The DOI matches its Crossref record; 57 of the 58 references it cites were found in a registry or an index; none of the 46 sources its claims cite is checked for support yet.
Predicting hospital admission at emergency department triage using machine learning
Open accessAbstract
OBJECTIVE: To predict hospital admission at the time of ED triage using patient history in addition to information collected at triage. METHODS: This retrospective study included all adult ED visits between March 2014 and July 2017 from one academic and two community emergency rooms that resulted in either admission or discharge. A total of 972 variables were extracted per patient visit. Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets. We trained a series of nine binary classifiers using logistic regression (LR), gradient boosting (XGBoost), and deep neural networks (DNN) on three dataset types: one using only triage information, one using only patient history, and one using the full set of variables. Next, we tested the potential benefit of additional training samples by training models on increasing fractions of our data. Lastly, variables of importance were identified using information gain as a metric to create a low-dimensional model. RESULTS: A total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%. Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.88) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.87) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91-0.91), 0.92 for XGBoost (95% CI 0.92-0.93) and 0.92 for DNN (95% CI 0.92-0.92). All algorithms reached maximum performance at 50% of the training set or less. A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91-0.91). CONCLUSION: Machine learning can robustly predict hospital admission using triage information and patient history. The addition of historical information improves predictive performance significantly compared to using triage information alone, highlighting the need to incorporate these variables into prediction models.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
Machine learning models using electronic health record data predicted hospital admission at ED triage, with full-variable models reaching test AUCs of 0.91–0.92. Adding patient history significantly improved performance over triage information alone.
Key findings
- Among 560,486 adult ED visits with an overall admission risk of 29.7%, full-variable models achieved test AUC 0.91 for logistic regression, 0.92 for XGBoost, and 0.92 for deep neural networks; triage-only and history-only models achieved AUCs around 0.86–0.87.
- A low-dimensional XGBoost model using ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91–0.91).
- All algorithms reached maximum performance at 50% of the training set or less; logistic regression reached it at 10%, while XGBoost and DNN reached it at 50%.
- XGBoost and DNN outperformed logistic regression on the full dataset, with no significant difference between XGBoost and DNN across the three dataset types.
Methods
- Retrospective study of all adult ED visits between March 2014 and July 2017 from one academic and two community EDs that ended in admission or discharge; 972 variables were extracted per visit, and 560,486 visits were randomly partitioned into training (80%), validation (10%), and test (10%) sets.
- Nine binary classifiers were trained using logistic regression, XGBoost, and deep neural networks on three dataset types: triage information only, patient history only, and the full variable set; hyperparameters were optimized by average validation AUC across five validation sets, and test AUCs were calculated with DeLong 95% CIs.
- Additional analyses trained full-variable models on increasing fractions of the training set (1%, 10%, 30%, 50%, 80%, 100%) and used information gain from 100 XGBoost iterations to identify important variables and build a low-dimensional model.
Limitations
- The paper states that patient history was restricted to previous ED visits, and expanding to outpatient or inpatient data may improve performance but could present EHR integration and differing-standards difficulties.
- The paper states that data came from one hospital system with multiple EDs and a large catchment area, and extending the approach to dense urban areas with multiple independent EDs may be difficult because patients may not consistently present to the same system.
- The paper states that the outcome label was the ED provider’s prior disposition decision, so appropriateness of individual clinical decisions could not be assessed, and it does not address implementation or efficacy barriers in clinical practice; evident from the text, the study is retrospective, adult-only, excludes non-admission/discharge dispositions, and uses a held-out test set from the same health system rather than external validation.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86–0.87), 0.87 for XGBoost (95% CI 0.87–0.88) and 0.87 for DNN (95% CI 0.87–0.88).”
§ Predicting hospital admission at emergency department triage using machine learning > Abstract > Results
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In the paper
“the addition of patient history improves predictive performance significantly compared to using triage information alone”
§ Predicting hospital admission at emergency department triage using machine learning > Discussion
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“as did prior hospital usage statistics such as the number of admissions within the past year and the disposition of the previous ED visit.”
§ Predicting hospital admission at emergency department triage using machine learning > Discussion
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For LR, the 95% CI of the AUC of the model trained on 10% of the training set contained the AUC of the model trained on the entire training set.”
§ Predicting hospital admission at emergency department triage using machine learning > Results > Testing the benefit of additional training samples
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In the paper
“Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86–0.87), 0.87 for XGBoost (95% CI 0.87–0.87) and 0.87 for DNN (95% CI 0.87–0.88).”
