Detecting the patient's need for help with machine learning based on expressions.

ABSTRACT:

Background

Developing machine learning models to support health analytics requires increased understanding about statistical properties of self-rated expression statements used in health-related communication and decision making. To address this, our current research analyzes self-rated expression statements concerning the coronavirus COVID-19 epidemic and with a new methodology identifies how statistically significant differences between groups of respondents can be linked to machine learning results.

Methods

A quantitative cross-sectional study gathering the "need for help" ratings for twenty health-related expression statements concerning the coronavirus epidemic on an 11-point Likert scale, and nine answers about the person's health and wellbeing, sex and age. The study

SUBMITTER: Lahti L 

PROVIDER: S-EPMC8898191 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature