Predicting mechanical restraint of psychiatric inpatients by applying machine learning on electronic health data

Danielsen, Andreas A.; Fenger, Morten; Østergaard, Søren Dinesen; Nielbo, Kristoffer Laigaard; Mors, Ole

doi:10.1111/acps.13061

Cited by 27 publications

(25 citation statements)

References 41 publications

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“…Treatment issues such as adherence or misuse are also depicted (6 cases) [ 56 , 72 , 74 , 81 , 101 , 103 ]. Only 1 study on mechanical restraints [ 90 ] and 1 on cognitive troubles [ 97 ] were found. A total of 8 studies were transnosographic [ 59 , 67 - 71 , 73 , 76 ]: 6 met the CEGS N-GRID 2016 Center of Excellence in Genomic Science Neuropsychiatric-Genome-Scale and Research Domain Criteria (RDoC) Individualized Domains 2016 Shared Task in Clinical NLP criteria, which will be developed further in our results.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning and Natural Language Processing in Mental Health: Systematic Review

Glaz¹,

Haralambous²,

Kim-Dufor³

et al. 2021

J Med Internet Res

239

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Background Machine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining. Objective The primary aim of this systematic review was to summarize and characterize, in methodological and technical terms, studies that used machine learning and NLP techniques for mental health. The secondary aim was to consider the potential use of these methods in mental health clinical practice Methods This systematic review follows the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) guidelines and is registered with PROSPERO (Prospective Register of Systematic Reviews; number CRD42019107376). The search was conducted using 4 medical databases (PubMed, Scopus, ScienceDirect, and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, and mental disorder. The exclusion criteria were as follows: languages other than English, anonymization process, case studies, conference papers, and reviews. No limitations on publication dates were imposed. Results A total of 327 articles were identified, of which 269 (82.3%) were excluded and 58 (17.7%) were included in the review. The results were organized through a qualitative perspective. Although studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into 3 categories: patients included in medical databases, patients who came to the emergency room, and social media users. The main objectives were to extract symptoms, classify severity of illness, compare therapy effectiveness, provide psychopathological clues, and challenge the current nosography. Medical records and social media were the 2 major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than transparent functioning classifiers. Python was the most frequently used platform. Conclusions Machine learning and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new information, and only one major category of the population (ie, social media users) is an imprecise cohort. Moreover, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, machine learning and NLP techniques provide useful information from unexplored data (ie, patients’ daily habits that are usually inaccessible to care providers). Before considering It as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner. Machine learning and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice.

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Section: Resultsmentioning

confidence: 99%

Machine Learning and Natural Language Processing in Mental Health: Systematic Review

Glaz¹,

Haralambous²,

Kim-Dufor³

et al. 2021

J Med Internet Res

239

View full text Add to dashboard Cite

show abstract

“…inform teaching strategies and education policies), in political psychology (in which large-scale surveys aimed at informing prediction of voting behavior are periodically conducted and many efforts are devoted to developing text models detecting toxic or misleading political contents), and in several domains of clinical research (in which predictive models are used for purposes ranging from causal modeling of psychiatric diagnoses to predicting patients' risk of being subjected to mechanical restraint; see Danielsen et al, 2019). Other clinical fields have fewer readily available data sets but also have high potential.…”

Section: Clear Practical Implicationsmentioning

confidence: 99%

Putting Psychology to the Test: Rethinking Model Evaluation Through Benchmarking and Prediction

Rocca

Yarkoni

2021

Advances in Methods and Practices in Psychological Science

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Consensus on standards for evaluating models and theories is an integral part of every science. Nonetheless, in psychology, relatively little focus has been placed on defining reliable communal metrics to assess model performance. Evaluation practices are often idiosyncratic and are affected by a number of shortcomings (e.g., failure to assess models’ ability to generalize to unseen data) that make it difficult to discriminate between good and bad models. Drawing inspiration from fields such as machine learning and statistical genetics, we argue in favor of introducing common benchmarks as a means of overcoming the lack of reliable model evaluation criteria currently observed in psychology. We discuss a number of principles benchmarks should satisfy to achieve maximal utility, identify concrete steps the community could take to promote the development of such benchmarks, and address a number of potential pitfalls and concerns that may arise in the course of implementation. We argue that reaching consensus on common evaluation benchmarks will foster cumulative progress in psychology and encourage researchers to place heavier emphasis on the practical utility of scientific models.

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“…Predicting real-life outcomes is also an important goal in educational psychology (where understanding the factors that impact educational achievement can directly inform teaching strategies and education policies), in political psychology (where large-scale surveys aimed at informing prediction of voting behavior are periodically conducted, and many efforts are devoted to developing text models detecting toxic or misleading political contents), and in several domains of clinical research (where predictive models are used for purposes ranging from causal modeling of psychiatric diagnoses, to predicting patients' risk of being subjected to mechanical restraint, see Danielsen, Fenger, Østergaard, Nielbo, & Mors, 2019). Other clinical fields have fewer readily available datasets, but also have high potential.…”

Section: Clear Practical Implicationsmentioning

confidence: 99%

Putting psychology to the test: Rethinking model evaluation through benchmarking and prediction

Rocca¹,

Yarkoni²

2020

Preprint

View full text Add to dashboard Cite

Consensus on standards for evaluating models and theories is an integral part of every science. Nonetheless, in psychology, relatively little focus has been placed on defining reliable communal metrics to assess model performance. Evaluation practices are often idiosyncratic, and are affected by a number of shortcomings (e.g., failure to assess models' ability to generalize to unseen data) that make it difficult to discriminate between good and bad models. Drawing inspiration from fields like machine learning and statistical genetics, we argue in favor of introducing common benchmarks as a means of overcoming the lack of reliable model evaluation criteria currently observed in psychology. We discuss a number of principles benchmarks should satisfy to achieve maximal utility; identify concrete steps the community could take to promote the development of such benchmarks; and address a number of potential pitfalls and concerns that may arise in the course of implementation. We argue that reaching consensus on common evaluation benchmarks will foster cumulative progress in psychology, and encourage researchers to place heavier emphasis on the practical utility of scientific models.

show abstract

Predicting mechanical restraint of psychiatric inpatients by applying machine learning on electronic health data

Cited by 27 publications

References 41 publications

Machine Learning and Natural Language Processing in Mental Health: Systematic Review

Machine Learning and Natural Language Processing in Mental Health: Systematic Review

Putting Psychology to the Test: Rethinking Model Evaluation Through Benchmarking and Prediction

Putting psychology to the test: Rethinking model evaluation through benchmarking and prediction

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