# Inpatient2Vec: Medical Representation Learning for Inpatients

arXiv: https://arxiv.org/abs/1904.08558 · HTML: https://mlsanity.com/paper/1904.08558/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2019.

## Built on by (most cited first, 7 of 7)

- [Multimodal data matters: language model pre-training over structured and unstructured electronic health records](https://mlsanity.com/paper/2201.10113/) (2022)
- [ICE-NODE: Integration of Clinical Embeddings with Neural Ordinary Differential Equations](https://mlsanity.com/paper/2207.01873/) (2022)
- [Inadequacy of common stochastic neural networks for reliable clinical decision support](https://mlsanity.com/paper/2401.13657/) (2024)
- [Predicting Opioid Use Disorder from Longitudinal Healthcare Data using Multi-stream Transformer](https://mlsanity.com/paper/2103.08800/) (2021)
- [Network representation learning systematic review: ancestors and current development state](https://mlsanity.com/paper/2109.07583/) (2021)
- [Enriching Unsupervised User Embedding via Medical Concepts](https://mlsanity.com/paper/2203.10627/) (2022)
- [Integrated Convolutional and Recurrent Neural Networks for Health Risk Prediction using Patient Journey Data with Many Missing Values](https://mlsanity.com/paper/2211.06045/) (2022)

## Abstract (arXiv)

Representation learning (RL) plays an important role in extracting proper representations from complex medical data for various analyzing tasks, such as patient grouping, clinical endpoint prediction and medication recommendation. Medical data can be divided into two typical categories, outpatient and inpatient, that have different data characteristics. However, few of existing RL methods are specially designed for inpatients data, which have strong temporal relations and consistent diagnosis. In addition, for unordered medical activity set, existing medical RL methods utilize a simple pooling strategy, which would result in indistinguishable contributions among the activities for learning. In this work, weproposeInpatient2Vec, anovelmodel for learning three kinds of representations for inpatient, including medical activity, hospital day and diagnosis. A multi-layer self-attention mechanism with two training tasks is designed to capture the inpatient data characteristics and process the unordered set. Using a real-world dataset, we demonstrate that the proposed approach outperforms the competitive baselines on semantic similarity measurement and clinical events prediction tasks.