chen C-H, Chen Y-W. et al. Developing a stroke severity index based on administrative data was feasible using data mining techniques. J Clin Epidemiol 2015; Nov 68 (Suppl. 11) 1292-300.
• 36 DeFraia GS. Psychological Trauma in the Work-place: Variation of Incident Severity among Industry Settings and between Recurring vs Isolated Incidents. Int J Occup Environ Med 2015 Jul 1 6 (Suppl. 03) 155-68.
• 37 Zhang-Salomons J, Salomons G. Determine the therapeutic role of radiotherapy in administrative data: a data mining approach. BMC Med Res Methodol 2015 Feb 3 15 (Suppl. 01) 11.
• 38 Hassani S, Lindman AS, Kristoffersen DT, Tomic O, Helgeland J. 30-Day Survival Probabilities as a Quality Indicator for Norwegian Hospitals: Data Management and Analysis. PLoS One 2015 Sep 9 10 (Suppl. 09) e0136547.
• 39 He D, Mathews SC, Kalloo AN, Hutfless S. Mining high-dimensional administrative claims data to predict early hospital readmissions. J Am Med Inform Assoc 2014 Mar 1 21 (Suppl. 02) 272-9.
• 40 Guiney H, Felicia P, Whelton H, Woods N. Analysis of a Payments Database Reveals Trends in Dental Treatment Provision. J Dent Res 2013 Jul 1 92 (Suppl. 07) S63-9.
• 41 Unnikrishnan KP, Patnaik D, Iwashyna TJ. Spatio-temporal Structure of US Critical Care Transfer Network. AMIA Summits Transl Sci Proc 2011 Mar 7 2011: 74-8.
• 42 Concaro S, Sacchi L, Cerra C, Fratino P, Bellazzi R. Mining Health Care Administrative Data with Temporal Association Rules on Hybrid Events. Methods Inf Med 2011; 50 (Suppl. 02) 166-79.
• 43 Hussey K, Siddiqui T, Burton P, Welch GH, Stuart WP. Understanding Administrative Abdominal Aortic Aneurysm Mortality Data. Eur J Vasc Endovasc Surg 2015; Mar 49 (Suppl. 03) 277-82.
• 44 Thigpen JL, Dillon C, Forster KB, Henault L, Quinn EK, Tripodis Y. et al. Validity of International Classification of Disease Codes to Identify Ischemic Stroke and Intracranial Hemorrhage Among Individuals With Associated Diagnosis of Atrial Fibrillation. Circ Cardiovasc Qual Outcomes 2015; Jan 8 (Suppl. 01) 8-14.
• 45 Benchimol EI, Guttmann A, Mack DR, Nguyen GC, Marshall JK, Gregor JC. et al. Validation of international algorithms to identify adults with inflammatory bowel disease in health administrative data from Ontario, Canada. J Clin Epidemiol 2014; Aug 67 (Suppl. 08) 887-96.
• 46 Grams ME, Waikar SS, MacMahon B, Whelton S, Ballew SH, Coresh J. Performance and Limitations of Administrative Data in the Identification of AKI. Clin J Am Soc Nephrol 2014 Apr 7 9 (Suppl. 04) 682-9.
• 47 Widdifield J, Bernatsky S, Paterson JM, Tu K, Ng R, Thorne JC. et al. Accuracy of Canadian Health Administrative Databases in Identifying Patients With Rheumatoid Arthritis: A Validation Study Using the Medical Records of Rheumatologists. Arthritis Care Res 2013 Oct 1 65 (Suppl. 10) 1582-91.
• 48 Mähönen M, Jula A, Harald K, Antikainen R, Tuomilehto J, Zeller T. et al. The validity of heart failure diagnoses obtained from administrative registers. Eur J Prev Cardiol 2013 Apr 1 20 (Suppl. 02) 254-9.
• 49 Marrie RA, Yu BN, Leung S, Elliott L, Caetano P, Warren S. et al. Rising prevalence of vascular comorbidities in multiple sclerosis: validation of administrative definitions for diabetes, hypertension, and hyperlipidemia. Mult Scler J 2012 Sep 1 18 (Suppl. 09) 1310-9.
• 50 Molodecky NA, Myers RP, Barkema HW, Quan H, Kaplan GG. Validity of administrative data for the diagnosis of primary sclerosing cholangitis: a population-based study. Liver Int 2011 May 1 31 (Suppl. 05) 712-20.
• 51 Schultz SE, Rothwell DM, Chen Z, Tu K. Identifying cases of congestive heart failure from administrative data: a validation study using primary care patient records. Chronic Dis Inj Can 2013; Jun 33 (Suppl. 03) 160-6.
