AI in Healthcare

The Artificial Intelligence (AI) revolution, enabled by the convergence of big data analytics, machine learning and very powerful computers, offers unprecedented opportunities for transforming the healthcare industry. With a deep understanding of healthcare data as well as the biological and clinical problems underlying patient care, UB and its collaborative partners are uniquely poised to shape the future of an AI-centered Health IT primed for safe and effective delivery of healthcare.

Ushering in a new era in healthcare

Academic and applied programs at UB excel in areas of engineering, medicine and the health sciences. Individual faculty and larger inter-disciplinary teams, working under the growing engineering-driven medicine (EDM) field at the convergence of medicine and engineering, aim to help address grand challenge questions around individualized diagnostics and therapies. Big data and machine learning can be used to understand the environmental contributions to disease, predict patient responses to drugs or other medical interventions, or track and help halt the spread of infectious diseases in large populations. EDM heralds a new era in medicine with global economic impact.

UB faculty are researching and collaborating in several areas, including:

  • Disease Surveillance
  • Digital Pathology
  • Knowledge and Data-Driven Models for Healthcare
  • Safety of Medical Products / Processes

What follows are references noting the work being done.


1.       Elkin PL, Trusko BE, Koppel R, Speroff T, Mohrer D, Sakji S, Gurewitz I, Tuttle M, Brown SH.

2.       “Secondary use of clinical data.”, Stud Health Technol Inform, 2010, 155:14-29. PMID: 20543306.

3.       b.) Murff HJ, FitzHenry F, Matheny ME, Gentry N, Kotter KL, Crimin K, Dittus RS, Rosen AK, Elkin    PL, Brown SH, Speroff T.  Automated identification of postoperative complications within an       electronic medical record using natural language processing.  JAMA, 2011, 306(8):848-55.        PMID:       21862746.

4.       Brown SH, Elkin PL, Fielstein E, Speroff T. “eQuality for All – extending automated quality measurement from free text clinical narratives”, AMIA Annu Symp Proc., 2008, 6:71-5. PMID: 18999230.

7.       Elkin PL, Brown SH, Carter J, Bauer BA, Wahner-Roedler D, Bergstrom L, Pittelkow M, Rosse C. “Guideline  and  Quality  Indicators  for  Development,  Purchase  and  Use  of  Controlled  health      vocabularies”.  International Journal of Medical  Informatics,  2002,  Vol  68/1-3  pp  175-186.  PMID:     12467801.

8.       Brown SH, Speroff T, Fielstein EM, Bauer BA, Wahner-Roedler DL, Greevy R, Elkin PL. eQuality: Automatic assessment from narrative clinical reports. Mayo Clin Proc 2006 81(11):1472-1481

a.       Brown SH, Elkin PL, Fielstein E, Speroff T. “eQuality for All – extending automated quality measurement from free text clinical narratives”, AMIA Annu Symp Proc., 2008, 6:71-5.  PMID: 18999230.

9.       Murff HJ, FitzHenry F, Matheny ME, Gentry N, Kotter KL, Crimin K, Dittus RS, Rosen AK, Elkin PL, Brown SH, Speroff T.  Automated identification of postoperative complications within an electronic medical record using natural language processing.  JAMA, 2011, 306(8):848-55. PMID: 21862746.

10.   Elkin PL, Froehling D, Wahner-Roedler D, Brown SH, Bailey K.  “Comparison of NLP Biosurveillance Methods for Identifying Influenza from Encounter Notes”;  Ann Intern Med. 2012 Jan 3;156(1 Pt 1):11-8.

11.   Husser CS, Buchhalter JR, Raffo OS, Shabo A, Brown SH, Lee KE, Elkin PL. Standardization of microarray and pharmacogenomics data. Methods Mol Biol. 2006;316:111-57. Review.

12.   Elkin PL, Tuttle M, Trusko B, Brown SH. Bioprospecting: Novel marker discover obtained from the bibleome. BMC Bioinformatics. 2009 Feb 5;10 Suppl 2:S9.

13.   Rice KL, Lin X, Wolniak K, Ebert BL, Berkofsky-Fessler W, Buzzai M, Sun Y, Xi C, Elkin P, Levine R, Golub T, Gilliland DG, Crispino JD, Licht JD, Zhang W.  Analysis of genomic aberrations and gene expression profiling identifies novel lesions and pathways in myeloproliferative neoplasms.  Blood Cancer J. 2011 Nov;1(11): e40.

