Modular Clinical Decision Support Networks (MoDN) -- Updatable, Interpretable, and Portable Predictions for Evolving Clinical Environments

TL;DR

MoDN is a modular, interpretable, and privacy-preserving clinical decision support system that enables collaborative learning across datasets with non-overlapping features, providing dynamic, personalized predictions during patient consultations.

Contribution

Introduces MoDN, a novel modular neural network architecture that allows flexible, privacy-preserving, and interpretable collaborative learning across datasets with imperfect interoperability.

Findings

Enables dynamic, personalized diagnosis predictions during consultations.

Supports collaborative learning without sharing raw data.

Provides interpretable feedback to clinicians.

Abstract

Data-driven Clinical Decision Support Systems (CDSS) have the potential to improve and standardise care with personalised probabilistic guidance. However, the size of data required necessitates collaborative learning from analogous CDSS's, which are often unsharable or imperfectly interoperable (IIO), meaning their feature sets are not perfectly overlapping. We propose Modular Clinical Decision Support Networks (MoDN) which allow flexible, privacy-preserving learning across IIO datasets, while providing interpretable, continuous predictive feedback to the clinician. MoDN is a novel decision tree composed of feature-specific neural network modules. It creates dynamic personalised representations of patients, and can make multiple predictions of diagnoses, updatable at each step of a consultation. The modular design allows it to compartmentalise training updates to specific features and collaboratively learn between IIO datasets without sharing any data.