Detecting the patient's need for help with machine learning

• URL: http://arxiv.org/abs/2012.13626v2
• Date: Wed, 24 Mar 2021 17:50:47 GMT
• Title: Detecting the patient's need for help with machine learning
• Abstract summary: We analyzed self-rated expression statements concerning the coronavirus COVID-19 epidemic. We found statistically significant correlations and high cosine similarity values between various health-related expression statement pairs. Our experiments with a convolutional neural network algorithm showed the applicability of machine learning to support detecting the need for help in the patient's expressions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Developing machine learning models to support health analytics requires increased understanding about statistical properties of self-rated expression statements. We analyzed self-rated expression statements concerning the coronavirus COVID-19 epidemic to identify statistically significant differences between groups of respondents and to detect the patient's need for help with machine learning. Our quantitative study gathered the "need for help" ratings for twenty health-related expression statements concerning the coronavirus epidemic on a 11-point Likert scale, and nine answers about the person's health and wellbeing, sex and age. Online respondents between 30 May and 3 August 2020 were recruited from Finnish patient and disabled people's organizations, other health-related organizations and professionals, and educational institutions (n=673). We analyzed rating differences and dependencies with Kendall rank-correlation and cosine similarity measures and tests of Wilcoxon rank-sum, Kruskal-Wallis and one-way analysis of variance (ANOVA) between groups, and carried out machine learning experiments with a basic implementation of a convolutional neural network algorithm. We found statistically significant correlations and high cosine similarity values between various health-related expression statement pairs concerning the "need for help" ratings and a background question pair. We also identified statistically significant rating differences for several health-related expression statements in respect to groupings based on the answer values of background questions, such as the ratings of suspecting to have the coronavirus infection and having it depending on the estimated health condition, quality of life and sex. Our experiments with a convolutional neural network algorithm showed the applicability of machine learning to support detecting the need for help in the patient's expressions.

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