Discovering novel disease comorbidities using electronic medical records

Abstract

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.

Trends in Optic Neuritis Incidence and Prevalence in the UK and Association With Systemic and Neurologic Disease

“…5 ON also develops in conjunction with other ocular presentations, including uveitis, 7,8 and scleritis. 9 This study had two aims. Firstly, to ascertain 22-year trends in the incidence and prevalence of ON in the general UK population, and risk factors for incident ON.…”

“…Optic neuritis also develops in the context of reactive, primary, and secondary vasculitides, infection, postinfectious autoimmune reactions (including with human coronavirus disease 2019 [COVID-19]), recent vaccination, and medication (eg, anti–tumor necrosis factor α therapy) . In addition, ON develops in conjunction with other ocular presentations, including uveitis and scleritis …”

From real-world electronic health record data to real-world results using artificial intelligence

“…RWD-EHR have led to findings such as the association between EBV exposure and multiple sclerosis development in the US military data, autoantibodies preceding SLE, as well as lifestyle and SLE development. [8][9][10][11] RWD-EHR can also capture data surrounding the time disease development compared with studies with fixed visits of trials and cohorts. In addition, the number of dimensions or types of data measured in the real world tends to be higher than RCT or observational cohort studies; EHR data contains all data collected as part of clinical care on all patients who visited a clinic or healthcare system.…”

“…RWD-EHR allows us to look back at previously collected data. RWD-EHR have led to findings such as the association between EBV exposure and multiple sclerosis development in the US military data, autoantibodies preceding SLE, as well as lifestyle and SLE development 8–11. RWD-EHR can also capture data surrounding the time disease development compared with studies with fixed visits of trials and cohorts.…”

Precision medicine in the era of artificial intelligence: implications in chronic disease management

“…Moreover, ML/AI is transforming the electronic health record (EHR) field and over time EHRs powered with AI were shown to reveal more about diseases. The ML/AI, tools applied to the health records of patients in EHRs and accurately predicted their probability of acquiring or developing chronic diseases [ 125 ].…”