• 52 Gregori D, Petrinco M, Bo S, Rosato R, Pagano E, Berchialla P. et al. Using Data Mining Techniques in Monitoring Diabetes Care. The Simpler the Better? J Med Syst 2009 Sep 10 35 (Suppl. 02) 277-81.
• 53 Wu T-SJ, Shih F-YF, Yen M-Y, Wu J-SJ, Lu S-W, Chang KC-M. et al. Establishing a nationwide emergency department-based syndromic surveil-lance system for better public health responses in Taiwan. BMC Public Health 2008
• 54 Mikosz CA, Silva J, Black S, Gibbs G, Cardenas I. Comparison of two major emergency department-based free-text chief-complaint coding systems. MMWR Suppl 2004 Sep 24 53: 101-5.
• 55 Gesteland PH, Gardner RM, Tsui F-C, Espino JU, Rolfs RT, James BC. et al. Automated syndromic surveillance for the 2002 Winter Olympics. J Am Med Inform Assoc 2003; Dec 10 (Suppl. 06) 547-54.
• 56 Silva JC, Shah SC, Rumoro DP, Bayram JD, Hal-lock MM, Gibbs GS. et al. Comparing the accuracy of syndrome surveillance systems in detecting influenza-like illness: GUARDIAN vs. RODS vs. electronic medical record reports. Artif Intell Med 2013; Nov 59 (Suppl. 03) 169-74.
• 57 Eyre DW, Walker AS. Clostridium difficile surveillance: harnessing new technologies to control transmission. [Review]. Expert Rev Antiinfective Ther 2013; Nov 11 (Suppl. 11) 1193-205.
• 58 Brownstein JS, Mandl KD. Reengineering real time outbreak detection systems for influenza epidemic monitoring. AMIA Annu Symp Proc 2006; 866.
• 59 Bourgeois FT, Olson KL, Brownstein JS, McAdam AJ, Mandl KD. Validation of syndromic surveil-lance for respiratory infections. Ann Emerg Med 2006 Mar;47(3).
• 60 Andreu Perez J, Leff D, Ip H, Yang G-Z. From Wearable Sensors to Smart Implants - Towards Pervasive and Personalised Healthcare. IEEE Trans Biomed Eng 2015 Apr 13
• 61 Rehman MH, ur Liew CS, Wah TY, Shuja J, Daghighi B. Mining Personal Data Using Smart-phones and Wearable Devices: A Survey. Sensors 2015 Feb 13 15 (Suppl. 02) 4430-69.
• 62 Kumar S, Abowd GD, Abraham WT, al’Absi M, Gayle Beck J, Chau DH. et al. Center of excellence for mobile sensor data-to-knowledge (MD2K). J Am Med Inform Assoc 2015; Nov 22 (Suppl. 06) 1137-42.
• 63 Zhang Z, Pi Z, Liu B. TROIKA: a general framework for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise. IEEE Trans Biomed Eng 2015; Feb 62 (Suppl. 02) 522-31.
• 64 Deepu CJ, Lian Y. A joint QRS detection and data compression scheme for wearable sensors. IEEE Trans Biomed Eng 2015; Jan 62 (Suppl. 01) 165-75.
• 65 Noh YH, Jeong DU. Implementation of a Data Packet Generator Using Pattern Matching for Wearable ECG Monitoring Systems. Sensors 2014 Jul 15 14 (Suppl. 07) 12623-39.
• 66 Lopez-Meyer P, Fulk GD, Sazonov ES. Automatic Detection of Temporal Gait Parameters in Post-stroke Individuals. Ieee Trans Inf Technol Biomed 2011; Jul 15 (Suppl. 04) 594-601.
• 67 Fontana JM, Farooq M, Sazonov E. Automatic Ingestion Monitor: A Novel Wearable Device for Monitoring of Ingestive Behavior. IEEE Trans Biomed Eng 2014; Jun 61 (Suppl. 06) 1772-9.
• 68 Sazonov E, Lopez-Meyer P, Tiffany S. A Wearable Sensor System for Monitoring Cigarette Smoking. J Stud Alcohol Drugs 2013; Nov 74 (Suppl. 06) 956-64.
• 69 Seiter J, Derungs A, Schuster-Amft C, Amft O, Tröster G. Daily life activity routine discovery in hemiparetic rehabilitation patients using topic models. Methods Inf Med 2015; 54 (Suppl. 03) 248-55.
