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.
翻译:为支持健康分析而开发机器学习模型,需要进一步了解自我评级表达语句的统计属性。我们分析了关于冠状病毒COVID-19流行病的自我评级表达语句,以查明各答复群体之间在统计上的重大差异,并发现病人需要帮助进行机器学习。我们定量研究收集了20份与健康有关的表达语句的“需要帮助”评级,涉及11点水平的冠状病毒流行,以及9份有关个人健康和福祉、性别和年龄的回答。2020年5月30日至8月3日,芬兰病人和残疾人组织、其他健康相关质量组织和专业人士以及教育机构(n=673)。我们从芬兰病人和残疾人组织、其他健康相关质量组织和专业人士以及教育机构(n=673)。我们分析了肯德尔级和患者对机器学习的帮助程度差异和依赖性。我们发现,在与健康相关的统计学排名中,对各种健康等级、Kruskal-Wallis和单向差异分析(ANOVA)进行了机算学习实验,并基本实施了神经网络算算算法的机算支持。我们发现,在各种健康评级的排序和高额评级中,我们为各种健康相关评级的排序和高比例的对比分析中,我们为各种健康评级的排序的对比和高比值进行了重要的对比。我们为:我们为各种健康评级的排序的对比,我们为各种健康评级的排序的对比和高估变变变变变变。