An Electronic Medical Record-Based Discharge Disposition Tool Gets Bundle Busted: Decaying Relevance of Clinical Data Accuracy in Machine Learning.

ABSTRACT:

Background

Determining discharge disposition after total joint arthroplasty (TJA) has been a challenge. Advances in machine learning (ML) have produced computer models that learn by example to generate predictions on future events. We hypothesized a trained ML algorithm's diagnostic accuracy will be better than that of current predictive tools to predict discharge disposition after primary TJA.

Methods

This study was a retrospective cohort study from a single, tertiary referral center for primary TJA. We trained and validated an artificial neural network (ANN) based on 4368 distinct surgical encounters between 1/1/2013 and 6/28/2016. The ANN's ability to identify discharge disposition was then tested on 1452 distinct surgical encounters between 1/3/17 and 11/30/17.

Result

SUBMITTER: Greenstein AS 

PROVIDER: S-EPMC7567055 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature