Abstract
BackgroundHeterogeneity 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.MethodsHere 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.ResultsUsing 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.ConclusionsThese 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
Highlights
Heterogeneity among patients’ responses to treatment is prevalent in psychiatric disorders
We used an approach inspired by explainable artificial intelligence (XAI), the Bayesian Rule Lists algorithm (BRL) [6, 7], to both help identify the most predictive variables and explain those predictions of treatment-indicated patients from Personalized Advantage Index (PAI) with simple if–-else statements that better mirror a clinician’s decision-making process by using Boolean criteria with clear cutoffs for predictor variables
The data requirements of the PAI and BRL approach could be satisfied in these scenarios. We present this combined PAI and BRL approach to patient selection for clinical trials using Explainable Artificial Intelligence (XAI) and validation in schizophrenia patients through a retrospective analysis of a clinical trial
Summary
Heterogeneity among patients’ responses to treatment is prevalent in psychiatric disorders. A method termed Personalized Advantage Index (PAI) has been recently developed to uncover subgroups of patients, termed “treatment-indicated,” who may be more responsive to a particular treatment than placebo suggesting that predictive modeling could lead to personalized medicine approaches for subtyping treatmentindicated patients [4]. This could help improve patient selection for clinical trials of that medication to enrich for patients most likely to show a treatment effect. It has yet to be tested in other psychiatric populations
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