  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.
