Machine learning and natural language processing in psychotherapy research: Alliance as example use case.

Goldberg, Simon B.; Flemotomos, Nikolaos; Martínez, Victor R.; Tanana, Michael; Kuo, Patty; Pace, Brian T.; Villatte, Jennifer L.; Georgiou, Panayiotis G.; Epps, Jake Van; Imel, Zac E.; Narayanan, Shrikanth; Atkins, David C.

doi:10.1037/cou0000382

Cited by 86 publications

(77 citation statements)

References 74 publications

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“…Given the omnipresence of smartphones, ecological momentary assessment (EMA) has become increasingly popular in psychological research and holds promise for psychotherapy studies investigating relevant state-like within-person vs. trait-like between-person processes. In addition to EMA, the expansion of “passive” (i.e., no user input required) measurement methods also holds promise for examining predictors and processes of change, including sensor data (e.g., activity levels and movement from accelerometer and GPS, proxies of social interaction from call and text meta-data) from smartphones and wearables ( 43 ), as well as other markers based on motion ( 44 , 45 ), acoustic and language style ( 46 – 48 ) and physiology ( 49 ). The extent to which biological variables, such as hormones ( 50 ), neuroimaging ( 51 – 53 ) and inflammatory biomarkers ( 54 ), provide incremental predictive validity above conventional (and less costly and time-consuming) self-report measures is also an important area of research ( 55 ).…”

Section: What Does the Future Hold?mentioning

confidence: 99%

Disentangling Trait-Like Between-Individual vs. State-Like Within-Individual Effects in Studying the Mechanisms of Change in CBT

Zilcha‐Mano

Webb

2021

Front. Psychiatry

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Hofmann et al. argued that “[w]hile the clinical field has produced a dizzying number of treatment models and treatment protocols for virtually every psychiatric and psychological problem imaginable, increases in understanding of the processes of change in psychotherapy has been slow to arrive.” We propose that one of the reasons for the slow progress is that prior psychotherapy research conflates trait-like and state-like components of mechanisms of change. Trait-like components can serve as prescriptive or prognostic variables, whereas state-like components reflect within-client processes of change, and may highlight active ingredients of successful treatment. Distinguishing between the two is essential for clarifying the underlying processes of change in psychotherapy, and ultimately identifying empirically-derived individualized treatment targets. We review studies that implement methodological and statistical approaches for disentangling the two. These studies clarified particular mechanisms of change that may operate in a given treatment, highlighted differences in the processes of change between different treatments, and explored the within-individual interplay between different mechanisms of change during treatment. Examples include studies investigating the therapeutic role of behavioral, cognitive, and interpersonal skills, as well as emotional processing. We conclude with suggestions for future research, including attention to diversity, improved measurement to facilitate a reliable and valid estimation of trait-like and state-like components, the use of appropriate statistical approaches to adequately disentangle the two components, integration of theory-driven and data-driven methods of analysis, and the need to experimentally manipulate the state-like changes in a given mechanism of change to strengthen causal inferences.

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Section: What Does the Future Hold?mentioning

confidence: 99%

Disentangling Trait-Like Between-Individual vs. State-Like Within-Individual Effects in Studying the Mechanisms of Change in CBT

Zilcha‐Mano

Webb

2021

Front. Psychiatry

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“…In this context, the machine learning methods were designed to automatically assign codes from the behavioural coding measures to overt interactions recorded in the dataset (e.g., words/utteran- (Atkins et al, 2014;Can et al, 2015;Can et al, 2012;Can et al, 2016;Cao et al, 2020;Carcone et al, 2019, Study 1;Chakravarthula et al, 2015;Chen et al, 2019;Gibson et al, 2017;Gibson et al, 2016;Gupta et al, 2014;Hasan et al, 2019;Hasan et al, 2018;Imel et al, 2015;Perez-Rosas et al, 2017;Perez-Rosas et al, 2019;Singla et al, 2018;Tanana et al, 2016;Xiao et al, 2012;Xiao et al, 2015;Xiao, Can, et al, 2016;Xiao, Huang, et al, 2016) Medical care, provider-patient clinical interactions (Carcone et al, 2019, Study 2;Park et al, 2019) Education (teachers) (Blanchard et al, 2016a;Blanchard et al, 2016b;Donelly et al, 2017;Donnely et al, 2016a;Donnelly et al, 2016b;Samei et al, 2014;Samei et al, 2015;Song et al, 2020;Suresh et al, 2019;Wang et al, 2014) Counselling, (counsellors), (Althoff et al, 2016;Flemotomos et al, 2018;Gallo et al, 2015;Gaut et al, 2017;Goldberg et al, 2020…”

