RHMCD-20数据集:使用机器学习在隔离生活期间识别人类快速心理健康抑郁症
Nazrul Amin1, Imrus Salehin2,3, Md Abu Baten1
1Department of Computer Science and Engineering, Northern University Bangladesh, Bangladesh.
Data in brief
|April 16, 2024
概括
RHMCD-20数据集详细介绍了孟加拉国在隔离期间的心理健康情况,揭示了影响压力的关键因素. 这些数据有助于开发更好的心理医疗干预预案的预测模型.
科学领域:
- 公共卫生 公共卫生
- 心理学 心理学 心理学
- 数据科学数据科学数据科学
背景情况:
- 隔离措施对全球人口的心理健康产生了重大影响.
- 有限的数据存在于孟加拉国在隔离期间的特定心理健康动态.
- 了解这些动态对于有针对性的公共卫生干预至关重要.
研究的目的:
- 介绍和描述RHMCD-20数据集,这是孟加拉国隔离期间心理健康的综合资源.
- 突出数据集对预测建模,社会影响分析和代际心理健康研究的有用性.
- 促进在医疗保健中开发机器学习应用程序.
主要方法:
- 在孟加拉国的不同人口群体中进行了结构化调查.
- 数据收集包括年龄,性别,职业和压力等变量.
- 数据集的策划是为了支持相关性分析和预测建模.
主要成果:
- RHMCD-20数据集提供了丰富的,多变量的洞察力,对隔离相关的心理健康.
- 它可以识别影响压力和心理健康的因素.
- 这些数据有助于理解隔离对心理健康的社会和工作场所影响.
结论:
- 孟加拉国RHMCD-20数据集是孟加拉国研究人员和政策制定者的宝贵资源.
- 它为基于证据的心理健康干预和政策制定提供了细微的见解.
- 该数据集支持精确精神卫生保健和动态预测模型的进步.
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