14.   Elkin PL, Frankel A, Liebow-Liebling EH, Elkin JR, Tuttle MS, Brown SH.  Bioprospecting the Bibleome: Adding Evidence to Support the Inflammatory Basis of Cancer.  Metabolomics (Los Angel). 2012 May 5;2(112). pii: 6451.

15.   Elkin PL, Peleg M, Lacson R, Bernstam E, Greenes R, Shortliffe EH. Toward standardization of electronic guideline representation. MD Computing 2000 Nov 39-44.

16.   Elkin PL, Liebow M, Bauer BA, Chaliki SS, Bergstrom LR, Wahner-Roedler DL et al. A diagnostic decision support system (DXplain) can decrease costs for diagnostically challenging cases.  Int J Med Inform. 2010 Nov;79(11):772-7.

17.   Peter L. Elkin, MD1, Daniel R. Schlegel, PhD1, Michael Anderson, MD2, Jordan Komm, MD1,2, Gregoire Ficheur, MD, PhD1, Leslie Bisson, MD2  Artificial Intelligence: Bayes vs. Heuristic Methods for Diagnostic Clinical Decision Support.  Submitted to Applied Clinical Informatics.

18.   Daniel R. Schlegel, PhD1, Grégoire Ficheur, MD, PhD1, Michael Anderson, MD2, Jordan Komm, MD1,2, Leslie Bisson, MD2, Peter L. Elkin, MD1 Comparing Heuristic and Machine Learning Approaches for an Orthopedic Diagnostic Clinical Decision Support System.  Submitted to AMIA Proceedings.

19.   Beuscart-Zephir MC*, Elkin PL **, Pelayo S*, Beuscart R. The human factors engineering approach to biomedical informatics projects: state of the art, results, benefits and challenges.  Yearb Med Inform. 2007:109-27.

20.   Beuscart-Zéphir MC, Aarts J, Elkin P. “Human factors engineering for healthcare IT clinical applications.”, Int J Med Inform. 2010 Apr;79(4):223-4. Epub 2010 Feb 18.Pelayo S, Anceaux F, Rogalski J, Elkin P, Beuscart-Zephir MC.  A comparison of the impact of CPOE implementation and organizational determinants on doctor-nurse communications and cooperation.  Int J Med Inform. 2012 Sep 20.

21.   Elkin PL, Beuscart-Zephir MC, Pelayo S, Patel V, Nøhr C.  The usability-error ontology.  Stud Health Technol Inform. 2013;194:91-6.

22.   Elkin PL.  Human Factors Engineering in HI: So What? Who Cares? and What's in It for You?  Healthc Inform Res. 2012 Dec;18(4):237-41

23.   Gaudioso C, Elkin P. Considerations of Human Factors in the Design and Implementation of Clinical Decision Support Systems for Tumor Boards. Stud Health Technol Inform. 2017;245:1324. PubMed PMID: 29295405

24.   Sinha S, Burstein GR, Leonard KE, Murphy TF, Elkin PL. Prescription Opioid dependence in Western New York: Using Data Analytics to Find an Answer to the Opioid Epidemic. Stud Health Technol Inform.  2017;245:594-598. PubMed PMID:29295165.

25.   Sinha S, Jensen M, Mullin S, Elkin PL. Safe Opioid Prescription: A SMART on FHIR Approach to Clinical Decision Support. Online J Public Health Inform. 2017 Sep 8;9(2):e193. doi: 10.5210/ojphi.v9i2.8034. eCollection 2017. PubMed PMID:29026458; PubMed Central PMCID: PMC5630280.

26.   Mullin S, Anand E, Sinha S, Song B, Zhao J, Elkin PL. Secondary Use of EHR: Interpreting Clinician Inter-Rater Reliability Through Qualitative Assessment. Stud Health Technol Inform. 2017;241:165-172. PubMed PMID: 28809201; PubMed Central PMCID: PMC5698262.

27.   Schlegel DR, Crowner C, Lehoullier F, Elkin PL. HTP-NLP: A New NLP System for High Throughput Phenotyping. Stud Health Technol Inform. 2017;235:276-280. PubMed PMID: 28423797.

28.   Chopra G, Kaushik S, Elkin PL, Samudrala R. Combating Ebola with Repurposed Therapeutics Using the CANDO Platform. Molecules. 2016 Nov 25;21(12). pii: E1537.  PubMed PMID: 27898018.

