患者对治疗反应的异质性在精神疾病中普遍存在。个性化的医学方法-涉及将患者分为更适合特定治疗的亚组-因此可以改善患者的预后,并作为临床试验中患者选择的有力工具。机器学习方法可以识别患者亚群,但由于使用复杂的算法而无法反映临床医生的自然决策过程,因此通常无法“解释”。
在这里,我们结合了两种分析方法-个性化优势指数和贝叶斯规则列表-以强调模型可解释性的方式识别帕潘立酮指示的精神分裂症患者。我们将这些方法回顾性地应用于随机,安慰剂对照临床试验数据,以确定帕利哌酮指示的精神分裂症患者亚组,这些患者表现出比Cohen'sd评估的完整随机样本更大的治疗效果(治疗结果优于安慰剂)。对于这项研究,结果对应于测量阳性和阴性综合征量表(PANSS)总分的降低(例如,幻觉,妄想),负(例如,钝的影响,情绪退缩),和一般精神病理学(例如,意志的干扰,不合作)精神分裂症的症状。
使用我们的联合可解释的AI方法来确定一个对帕潘立酮比安慰剂更敏感的亚组,与全样本相比,治疗效果显著增加(单样本t检验p<0.0001,比较全样本Cohen'sd=0.82和产生的亚组Cohen'sd's分布,平均d=1.22,stddd=0.09)。此外,我们的建模方法产生简单的逻辑语句(if-then-else),称为“规则列表”,便于临床医生的解释性。交叉验证生成的大多数规则列表发现了两个一般的精神病理学症状,意志和不合作的干扰,预测帕利哌酮指示的亚组中的成员资格。
这些结果有助于通过确定具有改善治疗效果的亚组,从技术上验证我们的可解释的AI方法来选择患者进行临床试验。有了这些数据,可解释的规则列表还表明,帕利哌酮可能为精神分裂症患者的治疗提供改善的治疗益处,精神分裂症患者的症状是意志高度紊乱或高度不合作。
clincialtrials.gov标识符:NCT00,083,668;预期注册于2004年5月28日。
Heterogeneity among patients\' responses to treatment is prevalent in psychiatric disorders. Personalized medicine approaches-which involve parsing patients into subgroups better indicated for a particular treatment-could therefore improve patient outcomes and serve as a powerful tool in patient selection within clinical trials. Machine learning approaches can identify patient subgroups but are often not \"explainable\" due to the use of complex algorithms that do not mirror clinicians\' natural decision-making processes.
Here we combine two analytical approaches-Personalized Advantage Index and Bayesian Rule Lists-to identify paliperidone-indicated schizophrenia patients in a way that emphasizes model
explainability. We apply these approaches retrospectively to randomized, placebo-controlled clinical
trial data to identify a paliperidone-indicated subgroup of schizophrenia patients who demonstrate a larger treatment effect (outcome on treatment superior than on placebo) than that of the full randomized sample as assessed with Cohen\'s d. For this
study, the outcome corresponded to a reduction in the Positive and Negative Syndrome Scale (PANSS) total score which measures positive (e.g., hallucinations, delusions), negative (e.g., blunted affect, emotional withdrawal), and general psychopathological (e.g., disturbance of volition, uncooperativeness) symptoms in schizophrenia.
Using our combined explainable AI approach to identify a subgroup more responsive to paliperidone than placebo, the treatment effect increased significantly over that of the full sample (p < 0.0001 for a one-sample t-test comparing the full sample Cohen\'s d = 0.82 and a generated distribution of subgroup Cohen\'s d\'s with mean d = 1.22, std d = 0.09). In addition, our modeling approach produces simple logical statements (if-then-else), termed a \"rule list\", to ease interpretability for clinicians. A majority of the rule lists generated from cross-validation found two general psychopathology symptoms, disturbance of volition and uncooperativeness, to predict membership in the paliperidone-indicated subgroup.
These results help to technically validate our explainable AI approach to patient selection for a clinical
trial by identifying a subgroup with an improved treatment effect. With these data, the explainable rule lists also suggest that paliperidone may provide an improved therapeutic benefit for the treatment of schizophrenia patients with either of the symptoms of high disturbance of volition or high uncooperativeness.
clincialtrials.gov identifier: NCT 00,083,668; prospectively registered May 28, 2004.