# Patient Similarity Analysis with Longitudinal Health Data

arXiv: https://arxiv.org/abs/2005.06630 · HTML: https://mlsanity.com/paper/2005.06630/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2020.

## Built on by (most cited first, 3 of 3)

- [AttDMM: An Attentive Deep Markov Model for Risk Scoring in Intensive Care Units](https://mlsanity.com/paper/2102.04702/) (2021)
- [Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG Signal](https://mlsanity.com/paper/2308.02433/) (2023)
- [MiranDa: Mimicking the Learning Processes of Human Doctors to Achieve Causal Inference for Medication Recommendation](https://mlsanity.com/paper/2408.01445/) (2024)

## Abstract (arXiv)

Healthcare professionals have long envisioned using the enormous processing powers of computers to discover new facts and medical knowledge locked inside electronic health records. These vast medical archives contain time-resolved information about medical visits, tests and procedures, as well as outcomes, which together form individual patient journeys. By assessing the similarities among these journeys, it is possible to uncover clusters of common disease trajectories with shared health outcomes. The assignment of patient journeys to specific clusters may in turn serve as the basis for personalized outcome prediction and treatment selection. This procedure is a non-trivial computational problem, as it requires the comparison of patient data with multi-dimensional and multi-modal features that are captured at different times and resolutions. In this review, we provide a comprehensive overview of the tools and methods that are used in patient similarity analysis with longitudinal data and discuss its potential for improving clinical decision making.