• 70 Garcia-Ceja E, Brena RF, Carrasco-Jimenez JC, Garrido L. Long-Term Activity Recognition from Wristwatch Accelerometer Data. Sensors 2014 Nov 27 14 (Suppl. 12) 22500-24.
• 71 Cole BT, Roy SH, De Luca CJ, Nawab SH. Dynamical Learning and Tracking of Tremor and Dyskinesia From Wearable Sensors. IEEE Trans Neural Syst Rehabil Eng 2014; Sep 22 (Suppl. 05) 982-91.
• 72 Gao L, Bourke AK, Nelson J. Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems. Med Eng Phys 2014; Jun 36 (Suppl. 06) 779-85.
• 73 Tang W, Sazonov ES. Highly Accurate Recognition of Human Postures and Activities Through Classification With Rejection. IEEE J Biomed Health Inform 2014; Jan 18 (Suppl. 01) 309-15.
• 74 Teichmann D, Kuhn A, Leonhardt S, Walter M. Human motion classification based on a textile integrated and wearable sensor array. Physiol Meas 2013; Sep 34 (Suppl. 09) 963-75.
• 75 Ganea R, Paraschiv-lonescu A, Aminian K. Detection and Classification of Postural Transitions in Real-World Conditions. IEEE Trans Neural Syst Rehabil Eng 2012; Sep 20 (Suppl. 05) 688-96.
• 76 Özdemir AT, Barshan B. Detecting Falls with Wearable Sensors Using Machine Learning Techniques. Sensors 2014 Jun 18 14 (Suppl. 06) 10691-708.
• 77 Clifton L, Clifton DA, Pimentel MAF, Watkinson PJ, Tarassenko L. Predictive Monitoring of Mobile Patients by Combining Clinical Observations With Data From Wearable Sensors. IEEE J Biomed Health Inform 2014; May 18 (Suppl. 03) 722-30.
• 78 Lopez-Meyer P, Tiffany S, Patil Y, Sazonov E. Monitoring of Cigarette Smoking Using Wearable Sensors and Support Vector Machines. IEEE Trans Biomed Eng 2013; Jul 60 (Suppl. 07) 1867-72.
• 79 Barsocchi P. Position Recognition to Support Bed-sores Prevention. IEEE J Biomed Health Inform 2013; Jan 17 (Suppl. 01) 53-9.
• 80 Sun H, Yuao T. Curve aligning approach for gait authentication based on a wearable accelerometer. Physiol Meas 2012; 33 (Suppl. 06) 1111.
• 81 Alshamsi A, Pianesi F, Lepri B, Pentland A, Rahwan I. Beyond Contagion: Reality Mining Reveals Complex Patterns of Social Influence. PLoS One [Internet] 2015 Aug 27 [cited 2015 Nov 15];10(8). Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4551670/
• 82 Kher R, Pawar T, Thakar V, Shah H. Physical activities recognition from ambulatory ECG signals using neuro-fuzzy classifiers and support vector machines. J Med Eng Technol 2015 Feb 17 39 (Suppl. 02) 138-52.
• 83 Lin H-C, Chiang S-Y, Lee K, Kan Y-C. An Activity Recognition Model Using Inertial Sensor Nodes in a Wireless Sensor Network for Frozen Shoulder Rehabilitation Exercises. Sensors 2015 Jan 19 15 (Suppl. 01) 2181-204.
• 84 Lin C-W, Yang Y-TC, Wang J-S, Yang Y-C. A Wearable Sensor Module With a Neural-Network-Based Activity Classification Algorithm for Daily Energy Expenditure Estimation. IEEE Trans Inf Technol Biomed 2012; Sep 16 (Suppl. 05) 991-8.
• 85 Wang Z, Jiang M, Hu Y, Li H. An Incremental Learning Method Based on Probabilistic Neural Networks and Adjustable Fuzzy Clustering for Human Activity Recognition by Using Wearable Sensors. IEEE Trans Inf Technol Biomed 2012; Jul 16 (Suppl. 04) 691-9.
• 86 Yurtman A, Barshan B. Automated evaluation of physical therapy exercises using multi-template dynamic time warping on wearable sensor signals. Comput Methods Programs Biomed 2014; Nov 117 (Suppl. 02) 189-207.
• 87 Taborri J, Rossi S, Palermo E, Patanè F, Cappa P. A Novel HMM Distributed Classifier for the Detection of Gait Phases by Means of a Wearable Inertial Sensor Network. Sensors 2014 Sep 2 14 (Suppl. 09) 16212-34.
