Machine Learning for Precision Psychiatry: Opportunities and Challenges

Bzdok, Danilo; Meyer‐Lindenberg, Andreas

doi:10.1016/j.bpsc.2017.11.007

Cited by 444 publications

(467 citation statements)

References 46 publications

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“…Machine learning might change diagnostic criteria given personalized medicine and continuous, real‐time monitoring, especially considering the limitations diagnostic criteria may currently have . By linking behavioral and biological features to symptoms instead of diagnoses, we could further understand the underlying diseases and endophenotypes that gives rise to the personalized configuration of symptoms and reduce the need of traditional disorders . Furthermore, not all acoustic features are measured across studies.…”

Section: Discussionmentioning

confidence: 99%

“…Comorbidity is one reason why the National Institute of Mental Health has developed the Research Domain Criteria with the goal of deconstructing diagnoses with biomarkers—from genetic to behavioral—to predict and improve response to treatments . Therefore, algorithms trained on behavioral descriptors could provide likelihood estimates for different disorders to aid clinicians in differential diagnosis (eg, determining whether a patient meets criteria for unipolar depression or bipolar disorder), help detect risk for chronic psychiatric disorders, psychiatric episodes, or suicidal behavior; and over time learn to predict the best treatment given multimodal (genetic, brain‐imaging, behavioral) data . Therefore, complementing clinical interviews with machine learning models trained on the recordings of these interviews could improve outcomes, save clinicians' time, reduce health care costs, and make treatment planning more efficient.…”

Section: Introductionmentioning

confidence: 99%

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Automated assessment of psychiatric disorders using speech: A systematic review

Low

Bentley

Ghosh

2020

Laryngoscope Investig Oto

288

268

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Objective There are many barriers to accessing mental health assessments including cost and stigma. Even when individuals receive professional care, assessments are intermittent and may be limited partly due to the episodic nature of psychiatric symptoms. Therefore, machine‐learning technology using speech samples obtained in the clinic or remotely could one day be a biomarker to improve diagnosis and treatment. To date, reviews have only focused on using acoustic features from speech to detect depression and schizophrenia. Here, we present the first systematic review of studies using speech for automated assessments across a broader range of psychiatric disorders. Methods We followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis (PRISMA) guidelines. We included studies from the last 10 years using speech to identify the presence or severity of disorders within the Diagnostic and Statistical Manual of Mental Disorders (DSM‐5). For each study, we describe sample size, clinical evaluation method, speech‐eliciting tasks, machine learning methodology, performance, and other relevant findings. Results 1395 studies were screened of which 127 studies met the inclusion criteria. The majority of studies were on depression, schizophrenia, and bipolar disorder, and the remaining on post‐traumatic stress disorder, anxiety disorders, and eating disorders. 63% of studies built machine learning predictive models, and the remaining 37% performed null‐hypothesis testing only. We provide an online database with our search results and synthesize how acoustic features appear in each disorder. Conclusion Speech processing technology could aid mental health assessments, but there are many obstacles to overcome, especially the need for comprehensive transdiagnostic and longitudinal studies. Given the diverse types of data sets, feature extraction, computational methodologies, and evaluation criteria, we provide guidelines for both acquiring data and building machine learning models with a focus on testing hypotheses, open science, reproducibility, and generalizability. Level of Evidence 3a

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated assessment of psychiatric disorders using speech: A systematic review

Low

Bentley

Ghosh

2020

Laryngoscope Investig Oto

288

268

View full text Add to dashboard Cite

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“…In statistical data analysis in general, there is a widely recognized tension between predictive performance and model interpretability (Bishop, ; Danilo Bzdok & Yeo, ; Hastie et al, ). Machine‐learning algorithms are particularly suited to achieve highly accurate predictions in a brute‐force fashion, which is why they might be promising for precision psychiatry (Danilo Bzdok & Meyer‐Lindenberg, ; Chekroud, Lane, & Ross, ). However, such purely data‐driven approaches were sometimes criticized for offering less direct insight into the cognitive or neurobiological architecture of schizophrenia.…”

Section: Discussionmentioning

confidence: 99%

Brain‐based ranking of cognitive domains to predict schizophrenia

Karrer

Bassett

Derntl

et al. 2019

Human Brain Mapping

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Schizophrenia is a devastating brain disorder that disturbs sensory perception, motor action, and abstract thought. Its clinical phenotype implies dysfunction of various mental domains, which has motivated a series of theories regarding the underlying pathophysiology. Aiming at a predictive benchmark of a catalog of cognitive functions, we developed a data‐driven machine‐learning strategy and provide a proof of principle in a multisite clinical dataset (n = 324). Existing neuroscientific knowledge on diverse cognitive domains was first condensed into neurotopographical maps. We then examined how the ensuing meta‐analytic cognitive priors can distinguish patients and controls using brain morphology and intrinsic functional connectivity. Some affected cognitive domains supported well‐studied directions of research on auditory evaluation and social cognition. However, rarely suspected cognitive domains also emerged as disease relevant, including self‐oriented processing of bodily sensations in gustation and pain. Such algorithmic charting of the cognitive landscape can be used to make targeted recommendations for future mental health research.

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“…The evolution has been much more protracted in psychiatry, given the complexity underpinning the aetiology of mental illnesses and diagnostic boundaries. With new neuroimaging technologies and latest advances in analytical strategies such as network analysis and machine learning, it may now be possible to apply this precision approach to psychiatry, including eating disorders (Bzdok & Meyer‐Lindenberg, ; Fernandes et al, ; Fernandes & Berk, ; Joyce, Kehagia, Tracy, Proctor, & Shergill, ; Loth et al, ; Schumann et al, ; Silbersweig & Loscalzo, ; Vieta, ; Zipfel & Schmidt, ).…”

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confidence: 99%