Drugs Resistance Analysis from Scarce Health Records via Multi-task Graph Representation

TL;DR

This paper introduces a graph-based multi-task learning framework that analyzes scarce health records to rapidly predict drug resistance, aiding clinical decisions without extensive lab testing.

Contribution

It proposes a novel graph representation of health records and a multi-task learning model for simultaneous drug resistance analysis, improving prediction accuracy on large datasets.

Findings

Achieved superior performance on over 110,000 patient records.

Enabled automated drug resistance predictions comparable to lab tests.

Demonstrated effective analysis with high-dimensional, scarce data.

Abstract

Clinicians prescribe antibiotics by looking at the patient's health record with an experienced eye. However, the therapy might be rendered futile if the patient has drug resistance. Determining drug resistance requires time-consuming laboratory-level testing while applying clinicians' heuristics in an automated way is difficult due to the categorical or binary medical events that constitute health records. In this paper, we propose a novel framework for rapid clinical intervention by viewing health records as graphs whose nodes are mapped from medical events and edges as correspondence between events in given a time window. A novel graph-based model is then proposed to extract informative features and yield automated drug resistance analysis from those high-dimensional and scarce graphs. The proposed method integrates multi-task learning into a common feature extracting graph encoder for simultaneous analyses of multiple drugs as well as stabilizing learning. On a massive dataset comprising over 110,000 patients with urinary tract infections, we verify the proposed method is capable of attaining superior performance on the drug resistance prediction problem. Furthermore, automated drug recommendations resemblant to laboratory-level testing can also be made based on the model resistance analysis.