# Machine Learning for Administrative Health Records: A Systematic Review of Techniques and Applications

arXiv: https://arxiv.org/abs/2308.14216 · HTML: https://mlsanity.com/paper/2308.14216/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 112 references that are in the map.
- First posted on arXiv 27 Aug 2023.

## Builds on (most cited first, 13 of 13)

- [GRAM: Graph-based Attention Model for Healthcare Representation Learning](https://mlsanity.com/paper/1611.07012/) (2016)
- [Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis](https://mlsanity.com/paper/1706.03446/) (2017)
- [Language Models Are An Effective Patient Representation Learning Technique For Electronic Health Record Data](https://mlsanity.com/paper/2001.05295/) (2020)
- [MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare](https://mlsanity.com/paper/1810.09593/) (2018)
- [Multi-layer Representation Learning for Medical Concepts](https://mlsanity.com/paper/1602.05568/) (2016)
- [Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record](https://mlsanity.com/paper/1810.04793/) (2018)
- [Secondary Use of Electronic Health Record: Opportunities and Challenges](https://mlsanity.com/paper/2001.09479/) (2020)
- [Mixture-based Multiple Imputation Model for Clinical Data with a Temporal Dimension](https://mlsanity.com/paper/1908.04209/) (2019)
- [Comparative Visual Analytics for Assessing Medical Records with Sequence Embedding](https://mlsanity.com/paper/2002.08356/) (2020)
- [Neural Topic Models with Survival Supervision: Jointly Predicting Time-to-Event Outcomes and Learning How Clinical Features Relate](https://mlsanity.com/paper/2007.07796/) (2020)
- [A Markov decision process approach to optimizing cancer therapy using multiple modalities](https://mlsanity.com/paper/1706.09481/) (2017)
- [ELMV: an Ensemble-Learning Approach for Analyzing Electrical Health Records with Significant Missing Values](https://mlsanity.com/paper/2006.14942/) (2020)
- [Beyond Topics: Discovering Latent Healthcare Objectives from Event Sequences](https://mlsanity.com/paper/2110.01160/) (2021)

## Abstract (arXiv)

Machine learning provides many powerful and effective techniques for analysing heterogeneous electronic health records (EHR). Administrative Health Records (AHR) are a subset of EHR collected for administrative purposes, and the use of machine learning on AHRs is a growing subfield of EHR analytics. Existing reviews of EHR analytics emphasise that the data-modality of the EHR limits the breadth of suitable machine learning techniques, and pursuable healthcare applications. Despite emphasising the importance of data modality, the literature fails to analyse which techniques and applications are relevant to AHRs. AHRs contain uniquely well-structured, categorically encoded records which are distinct from other data-modalities captured by EHRs, and they can provide valuable information pertaining to how patients interact with the healthcare system. This paper systematically reviews AHR-based research, analysing 70 relevant studies and spanning multiple databases. We identify and analyse which machine learning techniques are applied to AHRs and which health informatics applications are pursued in AHR-based research. We also analyse how these techniques are applied in pursuit of each application, and identify the limitations of these approaches. We find that while AHR-based studies are disconnected from each other, the use of AHRs in health informatics research is substantial and accelerating. Our synthesis of these studies highlights the utility of AHRs for pursuing increasingly complex and diverse research objectives despite a number of pervading data- and technique-based limitations. Finally, through our findings, we propose a set of future research directions that can enhance the utility of AHR data and machine learning techniques for health informatics research.