用可生成症状组合的规则，让复杂精神疾病诊断更易计算和比较。
Profile Generators: A Link between the Narrative and the Binary Matrix Representation
- 提出症状谱系生成器，将诊断描述转为可自动组合的症状规则。
- 能生成百万级症状组合，解决二进制矩阵过大的问题。
- 适合研究精神疾病分类、算法诊断或临床数据建模者使用。
精神健康障碍，尤其是由认知能力缺陷定义的认知障碍，在DSM-5中详细描述了其定义与症状表现。尽管已有简化且机器可读的表示方法用于评估障碍间的相似性与可区分性，但该方法不适用于最复杂的病例。由于症状组合数量庞大，生成或应用完整的二进制矩阵进行相似性计算不可行。本研究开发了一种新表示形式，将DSM-5的叙述性描述与二进制矩阵表示相连接，并实现症状组合的自动化生成。通过严格预定义的列表、集合与数值格式，即使涉及大量症状组合的复杂诊断路径也可被表达。该格式称为症状谱系生成器（简称生成器），在保持可读性与可扩展性的前提下，提供比二进制矩阵更全面的替代方案，并支持便捷生成症状组合（即谱系）。将多个精神病性障碍以生成器形式表示并生成全部症状组合后发现，传统矩阵表示对复杂障碍过于庞大而难以管理。现有的最大成对余弦相似度（MPCS）算法无法处理此类规模的矩阵，因此引入基于目标生成器操作的谱系压缩方法，以计算特定疾病的MPCS值。生成器使得大规模二进制表示的创建成为可能，并可通过条件生成器实现复杂障碍间特定MPCS的计算。
原文摘要 · Abstract (English)
Mental health disorders, particularly cognitive disorders defined by deficits in cognitive abilities, are described in detail in the DSM-5, which includes definitions and examples of signs and symptoms. A simplified, machine-actionable representation was developed to assess the similarity and separability of these disorders, but it is not suited for the most complex cases. Generating or applying a full binary matrix for similarity calculations is infeasible due to the vast number of symptom combinations. This research develops an alternative representation that links the narrative form of the DSM-5 with the binary matrix representation and enables automated generation of valid symptom combinations. Using a strict pre-defined format of lists, sets, and numbers with slight variations, complex diagnostic pathways involving numerous symptom combinations can be represented. This format, called the symptom profile generator (or simply generator), provides a readable, adaptable, and comprehensive alternative to a binary matrix while enabling easy generation of symptom combinations (profiles). Cognitive disorders, which typically involve multiple diagnostic criteria with several symptoms, can thus be expressed as lists of generators. Representing several psychotic disorders in generator form and generating all symptom combinations showed that matrix representations of complex disorders become too large to manage. The MPCS (maximum pairwise cosine similarity) algorithm cannot handle matrices of this size, prompting the development of a profile reduction method using targeted generator manipulation to find specific MPCS values between disorders. The generators allow easier creation of binary representations for large matrices and make it possible to calculate specific MPCS cases between complex disorders through conditional generators.
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