A Recommender System Based on Binary Matrix Representations for Cognitive Disorders

TL;DR

This paper presents a binary matrix-based recommender system designed to assist in diagnosing cognitive disorders by suggesting the most informative symptoms to evaluate, thereby aiding mental health professionals in improving diagnostic accuracy.

Contribution

It introduces a novel binary matrix approach for symptom-based disorder recommendation and demonstrates its potential as a clinical support tool.

Findings

Successfully identified plausible disorders from initial symptoms

Recommended relevant follow-up symptoms for refined diagnosis

Provided context on symptom-disorder relationships

Abstract

Diagnosing cognitive (mental health) disorders is a delicate and complex task. Identifying the next most informative symptoms to assess, in order to distinguish between possible disorders, presents an additional challenge. This process requires comprehensive knowledge of diagnostic criteria and symptom overlap across disorders, making it difficult to navigate based on symptoms alone. This research aims to develop a recommender system for cognitive disorder diagnosis using binary matrix representations. The core algorithm utilizes a binary matrix of disorders and their symptom combinations. It filters through the rows and columns based on the patient's current symptoms to identify potential disorders and recommend the most informative next symptoms to examine. A prototype of the recommender system was implemented in Python. Using synthetic test and some real-life data, the system successfully identified plausible disorders from an initial symptom set and recommended further symptoms to refine the diagnosis. It also provided additional context on the symptom-disorder relationships. Although this is a prototype, the recommender system shows potential as a clinical support tool. A fully-developed application of this recommender system may assist mental health professionals in identifying relevant disorders more efficiently and guiding symptom-specific follow-up investigations to improve diagnostic accuracy.