§ Predicting hospital admission at emergency department triage using machine learning > Abstract > Results
Strength:plain· not yet calibratedn = 560,486Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91–0.91), 0.92 for XGBoost (95% CI 0.92–0.93) and 0.92 for DNN (95% CI 0.92–0.92).”
§ Predicting hospital admission at emergency department triage using machine learning > Abstract > Results
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In the paper
“Variables correlated with age and markers of socioeconomic status such as insurance type were some of the other features identified by our model”
§ Predicting hospital admission at emergency department triage using machine learning > Discussion
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“All algorithms reached maximum performance at 50% of the training set or less.”
§ Predicting hospital admission at emergency department triage using machine learning > Abstract > Results
Strength:plain· not yet calibratedn = 560,486Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“XGBoost and DNN outperformed LR on the full dataset, while there was no significant difference in performance between XGBoost and DNN across all three dataset types.”
§ Predicting hospital admission at emergency department triage using machine learning > Results > Model performance
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91–0.91), 0.92 for XGBoost (95% CI 0.92–0.93) and 0.92 for DNN (95% CI 0.92–0.92).”
§ Predicting hospital admission at emergency department triage using machine learning > Abstract > Results
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- It may state two separate claims as one. 5
- The quote only partly states this claim. 1
- Probably not a substantial claim. 10
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“All three algorithms reach maximum performance at 50% of the training set or less.”
- Also stated in: § Discussion · boosted
“we train our models on increasing fractions of the training set and show that the AUC plateaus well below the full training set.”
§ Predicting hospital admission at emergency department triage using machine learning > Results > Testing the benefit of additional training samples
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one. The quote only partly states this claim.
- Also stated in: § Discussion · boosted
In the paper
“While most emergency department (ED) visits end in discharge, EDs represent the largest source of hospital admissions [1].”
§ Predicting hospital admission at emergency department triage using machine learning > Introduction
What the sources say
- Not checked yetcites [1] Moore B, Stocks C, Owens P. Trends in Emergency Department Visits, 2006–2014. HCUP Statistical Brief #227 [Internet]. Agency for Healthcare Research and Quality; 2017 Sep. Available: https://www.hcup-us.ahrq.gov/reports/statbriefs/sb227-Emergency-Department-Visit-Trends.pdf
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“It is increasingly understood that ED crowding is correlated with poorer patient outcomes [10].”
§ Predicting hospital admission at emergency department triage using machine learning > Introduction
What the sources say
- Not checked yetcites [10] SL Bernstein, 2009, The Effect of Emergency Department Crowding on Clinically Oriented Outcomes, Acad Emerg Med source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“the importance of medication counts in the model may either reflect a proxy feature for medical complexity or indicate that polypharmacy itself is a risk factor [42,43].”
§ Predicting hospital admission at emergency department triage using machine learning > Discussion
What the sources say
- Not checked yetcites [42] CM Hohl, 2001, Polypharmacy, adverse drug-related events, and potential adverse drug interactions in elderly patients presenting to an emergency department, Ann Emerg Med source
- Not checked yetcites [43] A Banerjee, 2011, The prevalence of polypharmacy in elderly attenders to an emergency department—a problem with a need for an effective solution, Int J Emerg Med source
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Notably, a progressive modeling approach that uses information available at later time-points, such as lab tests ordered, medications given, and diagnoses entered by the ED provider during the patient’s current visit, has been able to achieve high predictive power and indicates the utility of these features [20,21].”
§ Predicting hospital admission at emergency department triage using machine learning > Introduction
What the sources say
- Not checked yetcites [20] Y Barak-Corren, 2017, Progressive prediction of hospitalisation in the emergency department: uncovering hidden patterns to improve patient flow, Emerg Med J source
- Not checked yetcites [21] Y Barak-Corren, 2017, Early Prediction Model of Patient Hospitalization From the Pediatric Emergency Department, Pediatrics source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets.”
- Also stated in: § Model fitting and evaluation · plain
“A randomly chosen test set of 56,000 (10%) samples was held out, then the remaining 504,486 (90%) samples split randomly five times to create five independent validation sets of 56,000 (10%) and training sets of 448,486 (80%).”
§ Predicting hospital admission at emergency department triage using machine learning > Abstract > Methods
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- Also stated in: § Model fitting and evaluation · plain
None of the sources cited for this claim have been checked yet.
In the paper
“that have been previously linked to hospital admission [46–48].”
