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Related Experiment Video

Updated: Oct 1, 2025

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

Lauri Lahti1

  • 1Department of Computer Science, Aalto University School of Science, Espoo, Finland. lauri.lahti@aalto.fi.

BMC Medical Research Methodology
|March 7, 2022
PubMed
Summary
This summary is machine-generated.

Self-rated health needs concerning COVID-19 vary significantly based on background factors like health and sex. Our new methodology links these differences to machine learning results for better health analytics.

Keywords:
Convolutional neural networkCoronavirusDecision makingDisabledInterpretationMachine learningPatientPersonalized careSelf-ratingThe need for help

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Area of Science:

  • Health analytics
  • Machine learning
  • Statistical analysis

Background:

  • Machine learning models for health analytics require understanding statistical properties of self-rated health statements.
  • Research analyzes COVID-19 related self-rated statements to link group differences to machine learning outcomes.

Purpose of the Study:

  • To analyze self-rated expression statements about the COVID-19 epidemic.
  • To develop and apply a new methodology for influence analysis in machine learning.
  • To link statistically significant differences in respondent groups to machine learning results.

Main Methods:

  • Quantitative cross-sectional study with 673 online respondents from Finnish organizations.
  • Collected "need for help" ratings on 20 health statements (11-point Likert scale) and background data (health, wellbeing, sex, age).
  • Proposed and tested a new machine learning influence analysis methodology.

Main Results:

  • Found significant Kendall rank-correlations and high cosine similarity between statement ratings and background questions.
  • Identified significant rating differences based on health condition, quality of life, and sex using Wilcoxon, Kruskal-Wallis, and ANOVA tests.
  • Demonstrated how statistically significant rating differences link to machine learning results.

Conclusions:

  • Self-rated "need for help" varies significantly with individual background information (health, quality of life, sex).
  • The new methodology connects significant rating differences to machine learning outcomes.
  • Enables development of improved machine learning for personalized patient care.