29.   Elkin PL, Johnson HC, Callahan MR, Classen DC. Improving patient safety reporting with the common formats: Common data representation for Patient Safety Organizations. J Biomed Inform. 2016 Dec;64:116-121. doi:10.1016/j.jbi.2016.09.020. Epub 2016 Sep 29. Review. PubMed PMID: 27693764.

30.   Elkin PL, Schlegel DR, Anand E. Recruiting Participants to Local Clinical Trials using Ontology and the IoT. Stud Health Technol Inform. 2016;221:119.  PubMed PMID: 27071893.

31.   Schlegel DR, Crowner C, Elkin PL. Automatically Expanding the Synonym Set of SNOMED CT using Wikipedia. Stud Health Technol Inform. 2015;216:619-23. PubMed PMID: 26262125.

32.   Gobbel GT, Reeves R, Jayaramaraja S, Giuse D, Speroff T, Brown SH, Elkin PL, Matheny ME. Development and evaluation of RapTAT: a machine learning system for concept mapping of phrases from medical narratives. J Biomed Inform. 2014 Apr;48:54-65. doi: 10.1016/j.jbi.2013.11.008. Epub 2013 Dec 4. PubMed PMID:24316051.

33.   Wang, X., Hripcsak, G., Markatou, M.* and Friedman, C.* (2009). Active computerized pharmacovigilance using natural language processing, statistics and electronic health records: a feasibility study. Journal of the American Medical Informatics Association, 16 (3), 338-345.

34.   2.  Wang, X., Chase, H., Markatou, M., Hripcsak, G. and Friedman, C. (2010). Selecting information in electronic health records for knowledge acquisition. Journal of Biomedical Informatics, 43, 595-601.

35.   3.   Botsis, T., Nguyen, M. D., Woo, E. J., Markatou, M. and Ball, R. (2011). Text mining for Vaccine Adverse Event Reporting System: Medical text classification using informative feature selection. Journal of the American Medical Informatics Association, 18, 631-638.

36.   4.   Markatou, M., Ball, R., Botsis, T., Nguyen, M. & Woo, E. J. (2015). A text mining system for large medical text datasets and corresponding medical text classification using informative feature selection (Patent Number: 9,075,796, awarded on July 9, 2015 (joint IBM/FDA ownership)).

37.   Auchincloss, A. H., Gebreab, S. Y., Mair, C., & Diez Roux, A. V. (2012). A review of spatial methods in epidemiology, 2000–2010. Annual Review of Public Health, 33, 107–122. doi:10.1146/annurev-publhealth-031811-124655

38.   Coppersmith, G., Dredze, M., & Harman, C. (2014). Quantifying mental health signals in twitter. In Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality (pp. 51-60).

39.   Chaix, B. , J. Merlo, D. Evans, C. Leal, & S. Havard. Neighbourhoods in eco-epidemiologic research: delimiting personal exposure areas. a response to riva, gauvin, apparicio and brodeur. Social science & medicine, 69(9):1306–1310, 2009.

40.   De Choudhury, M., Gamon, M., Counts, S., & Horvitz, E. (2013). Predicting depression via social media. ICWSM, 13, 1-10.

41.   Lynn, V., Son, Y., Kulkarni, V., Balasubramanian, N., & Schwartz, H. A. (2017). Human Centered NLP with User-Factor Adaptation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (pp. 1146-1155).

42.   Park, G., Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Stillwell, D. J., Kosinski, M., Ungar, L. H., & Seligman, M. E. P. (2014). Automatic personality assessment through social media language. Journal of Personality and Social Psychology, Nov 3 , 2015.

43.   Preotiuc-Pietro, D., Sap, M., Schwartz, H. A., & Ungar, L. H. (2015). Mental Illness Detection at the World Well-Being Project for the CLPsych 2015 Shared Task. In Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, NAACL.

44.   Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M. E., & Ungar, L. H. (2013). Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach. PLOS ONE, 8(9), e73791

45.   Schwartz, H.A., Sap, M., Kern, M.L., Eichstaedt, J.C., Kapelner, A., Agrawal, M., Blanco, E., Dziurzynski, L., Park, G., Stillwell, D., Kosinski, M., Seligman, M.E.P. & Ungar, L.H. (2016). Predicting individual well-being through the language of social media. In Biocomputing 2016: Proceedings of the Pacific Symposium (pp. 516-527).