§ Predicting hospital admission at emergency department triage using machine learning > Discussion
What the sources say
- Not checked yetcites [46] AW Selassie, 2003, The Influence of Insurance, Race, and Gender on Emergency Department Disposition, Acad Emerg Med source
- Not checked yetcites [47] Jiang HJ, Russo CA, Barrett ML. Nationwide Frequency and Costs of Potentially Preventable Hospitalizations, 2006. HCUP Statistical Brief #72 [Internet]. Agency for Healthcare Research and Quality; 2009 Apr. Available: http://www.hcup-us.ahrq.gov/reports/statbriefs/sb72.pdf
- Not checked yetcites [48] S Kangovi, 2013, Understanding Why Patients Of Low Socioeconomic Status Prefer Hospitals Over Ambulatory Care, Health Aff (Millwood) source
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“As a recent work showed that using all elements of the electronic health record can robustly predict in-patient outcomes [26]”
§ Predicting hospital admission at emergency department triage using machine learning > Introduction
What the sources say
- Not checked yetcites [26] A Rajkomar, 2018, Scalable and accurate deep learning for electronic health records, Npj Digit Med source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“Logistic regression and Naive Bayes are commonly used [9,11,16,18–22], with few studies using more complex algorithms like random-forests, artificial neural networks, and support vector machines [12,15,17].”
§ Predicting hospital admission at emergency department triage using machine learning > Introduction
What the sources say
- Not checked yetcites [9] Y Sun, 2011, Predicting hospital admissions at emergency department triage using routine administrative data, Acad Emerg Med source
- Not checked yetcites [11] JS Peck, 2012, Predicting emergency department inpatient admissions to improve same-day patient flow, Acad Emerg Med source
- Not checked yetcites [16] J Leegon, 2005, Predicting Hospital Admission for Emergency Department Patients using a Bayesian Network, AMIA Annu Symp Proc
- Not checked yetcites [18] DW Savage, 2017, P112: Predicting patient admission from the emergency department using triage administrative data, Can J Emerg Med source
- Not checked yetcites [19] JA Lucke, 2017, Early prediction of hospital admission for emergency department patients: a comparison between patients younger or older than 70 years, Emerg Med J
- Not checked yetcites [20] Y Barak-Corren, 2017, Progressive prediction of hospitalisation in the emergency department: uncovering hidden patterns to improve patient flow, Emerg Med J source
- Not checked yetcites [21] Y Barak-Corren, 2017, Early Prediction Model of Patient Hospitalization From the Pediatric Emergency Department, Pediatrics source
- Not checked yetcites [22] MM Dinh, 2016, The Sydney Triage to Admission Risk Tool (START) to predict Emergency Department Disposition: A derivation and internal validation study using retrospective state-wide data from New South Wales, Australia, BMC Emerg Med source
- Not checked yetcites [12] S Levin, 2017, Machine-Learning-Based Electronic Triage More Accurately Differentiates Patients With Respect to Clinical Outcomes Compared With the Emergency Severity Index, Ann Emerg Med
- Not checked yetcites [15] J Leegon, 2006, Predicting Hospital Admission in a Pediatric Emergency Department using an Artificial Neural Network, AMIA Annu Symp Proc
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Prior models that incorporate past medical history utilize simplified chronic disease categories such as heart disease or diabetes [9,12] while leaving out rich historical information accessible from the electronic health record (EHR) such as outpatient medications and historical labs and vitals”
§ Predicting hospital admission at emergency department triage using machine learning > Introduction
What the sources say
- Not checked yetcites [9] Y Sun, 2011, Predicting hospital admissions at emergency department triage using routine administrative data, Acad Emerg Med source
- Not checked yetcites [12] S Levin, 2017, Machine-Learning-Based Electronic Triage More Accurately Differentiates Patients With Respect to Clinical Outcomes Compared With the Emergency Severity Index, Ann Emerg Med
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“Notification of administrators and inpatient teams regarding potential admissions may help alleviate this problem [11].”
§ Predicting hospital admission at emergency department triage using machine learning > Introduction
What the sources say
- Not checked yetcites [11] JS Peck, 2012, Predicting emergency department inpatient admissions to improve same-day patient flow, Acad Emerg Med source
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
None of the sources cited for this claim have been checked yet.
In the paper
“while some models include additional features such as hospital usage statistics and past medical history [9,12,20,21].”
§ Predicting hospital admission at emergency department triage using machine learning > Introduction
What the sources say
- Not checked yetcites [9] Y Sun, 2011, Predicting hospital admissions at emergency department triage using routine administrative data, Acad Emerg Med source
- Not checked yetcites [12] S Levin, 2017, Machine-Learning-Based Electronic Triage More Accurately Differentiates Patients With Respect to Clinical Outcomes Compared With the Emergency Severity Index, Ann Emerg Med
- Not checked yetcites [20] Y Barak-Corren, 2017, Progressive prediction of hospitalisation in the emergency department: uncovering hidden patterns to improve patient flow, Emerg Med J source
- Not checked yetcites [21] Y Barak-Corren, 2017, Early Prediction Model of Patient Hospitalization From the Pediatric Emergency Department, Pediatrics source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“Lastly, this study does not address the implementation and efficacy barriers present in clinical practice [53].”
