Semi-orthogonal Non-negative Matrix Factorization with an Application in Text Mining

TL;DR

This paper introduces semi-orthogonal non-negative matrix factorization (SONMF), a novel method for bi-clustering high-dimensional, noisy text data in medical applications, improving interpretability and classification accuracy.

Contribution

The paper proposes SONMF, a new matrix factorization technique that enhances interpretability and classification performance on complex text data in healthcare.

Findings

SONMF outperforms existing NMF methods in factorization accuracy

The method increases classification accuracy over traditional models

SONMF demonstrates faster convergence and better orthogonality in experiments

Abstract

Emergency Department (ED) crowding is a worldwide issue that affects the efficiency of hospital management and the quality of patient care. This occurs when the request for an admit ward-bed to receive a patient is delayed until an admission decision is made by a doctor. To reduce the overcrowding and waiting time of ED, we build a classifier to predict the disposition of patients using manually-typed nurse notes collected during triage, thereby allowing hospital staff to begin necessary preparation beforehand. However, these triage notes involve high dimensional, noisy, and also sparse text data which makes model fitting and interpretation difficult. To address this issue, we propose the semi-orthogonal non-negative matrix factorization (SONMF) for both continuous and binary design matrices to first bi-cluster the patients and words into a reduced number of topics. The subjects can then be interpreted as a non-subtractive linear combination of orthogonal basis topic vectors. These generated topic vectors provide the hospital with a direct understanding of the cause of admission. We show that by using a transformation of basis, the classification accuracy can be further increased compared to the conventional bag-of-words model and alternative matrix factorization approaches. Through simulated data experiments, we also demonstrate that the proposed method outperforms other non-negative matrix factorization (NMF) methods in terms of factorization accuracy, rate of convergence, and degree of orthogonality.