Diagnosing cognitive (mental health) disorders is a delicate and complex task. Identifying the next most informative symptoms to assess, in order to distinguish between possible disorders, presents an additional challenge. This process requires comprehensive knowledge of diagnostic criteria and symptom overlap across disorders, making it difficult to navigate based on symptoms alone. This research aims to develop a recommender system for cognitive disorder diagnosis using binary matrix representations. The core algorithm utilizes a binary matrix of disorders and their symptom combinations. It filters through the rows and columns based on the patient's current symptoms to identify potential disorders and recommend the most informative next symptoms to examine. A prototype of the recommender system was implemented in Python. Using synthetic test and some real-life data, the system successfully identified plausible disorders from an initial symptom set and recommended further symptoms to refine the diagnosis. It also provided additional context on the symptom-disorder relationships. Although this is a prototype, the recommender system shows potential as a clinical support tool. A fully-developed application of this recommender system may assist mental health professionals in identifying relevant disorders more efficiently and guiding symptom-specific follow-up investigations to improve diagnostic accuracy.

翻译：诊断认知（心理健康）障碍是一项精细且复杂的任务。为区分可能的障碍，确定下一步需评估的最具信息性的症状，带来了额外的挑战。该过程需要全面了解诊断标准及症状在不同障碍间的重叠性，仅凭症状难以有效推进。本研究旨在开发一种基于二元矩阵表示的认知障碍诊断推荐系统。核心算法利用障碍及其症状组合的二元矩阵，根据患者当前症状对行与列进行筛选，以识别潜在障碍并推荐下一步需检查的最具信息性的症状。该推荐系统的原型采用Python实现。通过使用合成测试数据及部分真实数据，系统成功从初始症状集中识别出合理的障碍，并推荐了进一步细化诊断的症状。同时，系统还提供了症状-障碍关系的额外背景信息。尽管仅为原型，该推荐系统展现了作为临床辅助工具的潜力。充分开发后的应用或可帮助心理健康专业人员更高效地识别相关障碍，并引导针对特定症状的后续调查，从而提高诊断准确性。