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. Trial Registration: clincialtrials.gov identifier: NCT 00,083,668; prospectively registered May 28, 2004

中文翻译：

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可解释的 AI 使临床试验患者选择能够回顾性地改善精神分裂症的治疗效果

患者对治疗反应的异质性在精神疾病中普遍存在。个性化医疗方法——包括将患者分解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组，但由于使用不反映临床医生自然决策过程的复杂算法，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利哌酮指示的精神分裂症患者。我们回顾性地将这些方法应用于随机、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，这些患者表现出比用 Cohen's d 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，结果对应于阳性和阴性综合症状量表 (PANSS) 总分的降低，该量表衡量阳性（例如，幻觉、妄想）、阴性（例如，迟钝的情感、情绪退缩）和一般精神病理学（例如，意志障碍，不合作）精神分裂症的症状。使用我们的组合可解释 AI 方法来识别对帕利哌酮比安慰剂更敏感的亚组，治疗效果显着高于整个样本（p < 0. 0001 用于单样本 t 检验，比较完整样本 Cohen 的 d = 0.82 和生成的子组 Cohen d 的分布，均值 d = 1.22，标准 d = 0.09）。此外，我们的建模方法生成简单的逻辑语句（if-then-else），称为“规则列表”，以简化临床医生的解释性。交叉验证生成的大多数规则列表发现了两种一般的精神病理学症状，即意志障碍和不合作，以预测帕利哌酮指示亚组的成员资格。这些结果通过识别具有改善治疗效果的亚组，有助于在技术上验证我们为临床试验选择患者的可解释 AI 方法。有了这些数据，可解释的规则列表还表明，帕利哌酮可以为具有高度意志障碍或高度不合作症状的精神分裂症患者的治疗提供改善的治疗益处。试验注册：clincialtrials.gov 标识符：NCT 00,083,668；预期注册 2004 年 5 月 28 日