# Multi-task Prediction of Disease Onsets from Longitudinal Lab Tests

arXiv: https://arxiv.org/abs/1608.00647 · HTML: https://mlsanity.com/paper/1608.00647/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2016.

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

- [Time Series Prediction using Deep Learning Methods in Healthcare](https://mlsanity.com/paper/2108.13461/) (2021)
- [EHRMamba: Towards Generalizable and Scalable Foundation Models for Electronic Health Records](https://mlsanity.com/paper/2405.14567/) (2024)
- [Temporal-spatial Correlation Attention Network for Clinical Data Analysis in Intensive Care Unit](https://mlsanity.com/paper/2306.01970/) (2023)
- [Unveiling the frontiers of deep learning: innovations shaping diverse domains](https://mlsanity.com/paper/2309.02712/) (2023)

## Abstract (arXiv)

Disparate areas of machine learning have benefited from models that can take raw data with little preprocessing as input and learn rich representations of that raw data in order to perform well on a given prediction task. We evaluate this approach in healthcare by using longitudinal measurements of lab tests, one of the more raw signals of a patient's health state widely available in clinical data, to predict disease onsets. In particular, we train a Long Short-Term Memory (LSTM) recurrent neural network and two novel convolutional neural networks for multi-task prediction of disease onset for 133 conditions based on 18 common lab tests measured over time in a cohort of 298K patients derived from 8 years of administrative claims data. We compare the neural networks to a logistic regression with several hand-engineered, clinically relevant features. We find that the representation-based learning approaches significantly outperform this baseline. We believe that our work suggests a new avenue for patient risk stratification based solely on lab results.