Confidence-based laboratory test reduction recommendation algorithm.

ABSTRACT:

Background

We propose a new deep learning model to identify unnecessary hemoglobin (Hgb) tests for patients admitted to the hospital, which can help reduce health risks and healthcare costs.

Methods

We collected internal patient data from a teaching hospital in Houston and external patient data from the MIMIC III database. The study used a conservative definition of unnecessary laboratory tests, which was defined as stable (i.e., stability) and below the lower normal bound (i.e., normality). Considering that machine learning models may yield less reliable results when trained on noisy inputs containing low-quality information, we estimated prediction confidence to assess the reliability of predicted outcomes. We adopted a "select and predict" design philosophy to maximize pr

SUBMITTER: Huang T 

PROVIDER: S-EPMC10173656 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature