Mining Electronic Health Records: A Survey

By Pranjul Yadav, Michael S. Steinbach, Vipin Kumar, György J. Simon. 2017

87 citations recorded by the source corpus.

The continuously increasing cost of the US healthcare system has received significant attention. Central to the ideas aimed at curbing this trend is the use of technology, in the form of the mandate to implement electronic health records (EHRs). EHRs consist of patient information such as demographics, medications, laboratory test results, diagnosis codes and procedures. Mining EHRs could lead to improvement in patient health management as EHRs contain detailed information related to disease prognosis for large patient populations. In this manuscript, we provide a structured and comprehensive overview of data mining techniques for modeling EHR data. We first provide a detailed understanding of the major application areas to which EHR mining has been applied and then discuss the nature of EHR data and its accompanying challenges. Next, we describe major approaches used for EHR mining, the metrics associated with EHRs, and the various study designs. With this foundation, we then provide a systematic and methodological organization of existing data mining techniques used to model EHRs and discuss ideas for future research. We conclude this survey with a comprehensive summary of clinical data mining applications of EHR data, as illustrated in the online supplement.

Subfields: Artificial Intelligence, Health Information Management, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Slobodan Vucetic
2. Tian Bai
3. Shanshan Zhang
4. Brian L. Egleston

In Health Information Management

1. Slobodan Vucetic
2. Tian Bai
3. Shanshan Zhang
4. Brian L. Egleston

In Molecular Biology

1. Slobodan Vucetic
2. Vladimir N Uversky
3. A. Keith Dunker
4. Lukasz Kurgan
5. Burkhard Rost

Citations in the corpus

• Interpretable Representation Learning for Healthcare via Capturing Disease Progression through Time (2018, background): “In order to trust the model, it is essential for doctors and domain experts to understand the rationale behind the model predictions [20, 32].”