A clinically-guided unsupervised clustering approach to recommend symptoms of disease associated with diagnostic opportunities.

ABSTRACT:

Objectives

A first step in studying diagnostic delays is to select the signs, symptoms and alternative diseases that represent missed diagnostic opportunities. Because this step is labor intensive requiring exhaustive literature reviews, we developed machine learning approaches to mine administrative data sources and recommend conditions for consideration. We propose a methodological approach to find diagnostic codes that exhibit known patterns of diagnostic delays and apply this to the diseases of tuberculosis and appendicitis.

Methods

We used the IBM MarketScan Research Databases, and consider the initial symptoms of cough before tuberculosis and abdominal pain before appendicitis. We analyze diagnosis codes during healthcare visits before the index diagnosis, and use k-me

SUBMITTER: Miller AC 

PROVIDER: S-EPMC9934811 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature