AIMC Topic: Antipsychotic Agents

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Predicting antipsychotic responsiveness using a machine learning classifier trained on plasma levels of inflammatory markers in schizophrenia.

Translational psychiatry
We apply machine learning techniques to navigate the multifaceted landscape of schizophrenia. Our method entails the development of predictive models, emphasizing peripheral inflammatory biomarkers, which are classified into treatment response subgro...

Machine learning prediction model of the treatment response in schizophrenia reveals the importance of metabolic and subjective characteristics.

Schizophrenia research
Predicting early treatment response in schizophrenia is pivotal for selecting the best therapeutic approach. Utilizing machine learning (ML) technique, we aimed to formulate a model predicting antipsychotic treatment outcomes. Data were obtained from...

AI-based medication adherence prediction in patients with schizophrenia and attenuated psychotic disorders.

Schizophrenia research
OBJECTIVE: The capacity of machine-learning algorithms to predict medication adherence was assessed using data from AiCure, a computer vision-assisted smartphone application, which records the medication ingestion event.

Predicting maintenance lithium response for bipolar disorder from electronic health records-a retrospective study.

PeerJ
BACKGROUND: Optimising maintenance drug treatment selection for people with bipolar disorder is challenging. There is some evidence that clinical and demographic features may predict response to lithium. However, attempts to personalise treatment cho...

Predicting remission after acute phase pharmacotherapy in patients with bipolar I depression: A machine learning approach with cross-trial and cross-drug replication.

Bipolar disorders
OBJECTIVES: Interpatient variability in bipolar I depression (BP-D) symptoms challenges the ability to predict pharmacotherapeutic outcomes. A machine learning workflow was developed to predict remission after 8 weeks of pharmacotherapy (total score ...

Unlocking treatment success: predicting atypical antipsychotic continuation in youth with mania.

BMC medical informatics and decision making
PURPOSE: This study aimed to create and validate robust machine-learning-based prediction models for antipsychotic drug (risperidone) continuation in children and teenagers suffering from mania over one year and to discover potential variables for cl...

Predicting treatment resistance in schizophrenia patients: Machine learning highlights the role of early pathophysiologic features.

Schizophrenia research
Detecting patients with a high-risk profile for treatment-resistant schizophrenia (TRS) can be beneficial for implementing individually adapted therapeutic strategies and better understanding the TRS etiology. The aim of this study was to explore, wi...

Illusory generalizability of clinical prediction models.

Science (New York, N.Y.)
It is widely hoped that statistical models can improve decision-making related to medical treatments. Because of the cost and scarcity of medical outcomes data, this hope is typically based on investigators observing a model's success in one or two d...

Effectiveness and safety of blonanserin for improving social and cognitive functions in patients with first-episode schizophrenia: a study protocol for a prospective, multicentre, single-arm clinical trial.

BMJ open
INTRODUCTION: Both the pharmacological characteristics of blonanserin and its related small sample size studies suggest that blonanserin could alleviate social and cognitive dysfunctions in patients with schizophrenia. However, no large sample size s...

Proteome-Informed Machine Learning Studies of Cocaine Addiction.

The journal of physical chemistry letters
No anti-cocaine addiction drugs have been approved by the Food and Drug Administration despite decades of effort. The main challenge is the intricate molecular mechanisms of cocaine addiction, involving synergistic interactions among proteins upstrea...