Categorical data analysis is relevant to suicide risk and prevention research that focuses on discrete outcomes (e.g., suicide attempt status). Unfortunately, results from these analyses are often misinterpreted and not presented in a clinically tangible manner. We aimed to address these issues and highlight the relevance and utility of categorical methods in suicide research and clinical assessment. Additionally, we introduce relevant basic machine learning methods concepts and address the distinct utility of the current methods.

中文翻译：

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在自杀研究中使用分类数据分析：考虑临床实用性和实用性

分类数据分析与侧重于离散结果（例如，自杀未遂状态）的自杀风险和预防研究相关。不幸的是，这些分析的结果经常被误解，并且没有以临床有形的方式呈现。我们旨在解决这些问题，并强调分类方法在自杀研究和临床评估中的相关性和实用性。此外，我们介绍了相关的基本机器学习方法概念，并解决了当前方法的独特效用。