Exploratory application of machine learning methods on patient reported data in the development of supervised models for predicting outcomes.

ABSTRACT:

Background

Patient-reported outcome measurements (PROMs) are commonly used in clinical practice to support clinical decision making. However, few studies have investigated machine learning methods for predicting PROMs outcomes and thereby support clinical decision making.

Objective

This study investigates to what extent different machine learning methods, applied to two different PROMs datasets, can predict outcomes among patients with non-specific neck and/or low back pain.

Methods

Using two datasets consisting of PROMs from (1) care-seeking low back pain patients in primary care who participated in a randomized controlled trial, and (2) patients with neck and/or low back pain referred to multidisciplinary biopsychosocial rehabilitation, we present data science metho

SUBMITTER: Verma D 

PROVIDER: S-EPMC9434943 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature