Revisión sistemática del aprendizaje automático para la evaluación y feedback de la fidelidad al tratamiento

Published on Jan 1, 2021in Psychosocial Intervention
· DOI :10.5093/PI2021A4
Asghar Ahmadi (ACU: Australian Catholic University), Michael Noetel9
Estimated H-index: 9
(ACU: Australian Catholic University)
+ 7 AuthorsNikos Ntoumanis1
Estimated H-index: 1
(University of Southern Denmark)
#1Yu Song (SCNU: South China Normal University)H-Index: 2
#2Shunwei Lei (SCNU: South China Normal University)
Last. Ying Ding (SCNU: South China Normal University)
view all 5 authors...
Due to benefits for teaching and learning, an increasing number of studies have focused on classroom dialogue and how to make it productive. Coding, in which the transcribed conversation is allocat...
1 CitationsSource
#1Katie Aafjes-van Doorn (Yeshiva University)H-Index: 7
#2Céline KamsteegH-Index: 3
Last. Marc AafjesH-Index: 1
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Machine learning (ML) offers robust statistical and probabilistic techniques that can help to make sense of large amounts of data. This scoping review paper aims to broadly explore the nature of re...
13 CitationsSource
#1Simon B. GoldbergH-Index: 22
Last. David C. Atkins (UW: University of Washington)H-Index: 77
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Artificial intelligence generally and machine learning specifically have become deeply woven into the lives and technologies of modern life. Machine learning is dramatically changing scientific research and industry and may also hold promise for addressing limitations encountered in mental health care and psychotherapy. The current paper introduces machine learning and natural language processing as related methodologies that may prove valuable for automating the assessment of meaningful aspects...
15 CitationsSource
#1Pedro J. Teixeira (University of Lisbon)H-Index: 54
#2Marta M. Marques (University of Lisbon)H-Index: 16
Last. Martin S. Hagger (UCM: University of California, Merced)H-Index: 91
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While evidence suggests that interventions based on self-determination theory can be effective in motivating adoption and maintenance of health-related behaviors, and in promoting adaptive psycholo ...
97 CitationsSource
#1Daniel M. Low (MIT: Massachusetts Institute of Technology)H-Index: 5
#2Kate H. Bentley (Harvard University)H-Index: 9
Last. Satrajit S. Ghosh (Harvard University)H-Index: 39
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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...
40 CitationsSource
#1Qifang Bi (Johns Hopkins University)H-Index: 12
#2Katherine E Goodman (Johns Hopkins University)H-Index: 8
Last. Justin Lessler (Johns Hopkins University)H-Index: 58
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: Machine learning is a branch of computer science that has the potential to transform epidemiological sciences. Amid a growing focus on "Big Data," it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to...
56 CitationsSource
#1Jihyun Park (UCI: University of California, Irvine)H-Index: 6
#2Dimitrios Kotzias (UCI: University of California, Irvine)H-Index: 7
Last. Padhraic Smyth (UCI: University of California, Irvine)H-Index: 80
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Objective Amid electronic health records, laboratory tests, and other technology, office-based patient and provider communication is still the heart of primary medical care. Patients typically present multiple complaints, requiring physicians to decide how to balance competing demands. How this time is allocated has implications for patient satisfaction, payments, and quality of care. We investigate the effectiveness of machine learning methods for automated annotation of medical topics in patie...
11 CitationsSource
#1Andy M.Y. Tai (UHN: University Health Network)H-Index: 1
#2Alcides Albuquerque (UHN: University Health Network)H-Index: 1
Last. Roger S. McIntyre (U of T: University of Toronto)H-Index: 93
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Abstract Introduction Machine learning capability holds promise to inform disease models, the discovery and development of novel disease modifying therapeutics and prevention strategies in psychiatry. Herein, we provide an introduction on how machine learning/Artificial Intelligence (AI) may instantiate such capabilities, as well as provide rationale for its application to psychiatry in both research and clinical ecosystems. Methods Databases PubMed and PsycINFO were searched from 1966 to June 2...
29 CitationsSource
Jul 17, 2019 in AAAI (National Conference on Artificial Intelligence)
#1Abhijit Suresh (CU: University of Colorado Boulder)H-Index: 6
#2Tamara Sumner (CU: University of Colorado Boulder)H-Index: 29
Last. Wayne H. Ward (CU: University of Colorado Boulder)H-Index: 32
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Our work builds on advances in deep learning for natural language processing to automatically analyze transcribed classroom discourse and reliably generate information about teachers’ uses of specific discursive strategies called ”talk moves.” Talk moves can be used by both teachers and learners to construct conversations in which students share their thinking, actively consider the ideas of others, and engage in sustained reasoning. Currently, providing teachers with detailed feedback about the...
9 CitationsSource
Jun 30, 2019 in ACL (Meeting of the Association for Computational Linguistics)
#1Jie Cao (HUST: Huazhong University of Science and Technology)H-Index: 3
#2Michael Tanana (UofU: University of Utah)H-Index: 10
Last. Vivek Srikumar (UofU: University of Utah)H-Index: 21
view all 6 authors...
Automatically analyzing dialogue can help understand and guide behavior in domains such as counseling, where interactions are largely mediated by conversation. In this paper, we study modeling behavioral codes used to asses a psychotherapy treatment style called Motivational Interviewing (MI), which is effective for addressing substance abuse and related problems. Specifically, we address the problem of providing real-time guidance to therapists with a dialogue observer that (1) categorizes ther...
18 CitationsSource
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