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

Funding Information: The author wants to express gratitude to all the people who have kindly participated in answering to the online questionnaire of the research. Special thanks to the people associated with various Finnish patient and disabled people?s organizations, other health and wellness organizations, and educational institutions as well as organizations of healthcare professionals, including also the representatives of Finnish Association for Emergency Medicine. The author thanks warmly Marko Nieminen at Department of Computer Science at Aalto University School of Science and Ilmo Keskim?ki and Tiina Laatikainen at Finnish Institute for Health and Welfare (THL) for valuable discussions and advice related to the research, including giving support for the author?s research initiative and its ethical approval, as well as giving comments about the research plan, methods and results created by the author. Thank you also for the anonymous reviewers for their valuable comments that enabled to enhance the manuscript. Publisher Copyright: © 2022, The Author(s).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 involved online respondents between 30 May and 3 August 2020 recruited from Finnish patient and disabled people’s organizations, other health-related organizations and professionals, and educational institutions (n = 673). We propose andexperimentally motivate a new methodology of influence analysis concerning machine learning to be applied for evaluating how machine learning results depend on and are influenced by various properties of the data which are identified with traditional statistical methods. Results: We found statistically significant Kendall rank-correlations and high cosine similarity values between various health-related expression statement pairs concerning the “need for help” ratings and a background question pair. With tests of Wilcoxon rank-sum, Kruskal-Wallis and one-way analysis of variance (ANOVA) between groups we 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 new methodology enabled us to identify how statistically significant rating differences were linked to machine learning results thus helping to develop better human-understandable machine learning models. Conclusions: The self-rated “need for help” concerning health-related expression statements differs statistically significantly depending on the person’s background information, such as his/her estimated health condition, quality of life and sex. With our new methodology statistically significant rating differences can be linked to machine learning results thus enabling to develop better machine learning to identify, interpret and address the patient’s needs for well-personalized care.Peer reviewe