Open AccessDiscovering novel disease comorbidities using electronic medical records
Author(s) -
Shikha Chaganti,
Valerie F. Welty,
Warren D. Taylor,
Kimberly Albert,
Michelle D. Failla,
Carissa J. Cascio,
Seth A. Smith,
Louise A. Mawn,
Susan M. Resnick,
Lori L. BeasonHeld,
Francesca Bagnato,
Thomas A. Lasko,
Jeffrey D. Blume,
Bennett A. Landman
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0225495
Subject(s) - leverage (statistics) , data science , disease , medical record , computer science , population , diagnosis code , rochester epidemiology project , medline , autism , medicine , machine learning , psychiatry , pathology , environmental health , population based study , radiology , political science , law
Increasing reliance on electronic medical records at large medical centers provides unique opportunities to perform population level analyses exploring disease progression and etiology. The massive accumulation of diagnostic, procedure, and laboratory codes in one place has enabled the exploration of co-occurring conditions, their risk factors, and potential prognostic factors. While most of the readily identifiable associations in medical records are (now) well known to the scientific community, there is no doubt many more relationships are still to be uncovered in EMR data. In this paper, we introduce a novel finding index to help with that task. This new index uses data mined from real-time PubMed abstracts to indicate the extent to which empirically discovered associations are already known (i.e., present in the scientific literature). Our methods leverage second-generation p -values, which better identify associations that are truly clinically meaningful. We illustrate our new method with three examples: Autism Spectrum Disorder, Alzheimer’s Disease, and Optic Neuritis. Our results demonstrate wide utility for identifying new associations in EMR data that have the highest priority among the complex web of correlations and causalities. Data scientists and clinicians can work together more effectively to discover novel associations that are both empirically reliable and clinically understudied.