AIMC Topic: Antipsychotic Agents

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Application of machine learning to predict reduction in total PANSS score and enrich enrollment in schizophrenia clinical trials.

Clinical and translational science
Clinical trial efficiency, defined as facilitating patient enrollment, and reducing the time to reach safety and efficacy decision points, is a critical driving factor for making improvements in therapeutic development. The present work evaluated a m...

Pattern classification as decision support tool in antipsychotic treatment algorithms.

Experimental neurology
Pattern classification aims to establish a new approach in personalized treatment. The scope is to tailor treatment on individual characteristics during all phases of care including prevention, diagnosis, treatment, and clinical outcome. In psychotic...

Predicting self-harm within six months after initial presentation to youth mental health services: A machine learning study.

PloS one
BACKGROUND: A priority for health services is to reduce self-harm in young people. Predicting self-harm is challenging due to their rarity and complexity, however this does not preclude the utility of prediction models to improve decision-making rega...

Characterization of specific and distinct patient types in clinical trials of acute schizophrenia using an uncorrelated PANSS score matrix transform (UPSM).

Psychiatry research
Understanding the specificity of symptom change in schizophrenia can facilitate the evaluation antipsychotic efficacy for different symptom domains. Previous work identified a transform of PANSS using an uncorrelated PANSS score matrix (UPSM) to redu...

Predicting individual improvement in schizophrenia symptom severity at 1-year follow-up: Comparison of connectomic, structural, and clinical predictors.

Human brain mapping
In a machine learning setting, this study aims to compare the prognostic utility of connectomic, brain structural, and clinical/demographic predictors of individual change in symptom severity in individuals with schizophrenia. Symptom severity at bas...

What do patients learn about psychotropic medications on the web? A natural language processing study.

Journal of affective disorders
BACKGROUND: Low rates of medication adherence remain a major challenge across psychiatry. In part, this likely reflects patient concerns about safety and adverse effects, accurate or otherwise. We therefore sought to characterize online information a...

Predicting hospital-acquired pneumonia among schizophrenic patients: a machine learning approach.

BMC medical informatics and decision making
BACKGROUND: Medications are frequently used for treating schizophrenia, however, anti-psychotic drug use is known to lead to cases of pneumonia. The purpose of our study is to build a model for predicting hospital-acquired pneumonia among schizophren...

Disease comorbidity-guided drug repositioning: a case study in schizophrenia.

AMIA ... Annual Symposium proceedings. AMIA Symposium
UNLABELLED: The key to any computational drug repositioning is the availability of relevant data in machine-understandable format. While large amount of genetic, genomic and chemical data are publicly available, large-scale higher-level disease and d...

Identification of novel immune-relevant drug target genes for Alzheimer's Disease by combining ontology inference with network analysis.

CNS neuroscience & therapeutics
AIMS: Alzheimer's disease (AD) is one of the leading causes of death in elderly people. Its pathogenesis is greatly associated with the abnormality of immune system. However, only a few immune-relevant AD drug target genes have been discovered up to ...