§ Predicting hospital admission at emergency department triage using machine learning > Discussion
What the sources say
- Not checked yetcites [53] SM Schenkel, 2018, Triage, Machine Learning, Algorithms, and Becoming the Borg, Ann Emerg Med
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
Gradient boosting and deep neural networks have not been applied to the task of predicting admission at ED triage to date.
“While gradient boosting and deep neural networks have been shown to be powerful tools for predictive modeling, neither has been applied to the task of predicting admission at ED triage to date.”
The paper relies on this without support.
The three EDs studied are representative of EDs generally, so the models' performance generalizes to other settings.
“Machine learning can robustly predict hospital admission using triage information and patient history.”
The paper relies on this without support.
The historical information available in the EHR at the time of triage is sufficiently complete and accurate to predict admission.
“For each patient visit, we collected a total of 972 variables, divided into major categories shown in Table 1.”
The paper relies on this without support.
The ED provider's prior decision is a valid ground truth for hospital admission.
“Throughout this study, we predict patient disposition by using the ED provider's prior decision as our true label.”
The paper relies on this without support.
The variables used in the models are available at the time of emergency department triage.
“Our study shows that machine learning can robustly predict hospital admission at emergency department (ED) triage”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“This represents state-of-the-art performance comparable with recent admission models (e.g. AUROC 0.825–0.87722–25).”
From Using explainable machine learning to characterise data drift and detect emergent health risks for emergency department admissions during COVID-19 (2021) · cites this paper as [24]
In the citing paper
“Hospital services can be managed more efficiently if accurate short-term forecasts for emergency demand are available8,9.”
From Machine learning for real-time aggregated prediction of hospital admission for emergency patients (2022) · cites this paper as [9]
In the citing paper
“Hong et al.9 showed that ML algorithms like gradient-boosted trees and deep neural networks, applied to a large EHR dataset of 972 variables, improved predictive performance.”
From Machine learning for real-time aggregated prediction of hospital admission for emergency patients (2022) · cites this paper as [9]
In the citing paper
“Using XGBoost, Barak-Corren et al.31 achieved AUROC of 0.87–0.93 with a 60 minute model, compared with 0.89 here, and Hong et al9 achieved 0.87 with triage data.”
From Machine learning for real-time aggregated prediction of hospital admission for emergency patients (2022) · cites this paper as [9]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Conflicting descriptions of how model cutoffs were chosen
MinorThe methods say Youden's index determined the cutoff for each model. Table 3 instead says every model's cutoff was set to a fixed specificity of 0.85, chosen using Youden's index only on the full XGBoost model.
“Youden's index was used to find the optimal cutoff point on the ROC curve to calculate the sensitivity, specificity, positive predictive value, and negative predictive value for each model [32,33].”
“The cutoff threshold for each model was set to match a fixed specificity of 0.85 to facilitate comparison. The value of 0.85 was chosen by using Youden’s Index on the full XGBoost model.”
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Metadata
- Authors
- Hong, W. S., Haimovich, A. D., & Taylor, R. A.
- Affiliations
- Yale University (US)
- Corresponding author
- Taylor, R. Andrew
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-07-20
- Volume
- 13
- Issue
- 7
- Pages
- e0201016
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC6054406
- OpenAlex
- W2884597820
- Semantic Scholar
- d012519a924e41aa7ff49d9b6be58033bd60fd9c
- Cited by
- 326
- References
- 58
- Citations by year
- 2026: 51 · 2025: 55 · 2024: 47 · 2023: 39 · 2022: 46 · 2021: 39 · 2020: 30 · 2019: 18 · 2018: 1
- FWCI
- 12.22
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- National Institutes of Health (1F30CA196191, T32GM007205)
- Yale School of Medicine
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Emergency Medicine › Emergency and Acute Care Studies
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- emergency department triage, hospital admission prediction, medical history, Emergency Severity Index, hospital utilization, XGBoost, deep neural network, logistic regression, feature importance, information gain, low-dimensional models, training data size, model generalization, clinical decision support
- MeSH terms
- Machine Learning, Adult, Emergency Service, Hospital, Female, Humans, Male, Middle Aged, Patient Admission, Patient Discharge, Triage, Models, Statistical
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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