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Thiruganasambandamoorthy V, Probst MA, Poterucha TJ, Sandhu RK, Toarta C, Raj SR, Sheldon R, Rahgozar A, Grant L. Role of Artificial Intelligence in Improving Syncope Management. Can J Cardiol 2024:S0828-282X(24)00429-X. [PMID: 38838932 DOI: 10.1016/j.cjca.2024.05.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 04/25/2024] [Accepted: 05/01/2024] [Indexed: 06/07/2024] Open
Abstract
Syncope is common in the general population and a common presenting symptom in acute care settings. Substantial costs are attributed to the care of patients with syncope. Current challenges include differentiating syncope from its mimickers, identifying serious underlying conditions that caused the syncope, and wide variations in current management. Although validated risk tools exist, especially for short-term prognosis, there is inconsistent application, and the current approach does not meet patient needs and expectations. Artificial intelligence (AI) techniques, such as machine learning methods including natural language processing, can potentially address the current challenges in syncope management. Preliminary evidence from published studies indicates that it is possible to accurately differentiate syncope from its mimickers and predict short-term prognosis and hospitalisation. More recently, AI analysis of electrocardiograms has shown promise in detection of serious structural and functional cardiac abnormalities, which has the potential to improve syncope care. Future AI studies have the potential to address current issues in syncope management. AI can automatically prognosticate risk in real time by accessing traditional and nontraditional data. However, steps to mitigate known problems such as generalisability, patient privacy, data protection, and liability will be needed. In the past AI has had limited impact due to underdeveloped analytical methods, lack of computing power, poor access to powerful computing systems, and availability of reliable high-quality data. All impediments except data have been solved. AI will live up to its promise to transform syncope care if the health care system can satisfy AI requirement of large scale, robust, accurate, and reliable data.
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Affiliation(s)
- Venkatesh Thiruganasambandamoorthy
- Department of Emergency Medicine, University of Ottawa, Ottawa, Ontario, Canada; Ottawa Hospital Research Institute, The Ottawa Hospital, Ottawa, Ontario, Canada; School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada.
| | - Marc A Probst
- Department of Emergency Medicine, Columbia University Irving Medical Center, New York, New York, USA
| | - Timothy J Poterucha
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
| | - Roopinder K Sandhu
- Libin Cardiovascular Institute, Department of Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Cristian Toarta
- Department of Emergency Medicine, McGill University, Montréal, Québec, Canada; McGill University Health Centre, Montréal, Québec, Canada
| | - Satish R Raj
- Libin Cardiovascular Institute, Department of Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Robert Sheldon
- Libin Cardiovascular Institute, Department of Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Arya Rahgozar
- Ottawa Hospital Research Institute, The Ottawa Hospital, Ottawa, Ontario, Canada; School of Engineering Design and Teaching Innovation, University of Ottawa, Ottawa, Ontario, Canada
| | - Lars Grant
- Department of Emergency Medicine, McGill University, Montréal, Québec, Canada; Lady Davis Research Institute, Montréal, Québec, Canada; Jewish General Hospital, Montréal, Québec, Canada
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Tyler S, Olis M, Aust N, Patel L, Simon L, Triantafyllidis C, Patel V, Lee DW, Ginsberg B, Ahmad H, Jacobs RJ. Use of Artificial Intelligence in Triage in Hospital Emergency Departments: A Scoping Review. Cureus 2024; 16:e59906. [PMID: 38854295 PMCID: PMC11158416 DOI: 10.7759/cureus.59906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Accepted: 05/08/2024] [Indexed: 06/11/2024] Open
Abstract
The integration of artificial intelligence (AI) and machine learning (ML) in healthcare has become a major point of interest and raises the question of its impact on the emergency department (ED) triaging process. AI's capacity to emulate human cognitive processes coupled with advancements in computing has shown positive outcomes in various aspects of healthcare but little is known about the use of AI in triaging patients in ED. AI algorithms may allow for earlier diagnosis and intervention; however, overconfident answers may present dangers to patients. The purpose of this review was to explore comprehensively recently published literature regarding the effect of AI and ML in ED triage and identify research gaps. A systemized search was conducted in September 2023 using the electronic databases EMBASE, Ovid MEDLINE, and Web of Science. To meet inclusion criteria, articles had to be peer-reviewed, written in English, and based on primary data research studies published in US journals 2013-2023. Other criteria included 1) studies with patients needing to be admitted to hospital EDs, 2) AI must have been used when triaging a patient, and 3) patient outcomes must be represented. The search was conducted using controlled descriptors from the Medical Subject Headings (MeSH) that included the terms "artificial intelligence" OR "machine learning" AND "emergency ward" OR "emergency care" OR "emergency department" OR "emergency room" AND "patient triage" OR "triage" OR "triaging." The search initially identified 1,142 citations. After a rigorous, systemized screening process and critical appraisal of the evidence, 29 studies were selected for the final review. The findings indicated that 1) ML models consistently demonstrated superior discrimination abilities compared to conventional triage systems, 2) the integration of AI into the triage process yielded significant enhancements in predictive accuracy, disease identification, and risk assessment, 3) ML accurately determined the necessity of hospitalization for patients requiring urgent attention, and 4) ML improved resource allocation and quality of patient care, including predicting length of stay. The suggested superiority of ML models in prioritizing patients in the ED holds the potential to redefine triage precision.