• 88 Mannini A, Sabatini AM. Accelerometry-Based Classification of Human Activities Using Markov Modeling. Comput Intell Neurosci [Internet] 2011 [cited 2015 Nov 21];2011. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3166724/
• 89 Valenza G, Nardelli M, Lanata A, Gentili C, Bertschy G, Paradiso R. et al. Wearable Monitoring for Mood Recognition in Bipolar Disorder Based on History-Dependent Long-Term Heart Rate Variability Analysis. IEEE J Biomed Health Inform 2014; Sep 18 (Suppl. 05) 1625-35.
• 90 Koshmak G, Linden M, Loutfi A. Dynamic Bayesian Networks for Context-Aware Fall Risk Assessment. Sensors 2014 May 23 14 (Suppl. 05) 9330-48.
• 91 Nam Y, Park JW. Child Activity Recognition Based on Cooperative Fusion Model of a Triaxial Accelerometer and a Barometric Pressure Sensor. IEEE J Biomed Health Inform 2013; Mar 17 (Suppl. 02) 420-6.
• 92 Liu S, Gao RX, John D, Staudenmayer JW, Freed-son PS. Multisensor Data Fusion for Physical Activity Assessment. IEEE Trans Biomed Eng 2012; Mar 59 (Suppl. 03) 687-96.
• 93 Piatetsky-Shapiro, G.. Knowledge Discovery in Real Databases: A Report on the IJCAI-89 Workshop. AI Mag 1989; 11 (Suppl. 05) 68-70.
• 94 Ericsson KA, Simon HA. Protocol analysis: Verbal reports as data. MIT Press; 1992
• 95 Barnett GO, Cimino JJ, Hupp JA, Hoffer EP. DXplain. An evolving diagnostic decision-support system. JAMA 1987 Jul 3 258 (Suppl. 01) 67-74.
• 96 Evans DA, Patel VL. editors. Advanced Models of Cognition for Medical Training and Practice [Internet]. Berlin, Heidelberg: Springer Berlin Heidelberg; 1992 [cited 2016 Jul 9]. Available from: link.springer.com/10.1007/978-3-662-02833-9
• 97 Patel VL, Kannampallil TG. Cognitive informatics in biomedicine and healthcare. J Biomed Inform 2015; Feb 53: 3-14.
• 98 Barnes FS. Some engineering models for interactions of electric and magnetic fields with biological systems. [Review] [63 refs]. Bioelectromagnetics 1992; 67-85.
• 99 Cavallo V, Giovagnorio F, Messineo D. Neural networks in diagnostic imaging. Rays 1992; Dec 17 (Suppl. 04) 556-61.
• 100 Clark BD, Leong SW. Crossmatch prediction of highly sensitized patients. Clin Transpl 1992; 435-55.
• 101 Howells SL, Maxwell RJ, Peet AC, Griffiths JR. An investigation of tumor 1H nuclear magnetic resonance spectra by the application of chemometric techniques. Magn Reson Med 1992; Dec 28 (Suppl. 02) 214-36.
• 102 Maclin PS, Dempsey J. Using an artificial neural network to diagnose hepatic masses. J Med Syst 1992; Oct 16 (Suppl. 05) 215-25.
• 103 Miller AS, Blott BH, Hames TK. Review of neural network applications in medical imaging and signal processing. [Review] [81 refs]. Med Biol Eng Comput 1992; Sep 30 (Suppl. 05) 449-64.
• 104 Schaberg ES, Jordan WH, Kuyatt BL. Artificial intelligence in automated classification of rat vaginal smear cells. Anal Quant Cytol Histol 1992; Dec 14 (Suppl. 06) 446-50.
• 105 Vieth M, Kolinski A, Skolnick J, Sikorski A. Prediction of protein secondary structure by neural networks: encoding short and long range patterns of amino acid packing. Acta Biochim Pol 1992; 39 (Suppl. 04) 369-92.
• 106 Wittenberg G, Kristan WBJ. Analysis and modeling of the multisegmental coordination of shortening behavior in the medicinal leech. II. Role of identified interneurons. J Neurophysiol 1992; Nov 68 (Suppl. 05) 1693-707.
• 107 Wu C, Whitson G, McLarty J, Ermongkonchai A, Chang TC. Protein classification artificial neural system. Protein Sci 1992; May 1 (Suppl. 05) 667-77.
• 108 Kernan WJ, Meeker WQ. A statistical test to assess changes in spontaneous behavior of rats observed with a computer pattern recognition system. J Biopharm Stat 1992; 2 (Suppl. 01) 115-35.
• 109 Pearl J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.; 1988
• 110 Cooper GF, Herskovits E. A Bayesian method for the induction of probabilistic networks from data. Mach Learn 9 (Suppl. 04) 309-47.
