Modeling Healthcare Quality via Compact Representations of Electronic Health Records

Stojanović, Jelena; Gligorijevic, Djordje; Radosavljević, Vladan; Djuric, Nemanja; Grbovic, Mihajlo; Obradović, Zoran

doi:10.1109/tcbb.2016.2591523

Cited by 31 publications

(15 citation statements)

References 19 publications

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“…Such sources of data provide a massive opportunity for novel data-driven approaches to give rise to automated site selection. Claims data, as 1 source of information in this work, were successfully used to address several high-impact health care tasks, 6,7 especially through deep learning models. 8,9 An additional challenge to clinical trial planning is the nuanced nature of clinical trials: no 2 studies are alike, and studies are very difficult to parameterize.…”

Section: Introductionmentioning

confidence: 99%

Optimizing clinical trials recruitment via deep learning

Gligorijević

Gligorijevic

Pavlovski

et al. 2019

Journal of the American Medical Informatics Association

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Objective Clinical trials, prospective research studies on human participants carried out by a distributed team of clinical investigators, play a crucial role in the development of new treatments in health care. This is a complex and expensive process where investigators aim to enroll volunteers with predetermined characteristics, administer treatment(s), and collect safety and efficacy data. Therefore, choosing top-enrolling investigators is essential for efficient clinical trial execution and is 1 of the primary drivers of drug development cost. Materials and Methods To facilitate clinical trials optimization, we propose DeepMatch (DM), a novel approach that builds on top of advances in deep learning. DM is designed to learn from both investigator and trial-related heterogeneous data sources and rank investigators based on their expected enrollment performance on new clinical trials. Results Large-scale evaluation conducted on 2618 studies provides evidence that the proposed ranking-based framework improves the current state-of-the-art by up to 19% on ranking investigators and up to 10% on detecting top/bottom performers when recruiting investigators for new clinical trials. Discussion The extensive experimental section suggests that DM can provide substantial improvement over current industry standards in several regards: (1) the enrollment potential of the investigator list, (2) the time it takes to generate the list, and (3) data-informed decisions about new investigators. Conclusion Due to the great significance of the problem at hand, related research efforts are set to shift the paradigm of how investigators are chosen for clinical trials, thereby optimizing and automating them and reducing the cost of new therapies.

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Section: Introductionmentioning

confidence: 99%

Optimizing clinical trials recruitment via deep learning

Gligorijević

Gligorijevic

Pavlovski

et al. 2019

Journal of the American Medical Informatics Association

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“…While word2vec was initially designed for handling text, recent studies demonstrate that word2vec could learn representations of other types of data, including medical codes from EHR data [21–25]. Choi et al used word2vec to learn the vector representations of medical codes using longitudinal medical records and show that the related codes indeed have similar vector representations [22].…”

Section: Introductionmentioning

confidence: 99%

EHR phenotyping via jointly embedding medical concepts and words into a unified vector space

Bai

Chanda

Egleston

et al. 2018

BMC Med Inform Decis Mak

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BackgroundThere has been an increasing interest in learning low-dimensional vector representations of medical concepts from Electronic Health Records (EHRs). Vector representations of medical concepts facilitate exploratory analysis and predictive modeling of EHR data to gain insights about the patterns of care and health outcomes. EHRs contain structured data such as diagnostic codes and laboratory tests, as well as unstructured free text data in form of clinical notes, which provide more detail about condition and treatment of patients.MethodsIn this work, we propose a method that jointly learns vector representations of medical concepts and words. This is achieved by a novel learning scheme based on the word2vec model. Our model learns those relationships by integrating clinical notes and sets of accompanying medical codes and by defining joint contexts for each observed word and medical code.ResultsIn our experiments, we learned joint representations using MIMIC-III data. Using the learned representations of words and medical codes, we evaluated phenotypes for 6 diseases discovered by our and baseline method. The experimental results show that for each of the 6 diseases our method finds highly relevant words. We also show that our representations can be very useful when predicting the reason for the next visit.ConclusionsThe jointly learned representations of medical concepts and words capture not only similarity between codes or words themselves, but also similarity between codes and words. They can be used to extract phenotypes of different diseases. The representations learned by the joint model are also useful for construction of patient features.

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“…[ 13 ] [ 19 ] As with genomics, for which the ability to capture increasingly larger orders of magnitude of data spurred the development of novel computational and statistical tools to analyze that data, the field of EHR analytics is rapidly evolving. [ 20 , 21 ] Very few studies using the EHR to develop predictive models have focused on medication adherence. [ 22 ] Therefore, in this study we used EHR data from a well-defined cohort of patients cared for by the Military Health System (MHS) to build a predictive model of statin adherence in patients who have initiated statin therapy.…”

Section: Introductionmentioning

confidence: 99%

An electronic health record based model predicts statin adherence, LDL cholesterol, and cardiovascular disease in the United States Military Health System

Lucas¹,

Bazemore²,

Alo³

et al. 2017

PLoS ONE

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HMG-CoA reductase inhibitors (or “statins”) are important and commonly used medications to lower cholesterol and prevent cardiovascular disease. Nearly half of patients stop taking statin medications one year after they are prescribed leading to higher cholesterol, increased cardiovascular risk, and costs due to excess hospitalizations. Identifying which patients are at highest risk for not adhering to long-term statin therapy is an important step towards individualizing interventions to improve adherence. Electronic health records (EHR) are an increasingly common source of data that are challenging to analyze but have potential for generating more accurate predictions of disease risk. The aim of this study was to build an EHR based model for statin adherence and link this model to biologic and clinical outcomes in patients receiving statin therapy. We gathered EHR data from the Military Health System which maintains administrative data for active duty, retirees, and dependents of the United States armed forces military that receive health care benefits. Data were gathered from patients prescribed their first statin prescription in 2005 and 2006. Baseline billing, laboratory, and pharmacy claims data were collected from the two years leading up to the first statin prescription and summarized using non-negative matrix factorization. Follow up statin prescription refill data was used to define the adherence outcome (> 80 percent days covered). The subsequent factors to emerge from this model were then used to build cross-validated, predictive models of 1) overall disease risk using coalescent regression and 2) statin adherence (using random forest regression). The predicted statin adherence for each patient was subsequently used to correlate with cholesterol lowering and hospitalizations for cardiovascular disease during the 5 year follow up period using Cox regression. The analytical dataset included 138 731 individuals and 1840 potential baseline predictors that were reduced to 30 independent EHR “factors”. A random forest predictive model taking patient, statin prescription, predicted disease risk, and the EHR factors as potential inputs produced a cross-validated c-statistic of 0.736 for classifying statin non-adherence. The addition of the first refill to the model increased the c-statistic to 0.81. The predicted statin adherence was independently associated with greater cholesterol lowering (correlation = 0.14, p < 1e-20) and lower hospitalization for myocardial infarction, coronary artery disease, and stroke (hazard ratio = 0.84, p = 1.87E-06). Electronic health records data can be used to build a predictive model of statin adherence that also correlates with statins’ cardiovascular benefits.

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Modeling Healthcare Quality via Compact Representations of Electronic Health Records

Cited by 31 publications

References 19 publications

Optimizing clinical trials recruitment via deep learning

Optimizing clinical trials recruitment via deep learning

EHR phenotyping via jointly embedding medical concepts and words into a unified vector space

An electronic health record based model predicts statin adherence, LDL cholesterol, and cardiovascular disease in the United States Military Health System

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