Machine Learning Approach to Inpatient Violence Risk Assessment Using Routinely Collected Clinical Notes in Electronic Health Records

Menger, Vincent Jorn; Spruit, Marco; Est, Roel van; Nap, Eline; Scheepers, Floortje

doi:10.1001/jamanetworkopen.2019.6709

Cited by 37 publications

(74 citation statements)

References 39 publications

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“…59 Research that focused on predicting violence and suicide also noted that the discriminative power was vastly improved when machine learning approaches were adopted. 60,61…”

Section: Discussionmentioning

confidence: 99%

“…59 Research that focused on predicting violence and suicide also noted that the discriminative power was vastly improved when machine learning approaches were adopted. 60,61 Implications Despite the debate on whether readmissions should be used as a proxy for the quality of care provided, many hospitals are still routinely assessed on readmission rates and are penalised if the rates are too high. The actual reasons why patients are rapidly readmitted may be influenced by a variety of patient-levels factors, broader social and environmental factors such as social support, and hospital-and health-system-level factors.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

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Clinical risk model to predict 28-day unplanned readmission via the accident and emergency department after discharge from acute psychiatric units for patients with psychotic spectrum disorders

et al. 2020

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Background Unplanned readmissions rates are an important indicator of the quality of care provided in a psychiatric unit. However, there is no validated risk model to predict this outcome in patients with psychotic spectrum disorders. Aims This paper aims to establish a clinical risk prediction model to predict 28-day unplanned readmission via the accident and emergency department after discharge from acute psychiatric units for patients with psychotic spectrum disorders. Method Adult patients with psychotic spectrum disorders discharged within a 5-year period from all psychiatric units in Hong Kong were included in this study. Information on the socioeconomic background, past medical and psychiatric history, current discharge episode and Health of the Nation Outcome Scales (HoNOS) scores were used in a logistic regression to derive the risk model and the predictive variables. The sample was randomly split into two to derive (n = 10 219) and validate (n = 10 643) the model. Results The rate of unplanned readmission was 7.09%. The risk factors for unplanned readmission include higher number of previous admissions, comorbid substance misuse, history of violence and a score of one or more in the discharge HoNOS overactivity or aggression item. Protective factors include older age, prescribing clozapine, living with family and relatives after discharge and imposition of conditional discharge. The model had moderate discriminative power with a c-statistic of 0.705 and 0.684 on the derivation and validation data-set. Conclusions The risk of readmission for each patient can be identified and adjustments in the treatment for those with a high risk may be implemented to prevent this undesirable outcome.

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“…59 Research that focused on predicting violence and suicide also noted that the discriminative power was vastly improved when machine learning approaches were adopted. 60,61…”

Section: Discussionmentioning

confidence: 99%

Section: Limitations and Future Directionsmentioning

confidence: 99%

Clinical risk model to predict 28-day unplanned readmission via the accident and emergency department after discharge from acute psychiatric units for patients with psychotic spectrum disorders

et al. 2020

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“…Koopman et al devised a binary classifier to detect whether or not death is related to cancer using free texts of death certificates [28]. Other text classification examples in clinical settings cover classifying a complete patient record with respect to its eligibility for a clinical trial [29], categorizing ICU risk stratification from nursing notes [30], assessing inpatient violence risk using routinely collected clinical notes [31], and among others. Sentiment Analysis : Unlocking the subjective meaning of clinical text is particularly helpful in psychology. A shared task for sentiment analysis of suicide notes was carried out as an i2b2 challenge [32].…”

Section: Resultsmentioning

confidence: 99%

A Lightweight API-Based Approach for Building Flexible Clinical NLP Systems

Shen

Krimpen

Spruit

2019

Journal of Healthcare Engineering

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Natural language processing (NLP) has become essential for secondary use of clinical data. Over the last two decades, many clinical NLP systems were developed in both academia and industry. However, nearly all existing systems are restricted to specific clinical settings mainly because they were developed for and tested with specific datasets, and they often fail to scale up. Therefore, using existing NLP systems for one’s own clinical purposes requires substantial resources and long-term time commitments for customization and testing. Moreover, the maintenance is also troublesome and time-consuming. This research presents a lightweight approach for building clinical NLP systems with limited resources. Following the design science research approach, we propose a lightweight architecture which is designed to be composable, extensible, and configurable. It takes NLP as an external component which can be accessed independently and orchestrated in a pipeline via web APIs. To validate its feasibility, we developed a web-based prototype for clinical concept extraction with six well-known NLP APIs and evaluated it on three clinical datasets. In comparison with available benchmarks for the datasets, three high F1 scores (0.861, 0.724, and 0.805) were obtained from the evaluation. It also gained a low F1 score (0.373) on one of the tests, which probably is due to the small size of the test dataset. The development and evaluation of the prototype demonstrates that our approach has a great potential for building effective clinical NLP systems with limited resources.

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“…Recent studies in psychiatry and in communities exposed to violence have started to navigate in this direction. 47 , 48 , 49 , 50 , 51 , 52 The present computational learning methods can help to elucidate associations between complex variables and numerous interactions between them. 53 …”

Section: Introductionmentioning

confidence: 99%

Uncovering social-contextual and individual mental health factors associated with violence via computational inference

et al. 2021

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Summary The identification of human violence determinants has sparked multiple questions from different academic fields. Innovative methodological assessments of the weight and interaction of multiple determinants are still required. Here, we examine multiple features potentially associated with confessed acts of violence in ex-members of illegal armed groups in Colombia (N = 26,349) through deep learning and feature-derived machine learning. We assessed 162 social-contextual and individual mental health potential predictors of historical data regarding consequentialist, appetitive, retaliative, and reactive domains of violence. Deep learning yields high accuracy using the full set of determinants. Progressive feature elimination revealed that contextual factors were more important than individual factors. Combined social network adversities, membership identification, and normalization of violence were among the more accurate social-contextual factors. To a lesser extent the best individual factors were personality traits (borderline, paranoid, and antisocial) and psychiatric symptoms. The results provide a population-based computational classification regarding historical assessments of violence in vulnerable populations.

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Machine Learning Approach to Inpatient Violence Risk Assessment Using Routinely Collected Clinical Notes in Electronic Health Records

Cited by 37 publications

References 39 publications

Clinical risk model to predict 28-day unplanned readmission via the accident and emergency department after discharge from acute psychiatric units for patients with psychotic spectrum disorders

Clinical risk model to predict 28-day unplanned readmission via the accident and emergency department after discharge from acute psychiatric units for patients with psychotic spectrum disorders

A Lightweight API-Based Approach for Building Flexible Clinical NLP Systems

Uncovering social-contextual and individual mental health factors associated with violence via computational inference

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