# Mining Electronic Health Records: A Survey

arXiv: https://arxiv.org/abs/1702.03222 · HTML: https://mlsanity.com/paper/1702.03222/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2017.

## Built on by (most cited first, 4 of 4)

- [What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data](https://mlsanity.com/paper/2106.06410/) (2021)
- [tsflex: flexible time series processing & feature extraction](https://mlsanity.com/paper/2111.12429/) (2021)
- [MULTIPAR: Supervised Irregular Tensor Factorization with Multi-task Learning](https://mlsanity.com/paper/2208.00993/) (2022)
- [Self-supervised Representation Learning on Electronic Health Records with Graph Kernel Infomax](https://mlsanity.com/paper/2209.00655/) (2022)

## Abstract (arXiv)

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.