• 111 Lauritzen SL, Spiegelhalter DJ. Local Computations with Probabilities on Graphical Structures and Their Application to Expert Systems. J R Stat Soc Ser B Methodol 1988; 50 (Suppl. 02) 157-224.
• 112 Shwe MA, Middleton B, Heckerman DE, Henrion M, Horvitz EJ, Lehmann HP. et al. Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base. I. The probabilistic model and inference algorithms. Methods Inf Med 1991; Oct 30 (Suppl. 04) 241-55.
• 113 Cowell RG, Dawid AP, Hutchinson T, Spiegelhalter DJ. A Bayesian expert system for the analysis of an adverse drug reaction. Artif Intell Med 1991 Oct 1 3 (Suppl. 05) 257-70.
• 114 Spiegelhalter DJ. Probabilistic Reasoning in Expert Systems. Am J Math Manage Sci 1991; Jan 9 3–4 191-210.
• 115 Muggleton S. Inductive logic programming. New Gener Comput 1991; Feb 8 (Suppl. 04) 295-318.
• 116 Lavrac N, Dzeroski S. Inductive Logic Programming: Techniques and Applications. New York, NY, 10001: Routledge; 1993
• 117 Koza JR. Genetic programming: on the programming of computers by means of natural selection [Internet]. MIT Press; 1992 [cited 2016 Jul 9]. Available from: http://dl.acm.org/citation.cfm?id=138936
• 118 Blei DM, Ng AY, Jordan MI. Latent Dirichlet Allocation. J Mach Learn Res 2003; Mar 3: 993-1022.
• 119 Boser BE, Guyon IM, Vapnik VN. A Training Algorithm for Optimal Margin Classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory [Internet]. New York, NY, USA: ACM; 1992. [cited 2015 Nov 22]. p. 144-152. (COLT ’92). Available from: http://doi.acm.org/10.1145/130385.130401
• 120 International Human Genome Sequencing Consortium.. Finishing the euchromatic sequence of the human genome. Nature 2004 Oct 21 431 7011 931-45.
• 121 Lockhart DJ, Dong H, Byrne MC, Follettie MT, Gallo MV, Chee MS. et al. Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol 1996; Dec 14 (Suppl. 13) 1675-80.
• 122 Precision Medicine Initiative [Internet].. National Institutes of Health (NIH). [cited 2016 Jul 15]. Available from: http://www.nih.gov/precision-medicine-initiative-cohort-program
• 123 Sacchi L, Lanzola G, Viani N, Quaglini S. Personalization and Patient Involvement in Decision Support Systems: Current Trends. Yearb Med Inform 2015 Aug 13 10 (Suppl. 01) 106-18.
• 124 Toga AW, Clark KA, Thompson PM, Shattuck DW, Van Horn JD. Mapping the Human Connectome. Neurosurgery 2012; Jul 71 (Suppl. 01) 1-5.
• 125 Health Information Privacy | HHS.gov [Internet].. [cited 2016 Jan 24]. Available from: http://www.hhs.gov/hipaa/126.BD2K Home Page | Data Science at NIH [Internet]. [cited 2016 Jul 15]. Available from: datascience.nih.gov/bd2k
• 127 Murdoch TB, Detsky AS. THe inevitable application of big data to health care. JAMA 2013 Apr 3 309 (Suppl. 13) 1351-2.
• 128 Rumsfeld JS, Joynt KE, Maddox TM. Big data analytics to improve cardiovascular care: promise and challenges. Nat Rev Cardiol 2016; Jun 13 (Suppl. 06) 350-9.
• 129 Schmidhuber J. Deep learning in neural networks: an overview. [Review]. Neural Netw 2015 Jan; 85-117.
• 130 LeCun Y, Bengio Y, Hinton G. Deep learning. [Review]. Nature 2015; May 521 7553 436-44.
• 131 Guclu U, van Gerven MAJ. Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream. J Neurosci 2015; Jul 35 (Suppl. 27) 10005-14.
• 132 Yan Z, Zhan Y, Peng Z, Liao S, Shinagawa Y, Metaxas DN. et al. Bodypart Recognition Using Multi-stage Deep Learning. Inf Process Med Imaging 2015; 449-61.
• 133 Ibrahim R, Yousri NA, Ismail MA, El-Makky NM. Multi-level gene/MiRNA feature selection using deep belief nets and active learning. Conf Proc 2014; 3957-60
• 134 Lake BM, Salakhutdinov R, Tenenbaum JB. Human-level concept learning through probabilistic program induction. Science 2015; Dec 350 6266 1332-8.