Alerting on mortality among patients discharged from the emergency department: a machine learning model

Barash, Yiftach; Soffer, Shelly; Grossman, Ehud; Tau, Noam; Sorin, Vera; BenDavid, Eyal; Irony, Avinoah; Konen, Eli; Zimlichman, Eyal; Klang, Eyal

doi:10.1136/postgradmedj-2020-138899

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…Previous machine learning studies evaluated models for different ED outcomes, for example, prediction of mortality, intensive care unit (ICU) admission, and resource utilization. [18][19][20] A commonly used machine learning model is random forest. Random forest is an ensemble learning method commonly used for classification problems.…”

Section: Machine Learning For Prediction Of Intra-abdominal Abscesses In Patients With Crohn's Disease Visiting the Emergency Departmentmentioning

confidence: 99%

Machine learning for prediction of intra-abdominal abscesses in patients with Crohn’s disease visiting the emergency department

Levartovsky

Barash

Ben‐Horin

et al. 2021

Therap Adv Gastroenterol

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Background: Intra-abdominal abscess (IA) is an important clinical complication of Crohn’s disease (CD). A high index of clinical suspicion is needed as imaging is not routinely used during hospital admission. This study aimed to identify clinical predictors of an IA among hospitalized patients with CD using machine learning. Methods: We created an electronic data repository of all patients with CD who visited the emergency department of our tertiary medical center between 2012 and 2018. We searched for the presence of an IA on abdominal imaging within 7 days from visit. Machine learning models were trained to predict the presence of an IA. A logistic regression model was compared with a random forest model. Results: Overall, 309 patients with CD were hospitalized and underwent abdominal imaging within 7 days. Forty patients (12.9%) were diagnosed with an IA. On multivariate analysis, high C-reactive protein (CRP) [above 65 mg/l, adjusted odds ratio (aOR): 16 (95% CI: 5.51–46.18)], leukocytosis [above 10.5 K/μl, aOR: 4.47 (95% CI: 1.91–10.45)], thrombocytosis [above 322.5 K/μl, aOR: 4.1 (95% CI: 2–8.73)], and tachycardia [over 97 beats per minute, aOR: 2.7 (95% CI: 1.37–5.3)] were independently associated with an IA. Random forest model showed an area under the curve of 0.817 ± 0.065 with six features (CRP, hemoglobin, WBC, age, current biologic therapy, and BUN). Conclusion: In our large tertiary center cohort, the machine learning model identified the association of six clinical features (CRP, hemoglobin, WBC, age, BUN, and biologic therapy) with the presentation of an IA. These may assist as a decision support tool in triaging CD patients for imaging to exclude this potentially life-threatening complication.

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Section: Machine Learning For Prediction Of Intra-abdominal Abscesses In Patients With Crohn's Disease Visiting the Emergency Departmentmentioning

confidence: 99%

Machine learning for prediction of intra-abdominal abscesses in patients with Crohn’s disease visiting the emergency department

Levartovsky

Barash

Ben‐Horin

et al. 2021

Therap Adv Gastroenterol

Self Cite

View full text Add to dashboard Cite

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“…Various machine learning applications are being investigated for optimizing healthcare. Emphasis is placed on the use of algorithms for predicting the clinical course [24,25]. Such decision support tools can affect the diagnostic workup and treatment plan.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Model for Outcome Prediction of Patients Suffering from Acute Diverticulitis Arriving at the Emergency Department—A Proof of Concept Study

et al. 2021

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Background & Aims: We aimed at identifying specific emergency department (ED) risk factors for developing complicated acute diverticulitis (AD) and evaluate a machine learning model (ML) for predicting complicated AD. Methods: We analyzed data retrieved from unselected consecutive large bowel AD patients from five hospitals from the Mount Sinai health system, NY. The study time frame was from January 2011 through March 2021. Data were used to train and evaluate a gradient-boosting machine learning model to identify patients with complicated diverticulitis, defined as a need for invasive intervention or in-hospital mortality. The model was trained and evaluated on data from four hospitals and externally validated on held-out data from the fifth hospital. Results: The final cohort included 4997 AD visits. Of them, 129 (2.9%) visits had complicated diverticulitis. Patients with complicated diverticulitis were more likely to be men, black, and arrive by ambulance. Regarding laboratory values, patients with complicated diverticulitis had higher levels of absolute neutrophils (AUC 0.73), higher white blood cells (AUC 0.70), platelet count (AUC 0.68) and lactate (AUC 0.61), and lower levels of albumin (AUC 0.69), chloride (AUC 0.64), and sodium (AUC 0.61). In the external validation cohort, the ML model showed AUC 0.85 (95% CI 0.78–0.91) for predicting complicated diverticulitis. For Youden’s index, the model showed a sensitivity of 88% with a false positive rate of 1:3.6. Conclusions: A ML model trained on clinical measures provides a proof of concept performance in predicting complications in patients presenting to the ED with AD. Clinically, it implies that a ML model may classify low-risk patients to be discharged from the ED for further treatment under an ambulatory setting.

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Desired clinical applications of artificial intelligence in emergency medicine: A Delphi study

Li,

Hayward,

Aguilar

et al. 2024

The American Journal of Emergency Medicine

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Alerting on mortality among patients discharged from the emergency department: a machine learning model

Cited by 5 publications

References 22 publications

Machine learning for prediction of intra-abdominal abscesses in patients with Crohn’s disease visiting the emergency department

Machine learning for prediction of intra-abdominal abscesses in patients with Crohn’s disease visiting the emergency department

Machine Learning Model for Outcome Prediction of Patients Suffering from Acute Diverticulitis Arriving at the Emergency Department—A Proof of Concept Study

Desired clinical applications of artificial intelligence in emergency medicine: A Delphi study

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