Treatment-Resistant Schizophrenia: Insights From Genetic Studies and Machine Learning Approaches

Pisanu, Claudia; Squassina, Alessio

doi:10.3389/fphar.2019.00617

Cited by 22 publications

(11 citation statements)

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“…Evidence for polygenic risk scores versus support vector machines for the prediction of treatment‐resistant schizophrenia from GWAS data have been reviewed 135 . Although support vector machines might be more suitable to take into account complex genetic interactions, the traditional polygenic risk score approach showed higher accuracy for classifying treatment‐resistant individuals 115 .…”

Section: The Use Of Data From Genetics Electrophysiology Neuroimaging and Cognitive Testingmentioning

confidence: 99%

The promise of machine learning in predicting treatment outcomes in psychiatry

Bondar²,

et al. 2021

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For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory power of a predictive model when tested on new, unseen datasets. In this paper, we review key studies using machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments. Next, we focus on some new sources of data that are being used for the development of predictive models based on machine learning, such as electronic health records, smartphone and social media data, and on the potential utility of data from genetics, electrophysiology, neuroimaging and cognitive testing. Finally, we discuss how far the field has come towards implementing prediction tools in real‐world clinical practice. Relatively few retrospective studies to‐date include appropriate external validation procedures, and there are even fewer prospective studies testing the clinical feasibility and effectiveness of predictive models. Applications of machine learning in psychiatry face some of the same ethical challenges posed by these techniques in other areas of medicine or computer science, which we discuss here. In short, machine learning is a nascent but important approach to improve the effectiveness of mental health care, and several prospective clinical studies suggest that it may be working already.

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Section: The Use Of Data From Genetics Electrophysiology Neuroimaging and Cognitive Testingmentioning

confidence: 99%

The promise of machine learning in predicting treatment outcomes in psychiatry

Bondar²,

et al. 2021

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show abstract

“…Rather than focusing on response to single antipsychotics, most studies focused on predicting treatment-resistant schizophrenia. 48 A recent study from Vivian-Griffiths et al (2019) used an SVM model to compare its predictive performance to that of a polygenic risk score (PRS) prediction in discriminating patients with treatment-resistant schizophrenia from healthy controls. 49 The study was conducted in the CLOZUK sample, including 5554 patients with treatment-resistant schizophrenia and 6299 healthy controls 50 and the PRS included 4998 SNPs from the Psychiatric Genomics Consortium (PGC) wave 2 GWAS meta-analysis.…”

Section: Biological Predictive Models In Precision Psychiatrymentioning

confidence: 99%

<p>Challenges and Future Prospects of Precision Medicine in Psychiatry</p>

Manchia¹,

Pisanu²,

Squassina³

et al. 2020

PGPM

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Precision medicine is increasingly recognized as a promising approach to improve disease treatment, taking into consideration the individual clinical and biological characteristics shared by specific subgroups of patients. In specific fields such as oncology and hematology, precision medicine has already started to be implemented in the clinical setting and molecular testing is routinely used to select treatments with higher efficacy and reduced adverse effects. The application of precision medicine in psychiatry is still in its early phases. However, there are already examples of predictive models based on clinical data or combinations of clinical, neuroimaging and biological data. While the power of single clinical predictors would remain inadequate if analyzed only with traditional statistical approaches, these predictors are now increasingly used to impute machine learning models that can have adequate accuracy even in the presence of relatively small sample size. These models have started to be applied to disentangle relevant clinical questions that could lead to a more effective management of psychiatric disorders, such as prediction of response to the mood stabilizer lithium, resistance to antidepressants in major depressive disorder or stratification of the risk and outcome prediction in schizophrenia. In this narrative review, we summarized the most important findings in precision medicine in psychiatry based on studies that constructed machine learning models using clinical, neuroimaging and/or biological data. Limitations and barriers to the implementation of precision psychiatry in the clinical setting, as well as possible solutions and future perspectives, will be presented.

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“…The multi-omics data in these selected studies included SNPs datasets, DNA methylation datasets, gene expression datasets, and phenotypic datasets (such as demographic and clinical datasets). In addition, the reader can refer to a recent review by Pisanu and Squassina [19] for treatment-resistant schizophrenia, where patients with treatment-resistant schizophrenia are defined as those revealing little or no response to at least two non-clozapine antipsychotic medications. The reader can also refer to a recent review by Perlman et al [20] for predictors of antidepressant treatment response in MDD, where predictor categories include demographic factors, symptom profiles (such as age of onset), peripheral markers (accessible through urine, blood, or saliva), genetic biomarkers, and neuroimaging data.…”

Section: Applications In Treatment Predictionmentioning

confidence: 99%

Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches

Lin

Lane

2020

IJMS

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A growing body of evidence now suggests that precision psychiatry, an interdisciplinary field of psychiatry, precision medicine, and pharmacogenomics, serves as an indispensable foundation of medical practices by offering the accurate medication with the accurate dose at the accurate time to patients with psychiatric disorders. In light of the latest advancements in artificial intelligence and machine learning techniques, numerous biomarkers and genetic loci associated with psychiatric diseases and relevant treatments are being discovered in precision psychiatry research by employing neuroimaging and multi-omics. In this review, we focus on the latest developments for precision psychiatry research using artificial intelligence and machine learning approaches, such as deep learning and neural network algorithms, together with multi-omics and neuroimaging data. Firstly, we review precision psychiatry and pharmacogenomics studies that leverage various artificial intelligence and machine learning techniques to assess treatment prediction, prognosis prediction, diagnosis prediction, and the detection of potential biomarkers. In addition, we describe potential biomarkers and genetic loci that have been discovered to be associated with psychiatric diseases and relevant treatments. Moreover, we outline the limitations in regard to the previous precision psychiatry and pharmacogenomics studies. Finally, we present a discussion of directions and challenges for future research.

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Treatment-Resistant Schizophrenia: Insights From Genetic Studies and Machine Learning Approaches

Cited by 22 publications

References 50 publications

The promise of machine learning in predicting treatment outcomes in psychiatry

The promise of machine learning in predicting treatment outcomes in psychiatry

<p>Challenges and Future Prospects of Precision Medicine in Psychiatry</p>

Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches

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