Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks

Zhang, Xingyu; Kim, Joyce; Patzer, Rachel E.; Pitts, Stephen R.; Patzer, Aaron; Schrager, Justin D.

doi:10.3414/me17-01-0024

Cited by 94 publications

(72 citation statements)

References 62 publications

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“…However, modern machine learning approaches possess scalability within a larger context of health information technology (eg, extracting a multitude of potential predictors from electronic health records and monitoring devices, continuous sophistication of the model using updated health data, and reinforcement learning). 55 , 56 Indeed, the machine learning approaches have demonstrated potential to further improve their performance by integrating recently developed algorithms, such as natural language processing 57 , 58 and diagnostically relevant facial gestalt information from images. 59 Our observations and these recent developments collectively present reason for cautious optimism that machine learning approaches, as an assistive technology, further enhance the clinician’s triage decision making in a large ED population of children.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning–Based Prediction of Clinical Outcomes for Children During Emergency Department Triage

et al. 2019

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Key Points Question Do machine learning approaches improve the ability to predict clinical outcomes and disposition of children at emergency department triage? Findings In this prognostic study of a nationally representative sample of 52 037 emergency department visits by children, machine learning–based triage models had better discrimination ability for clinical outcomes and disposition compared with the conventional triage approaches, with a higher sensitivity for the critical care outcome and higher specificity for the hospitalization outcome. Meaning Machine learning may improve the prediction ability of triage approaches and could be used to reduce undertriage of critically ill children and to improve resource allocation in emergency departments.

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Section: Discussionmentioning

confidence: 99%

Machine Learning–Based Prediction of Clinical Outcomes for Children During Emergency Department Triage

et al. 2019

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“…These approaches offer advantages in that they account for high-order, non-linear interactions between predictors and gain more stable prediction [17]. Recent studies have reported that the application of machine learning models may provide high predictive ability at ED triage in selected patient populations and settings—e.g., children [18], patients with asthma and COPD exacerbation [19], and in few urban EDs [11, 20, 21]. Despite this clinical and research promise, no study has yet examined the utility of modern machine learning models for predicting clinical outcomes in a large population of adult patients in the ED.…”

Section: Introductionmentioning

confidence: 99%

Emergency department triage prediction of clinical outcomes using machine learning models

et al. 2019

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Background Development of emergency department (ED) triage systems that accurately differentiate and prioritize critically ill from stable patients remains challenging. We used machine learning models to predict clinical outcomes, and then compared their performance with that of a conventional approach—the Emergency Severity Index (ESI). Methods Using National Hospital and Ambulatory Medical Care Survey (NHAMCS) ED data, from 2007 through 2015, we identified all adult patients (aged ≥ 18 years). In the randomly sampled training set (70%), using routinely available triage data as predictors (e.g., demographics, triage vital signs, chief complaints, comorbidities), we developed four machine learning models: Lasso regression, random forest, gradient boosted decision tree, and deep neural network. As the reference model, we constructed a logistic regression model using the five-level ESI data. The clinical outcomes were critical care (admission to intensive care unit or in-hospital death) and hospitalization (direct hospital admission or transfer). In the test set (the remaining 30%), we measured the predictive performance, including area under the receiver-operating-characteristics curve (AUC) and net benefit (decision curves) for each model. Results Of 135,470 eligible ED visits, 2.1% had critical care outcome and 16.2% had hospitalization outcome. In the critical care outcome prediction, all four machine learning models outperformed the reference model (e.g., AUC, 0.86 [95%CI 0.85–0.87] in the deep neural network vs 0.74 [95%CI 0.72–0.75] in the reference model), with less under-triaged patients in ESI triage levels 3 to 5 (urgent to non-urgent). Likewise, in the hospitalization outcome prediction, all machine learning models outperformed the reference model (e.g., AUC, 0.82 [95%CI 0.82–0.83] in the deep neural network vs 0.69 [95%CI 0.68–0.69] in the reference model) with less over-triages in ESI triage levels 1 to 3 (immediate to urgent). In the decision curve analysis, all machine learning models consistently achieved a greater net benefit—a larger number of appropriate triages considering a trade-off with over-triages—across the range of clinical thresholds. Conclusions Compared to the conventional approach, the machine learning models demonstrated a superior performance to predict critical care and hospitalization outcomes. The application of modern machine learning models may enhance clinicians’ triage decision making, thereby achieving better clinical care and optimal resource utilization. Electronic supplementary material The online version of this article (10.1186/s13054-019-2351-7) contains supplementary material, which is available to authorized users.

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“…The number of actual and predicted outcomes of prediction models, according to the score of the reference model. www.nature.com/scientificreports/ and reinforcement learning [21][22][23][24] . In the past, this scalability had not been attainable with the use of conventional approaches.…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based prediction of acute severity in infants hospitalized for bronchiolitis: a multicenter prospective study

Raita

Camargo

Macias

et al. 2020

Sci Rep

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We aimed to develop machine learning models to accurately predict bronchiolitis severity, and to compare their predictive performance with a conventional scoring (reference) model. In a 17-center prospective study of infants (aged < 1 year) hospitalized for bronchiolitis, by using routinely-available pre-hospitalization data as predictors, we developed four machine learning models: Lasso regression, elastic net regression, random forest, and gradient boosted decision tree. We compared their predictive performance-e.g., area-under-the-curve (AUC), sensitivity, specificity, and net benefit (decision curves)-using a cross-validation method, with that of the reference model. the outcomes were positive pressure ventilation use and intensive treatment (admission to intensive care unit and/ or positive pressure ventilation use). Of 1,016 infants, 5.4% underwent positive pressure ventilation and 16.0% had intensive treatment. For the positive pressure ventilation outcome, machine learning models outperformed reference model (e.g., AUC 0.88 [95% CI 0.84-0.93] in gradient boosted decision tree vs 0.62 [95% CI 0.53-0.70] in reference model), with higher sensitivity (0.89 [95% CI 0.80-0.96] vs. 0.62 [95% CI 0.49-0.75]) and specificity (0.77 [95% CI 0.75-0.80] vs. 0.57 [95% CI 0.54-0.60]). The machine learning models also achieved a greater net benefit over ranges of clinical thresholds. Machine learning models consistently demonstrated a superior ability to predict acute severity and achieved greater net benefit.

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Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks

Cited by 94 publications

References 62 publications

Machine Learning–Based Prediction of Clinical Outcomes for Children During Emergency Department Triage

Machine Learning–Based Prediction of Clinical Outcomes for Children During Emergency Department Triage

Emergency department triage prediction of clinical outcomes using machine learning models

Machine learning-based prediction of acute severity in infants hospitalized for bronchiolitis: a multicenter prospective study

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