Section: Synthesis Of Resultsmentioning

confidence: 99%

A Systematic Review of Machine Learning for Assessment and Feedback of Treatment Fidelity

Ahmadi¹,

Noetel²,

Schellekens³

et al. 2021

Preprint

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Many psychological treatments have been shown to be cost-effective and efficacious, as long as they are implemented faithfully. Assessing fidelity and providing feedback is expensive and time-consuming. Machine learning has been used to assess treatment fidelity, but the reliability and generalisability is unclear. We collated and critiqued all implementations of machine learning to assess the verbal behaviour of all helping professionals, with particular emphasis on treatment fidelity for therapists. We conducted searches using nine electronic databases for automated approaches of coding verbal behaviour in therapy and similar contexts. We completed screening, extraction, and quality assessment in duplicate. Fifty-two studies met our inclusion criteria (65.3% in psychotherapy). Automated coding methods performed better than chance, and some methods showed near human-level performance; performance tended to be better with larger data sets, a smaller number of codes, conceptually simple codes, and when predicting session-level ratings than utterance-level ones. Few studies adhered to best-practice machine learning guidelines. Machine learning demonstrated promising results, particularly where there are large, annotated datasets and a modest number of concrete features to code. These methods are novel, cost-effective, scalable ways of assessing fidelity and providing therapists with individualised, prompt, and objective feedback.

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“…Fine-grained longitudinal data on therapeutic processes, gathered within or outside therapy sessions, can be shared among researchers conducting individual patient data meta-analyses, in order to develop multivariable algorithms that contribute to precision mental health ( Furukawa et al, 2018 , 2019 ; Lin et al, 2019 ). Innovative machine learning approaches might predict trajectories of change based on these data, which can inform pre-treatment and in-session decisions of mental healthcare practices ( Cohen and DeRubeis, 2018 ; Goldberg et al, 2020 ; Rubel et al, 2020 ). Additionally, virtual reality (VR) interventions reveal novel findings on change mechanisms that were not conceivable with conventional studies so far.…”

Section: Novel Methodological and Technological Opportunitiesmentioning

confidence: 99%

More Light? Opportunities and Pitfalls in Digitalized Psychotherapy Process Research

et al. 2021

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While the evidence on the effectiveness of different psychotherapies is often strong, it is not settled whereby and how these therapies work. Knowledge on the causal factors and change mechanisms is of high clinical and public relevance, as it contributes to the empirically informed advancement of psychotherapeutic interventions. Here, digitalized research approaches might possess the potential to generate new insights into human behavior change, contributing to augmented interventions and mental healthcare practices with better treatment outcomes. In this perspective article, we describe recent findings of research into change mechanisms that were only feasible with digital tools and outline important future directions for this rather novel branch of research. Furthermore, we indicate several challenges and pitfalls that are to be solved, in order to advance digitalized psychotherapy process research, both methodologically and technologically.

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Machine learning and natural language processing in psychotherapy research: Alliance as example use case.

Cited by 86 publications

References 74 publications

Disentangling Trait-Like Between-Individual vs. State-Like Within-Individual Effects in Studying the Mechanisms of Change in CBT

Disentangling Trait-Like Between-Individual vs. State-Like Within-Individual Effects in Studying the Mechanisms of Change in CBT

A Systematic Review of Machine Learning for Assessment and Feedback of Treatment Fidelity

More Light? Opportunities and Pitfalls in Digitalized Psychotherapy Process Research

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