A scoping review of machine learning in psychotherapy research

Doorn, Katie Aafjes‐van; Kamsteeg, Céline; Bate, Jordan; Aafjes, Marc

doi:10.1080/10503307.2020.1808729

Cited by 129 publications

(105 citation statements)

References 99 publications

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“…Several studies since then have tried to predict which evi dencebased psychotherapy is most likely to benefit a specific patient 55,59 , including efforts to identify which of two (or more) psychotherapies may be most effective 60,61 , and whether a given patient is predicted to respond better to psychotherapy or medi cations 56 . A recent scoping review 62 identified a total of 44 studies that developed and tested a machine learning model in psycho therapy, but only seven of them reported on the feasibility of the tool. Since psychotherapy trials are often expensive and rarely have large sample sizes, some have argued that predictive mod els may need to be developed initially with large observational datasets 63 .…”

Section: Psychotherapiesmentioning

confidence: 99%

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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: Psychotherapiesmentioning

confidence: 99%

“…In general, many machine learning approaches to predict responses to psychotherapies are in the early stages of develop ment 62 . However, a notable exception is found in the welldevel oped literature on routine outcome monitoring and "progress feedback".…”

Section: Psychotherapiesmentioning

confidence: 99%

The promise of machine learning in predicting treatment outcomes in psychiatry

Bondar²,

et al. 2021

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“…The present study reports on a rigorous cross-validation method for producing results that is likely to be generalizable to the broader population. However, there is a risk of identifying predictors in the test and validation samples that may not be as important in a new sample (Aafjes-van Doorn et al, 2020). Although the absence of out-of-sample external validation is common in mental health machine learning research (Aafjes-van Doorn et al, 2020), an additional step of out-of-sample validation would certainly strengthen the external validity of the findings (Sammut and Webb, 2017).…”

Section: Strengths and Limitationsmentioning

confidence: 99%

Identifying Predictors of Psychological Distress During COVID-19: A Machine Learning Approach

et al. 2020

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“…We assume that due to the increasing complexity of medication administration, physicians hope to be supported by AI-based decision support systems to avoid drug interactions especially when treating polymorbid, polypharmacy patients. The low rating of AI's potential for psychiatric diseases is remarkable as there are several AI applications for this medical field as described for ML in recent publications [52,53]. Respondents in the study population might not yet be completely informed about all potential AI applications, like in this context as well as the application of AI for speech or voice analysis using a recording.…”

Section: Fields Of Ai Application With the Potential To Improve Clinimentioning

confidence: 99%

Future Medical Artificial Intelligence Application Requirements and Expectations of Physicians in German University Hospitals: Web-Based Survey

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Palm³

et al. 2021

J Med Internet Res

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Background The increasing development of artificial intelligence (AI) systems in medicine driven by researchers and entrepreneurs goes along with enormous expectations for medical care advancement. AI might change the clinical practice of physicians from almost all medical disciplines and in most areas of health care. While expectations for AI in medicine are high, practical implementations of AI for clinical practice are still scarce in Germany. Moreover, physicians’ requirements and expectations of AI in medicine and their opinion on the usage of anonymized patient data for clinical and biomedical research have not been investigated widely in German university hospitals. Objective This study aimed to evaluate physicians’ requirements and expectations of AI in medicine and their opinion on the secondary usage of patient data for (bio)medical research (eg, for the development of machine learning algorithms) in university hospitals in Germany. Methods A web-based survey was conducted addressing physicians of all medical disciplines in 8 German university hospitals. Answers were given using Likert scales and general demographic responses. Physicians were asked to participate locally via email in the respective hospitals. Results The online survey was completed by 303 physicians (female: 121/303, 39.9%; male: 173/303, 57.1%; no response: 9/303, 3.0%) from a wide range of medical disciplines and work experience levels. Most respondents either had a positive (130/303, 42.9%) or a very positive attitude (82/303, 27.1%) towards AI in medicine. There was a significant association between the personal rating of AI in medicine and the self-reported technical affinity level (H4=48.3, P<.001). A vast majority of physicians expected the future of medicine to be a mix of human and artificial intelligence (273/303, 90.1%) but also requested a scientific evaluation before the routine implementation of AI-based systems (276/303, 91.1%). Physicians were most optimistic that AI applications would identify drug interactions (280/303, 92.4%) to improve patient care substantially but were quite reserved regarding AI-supported diagnosis of psychiatric diseases (62/303, 20.5%). Of the respondents, 82.5% (250/303) agreed that there should be open access to anonymized patient databases for medical and biomedical research. Conclusions Physicians in stationary patient care in German university hospitals show a generally positive attitude towards using most AI applications in medicine. Along with this optimism comes several expectations and hopes that AI will assist physicians in clinical decision making. Especially in fields of medicine where huge amounts of data are processed (eg, imaging procedures in radiology and pathology) or data are collected continuously (eg, cardiology and intensive care medicine), physicians’ expectations of AI to substantially improve future patient care are high. In the study, the greatest potential was seen in the application of AI for the identification of drug interactions, assumedly due to the rising complexity of drug administration to polymorbid, polypharmacy patients. However, for the practical usage of AI in health care, regulatory and organizational challenges still have to be mastered.

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A scoping review of machine learning in psychotherapy research

Cited by 129 publications

References 99 publications

The promise of machine learning in predicting treatment outcomes in psychiatry

The promise of machine learning in predicting treatment outcomes in psychiatry

Identifying Predictors of Psychological Distress During COVID-19: A Machine Learning Approach

Future Medical Artificial Intelligence Application Requirements and Expectations of Physicians in German University Hospitals: Web-Based Survey

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