大型语言模型测量精神病学功能的能力
The Capability of Large Language Models to Measure Psychiatric Functioning
AI总结：
本研究探讨了在医学知识上训练的大型语言模型（Med-PaLM 2）在未经专门训练时，从患者访谈和临床描述中预测精神病学功能的能力，发现其在抑郁分数预测等任务上表现接近人类临床评分者。
摘要：
本研究调查了在大型医学知识语料库上显式训练的大型语言模型（Med-PaLM 2）在未经专门训练的情况下，从患者访谈和临床描述中预测精神病学功能的能力。为此，使用提示词提取估计的临床分数和诊断，分析了n = 145份抑郁症、n = 115份PTSD评估以及n = 46份涵盖高患病率/高共病率障碍（抑郁障碍、焦虑障碍、精神病性障碍、创伤与应激障碍、成瘾障碍）的临床案例研究。结果表明，Med-PaLM 2能够评估一系列精神疾病状况下的精神病学功能，其中表现最佳的是基于标准化评估的抑郁分数预测（准确率范围 = 0.80 - 0.84），在统计学上与人类临床评分者无法区分，t(1,144) = 1.20；p = 0.23。结果展示了通用临床语言模型基于患者和临床医生对功能的自由描述来灵活预测精神病学风险的潜力。
英文摘要：
The current work investigates the capability of Large language models (LLMs) that are explicitly trained on large corpuses of medical knowledge (Med-PaLM 2) to predict psychiatric functioning from patient interviews and clinical descriptions without being trained to do so. To assess this, n = 145 depression and n =115 PTSD assessments and n = 46 clinical case studies across high prevalence/high comorbidity disorders (Depressive, Anxiety, Psychotic, trauma and stress, Addictive disorders) were analyzed using prompts to extract estimated clinical scores and diagnoses. Results demonstrate that Med-PaLM 2 is capable of assessing psychiatric functioning across a range of psychiatric conditions with the strongest performance being the prediction of depression scores based on standardized assessments (Accuracy range= 0.80 - 0.84) which were statistically indistinguishable from human clinical raters t(1,144) = 1.20; p = 0.23. Results show the potential for general clinical language models to flexibly predict psychiatric risk based on free descriptions of functioning from both patients and clinicians.
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