Multi-task Prediction of Disease Onsets from Longitudinal Lab Tests

TL;DR

This study demonstrates that deep learning models trained on raw longitudinal lab test data can effectively predict the onset of multiple diseases, outperforming traditional feature-based methods in healthcare risk stratification.

Contribution

The paper introduces novel neural network architectures for multi-task disease prediction using raw lab test data, showing significant improvements over traditional methods.

Findings

Deep learning models outperform logistic regression baselines.

Representation learning captures complex health patterns.

Models predict 133 disease onsets from lab tests.

Abstract

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.