# Supervised multi-specialist topic model with applications on large-scale electronic health record data

arXiv: https://arxiv.org/abs/2105.01238 · HTML: https://mlsanity.com/paper/2105.01238/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 4 May 2021.

## Builds on (most cited first, 10 of 10)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Scalable and accurate deep learning for electronic health records](https://mlsanity.com/paper/1801.07860/) (2018)
- [Doctor AI: Predicting Clinical Events via Recurrent Neural Networks](https://mlsanity.com/paper/1511.05942/) (2015)
- [GRAM: Graph-based Attention Model for Healthcare Representation Learning](https://mlsanity.com/paper/1611.07012/) (2016)
- [Learning to Diagnose with LSTM Recurrent Neural Networks](https://mlsanity.com/paper/1511.03677/) (2015)
- [Deepr: A Convolutional Net for Medical Records](https://mlsanity.com/paper/1607.07519/) (2016)
- [Temporal Convolutional Neural Networks for Diagnosis from Lab Tests](https://mlsanity.com/paper/1511.07938/) (2015)
- [Identifiable Phenotyping using Constrained Non-Negative Matrix Factorization](https://mlsanity.com/paper/1608.00704/) (2016)
- [The Use of Autoencoders for Discovering Patient Phenotypes](https://mlsanity.com/paper/1703.07004/) (2017)
- [Modeling disease progression in longitudinal EHR data using continuous-time hidden Markov models](https://mlsanity.com/paper/1812.00528/) (2018)

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

- [MixEHR-SurG: a joint proportional hazard and guided topic model for inferring mortality-associated topics from electronic health records](https://mlsanity.com/paper/2312.13454/) (2023)
- [TrajGPT: Irregular Time-Series Representation Learning for Health Trajectory Analysis](https://mlsanity.com/paper/2410.02133/) (2024)
- [Modeling electronic health record data using a knowledge-graph-embedded topic model](https://mlsanity.com/paper/2206.01436/) (2022)
- [MixEHR-Nest: Identifying Subphenotypes within Electronic Health Records through Hierarchical Guided-Topic Modeling](https://mlsanity.com/paper/2410.13217/) (2024)

## Abstract (arXiv)

Motivation: Electronic health record (EHR) data provides a new venue to elucidate disease comorbidities and latent phenotypes for precision medicine. To fully exploit its potential, a realistic data generative process of the EHR data needs to be modelled. We present MixEHR-S to jointly infer specialist-disease topics from the EHR data. As the key contribution, we model the specialist assignments and ICD-coded diagnoses as the latent topics based on patient's underlying disease topic mixture in a novel unified supervised hierarchical Bayesian topic model. For efficient inference, we developed a closed-form collapsed variational inference algorithm to learn the model distributions of MixEHR-S. We applied MixEHR-S to two independent large-scale EHR databases in Quebec with three targeted applications: (1) Congenital Heart Disease (CHD) diagnostic prediction among 154,775 patients; (2) Chronic obstructive pulmonary disease (COPD) diagnostic prediction among 73,791 patients; (3) future insulin treatment prediction among 78,712 patients diagnosed with diabetes as a mean to assess the disease exacerbation. In all three applications, MixEHR-S conferred clinically meaningful latent topics among the most predictive latent topics and achieved superior target prediction accuracy compared to the existing methods, providing opportunities for prioritizing high-risk patients for healthcare services. MixEHR-S source code and scripts of the experiments are freely available at https://github.com/li-lab-mcgill/mixehrS