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Affiliation(s)
- Samantha Tyler
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Matthew Olis
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Nicole Aust
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Love Patel
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Leah Simon
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Catherine Triantafyllidis
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Vijay Patel
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Dong Won Lee
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Brendan Ginsberg
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Hiba Ahmad
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Robin J Jacobs
- Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
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Farimani RM, Karim H, Atashi A, Tohidinezhad F, Bahaadini K, Abu-Hanna A, Eslami S. Models to predict length of stay in the emergency department: a systematic literature review and appraisal. BMC Emerg Med 2024; 24:54. [PMID: 38575857 PMCID: PMC10996208 DOI: 10.1186/s12873-024-00965-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 03/11/2024] [Indexed: 04/06/2024] Open
Abstract
INTRODUCTION Prolonged Length of Stay (LOS) in ED (Emergency Department) has been associated with poor clinical outcomes. Prediction of ED LOS may help optimize resource utilization, clinical management, and benchmarking. This study aims to systematically review models for predicting ED LOS and to assess the reporting and methodological quality about these models. METHODS The online database PubMed, Scopus, and Web of Science (10 Sep 2023) was searched for English language articles that reported prediction models of LOS in ED. Identified titles and abstracts were independently screened by two reviewers. All original papers describing either development (with or without internal validation) or external validation of a prediction model for LOS in ED were included. RESULTS Of 12,193 uniquely identified articles, 34 studies were included (29 describe the development of new models and five describe the validation of existing models). Different statistical and machine learning methods were applied to the papers. On the 39-point reporting score and 11-point methodological quality score, the highest reporting scores for development and validation studies were 39 and 8, respectively. CONCLUSION Various studies on prediction models for ED LOS were published but they are fairly heterogeneous and suffer from methodological and reporting issues. Model development studies were associated with a poor to a fair level of methodological quality in terms of the predictor selection approach, the sample size, reproducibility of the results, missing imputation technique, and avoiding dichotomizing continuous variables. Moreover, it is recommended that future investigators use the confirmed checklist to improve the quality of reporting.
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Affiliation(s)
| | - Hesam Karim
- Department of Health Information Management, Faculty of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Alireza Atashi
- E-Health Department, Virtual School, Tehran University of Medical Sciences, Tehran, Iran
| | - Fariba Tohidinezhad
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Kambiz Bahaadini
- Department of Medical Informatics, Kerman University of Medical Sciences, Kerman, Iran
| | - Ameen Abu-Hanna
- Medical Informatics, UMC Location University of Amsterdam, Meibergdreef, Amsterdam, The Netherlands
- Amsterdam Public Health, Amsterdam, The Netherlands
| | - Saeid Eslami
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
- Medical Informatics, UMC Location University of Amsterdam, Meibergdreef, Amsterdam, The Netherlands.
- Pharmaceutical Research Center, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran.
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Dipaola F, Gatti M, Menè R, Shiffer D, Giaj Levra A, Solbiati M, Villa P, Costantino G, Furlan R. A Hybrid Model for 30-Day Syncope Prognosis Prediction in the Emergency Department. J Pers Med 2023; 14:4. [PMID: 38276219 PMCID: PMC10817569 DOI: 10.3390/jpm14010004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 12/06/2023] [Accepted: 12/11/2023] [Indexed: 01/27/2024] Open
Abstract
Syncope is a challenging problem in the emergency department (ED) as the available risk prediction tools have suboptimal predictive performances. Predictive models based on machine learning (ML) are promising tools whose application in the context of syncope remains underexplored. The aim of the present study was to develop and compare the performance of ML-based models in predicting the risk of clinically significant outcomes in patients presenting to the ED for syncope. We enrolled 266 consecutive patients (age 73, IQR 58-83; 52% males) admitted for syncope at three tertiary centers. We collected demographic and clinical information as well as the occurrence of clinically significant outcomes at a 30-day telephone follow-up. We implemented an XGBoost model based on the best-performing candidate predictors. Subsequently, we integrated the XGboost predictors with knowledge-based rules. The obtained hybrid model outperformed the XGboost model (AUC = 0.81 vs. 0.73, p < 0.001) with acceptable calibration. In conclusion, we developed an ML-based model characterized by a commendable capability to predict adverse events within 30 days post-syncope evaluation in the ED. This model relies solely on clinical data routinely collected during a patient's initial syncope evaluation, thus obviating the need for laboratory tests or syncope experienced clinical judgment.
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Affiliation(s)
- Franca Dipaola
- Internal Medicine, Syncope Unit, IRCCS Humanitas Research Hospital, 20089 Milan, Italy;
| | | | - Roberto Menè
- Department of Medicine and Surgery, University of Milano-Bicocca, 20100 Milan, Italy;
| | - Dana Shiffer
- Emergency Department, IRCCS Humanitas Research Hospital, 20089 Milan, Italy;
- Department of Biomedical Sciences, Humanitas University, 20072 Milan, Italy;
| | | | - Monica Solbiati
- Emergency Department, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Università Degli Studi Di Milano, 20100 Milan, Italy; (M.S.); (G.C.)
| | - Paolo Villa
- Emergency Medicine Unit, Luigi Sacco Hospital, ASST Fatebenefratelli Sacco, 20100 Milan, Italy;
| | - Giorgio Costantino
- Emergency Department, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Università Degli Studi Di Milano, 20100 Milan, Italy; (M.S.); (G.C.)
| | - Raffaello Furlan
- Internal Medicine, Syncope Unit, IRCCS Humanitas Research Hospital, 20089 Milan, Italy;
- Department of Biomedical Sciences, Humanitas University, 20072 Milan, Italy;
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Olshansky B, Gebska MA, Johnston SL. Syncope-Do We Need AI? J Pers Med 2023; 13:jpm13050740. [PMID: 37240910 DOI: 10.3390/jpm13050740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 04/23/2023] [Indexed: 05/28/2023] Open
Abstract
Syncope is a form of transient loss of consciousness (TLOC) resulting from cerebral hypoperfusion and is characterized by rapid onset, short duration and spontaneous complete recovery [...].
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Affiliation(s)
- Brian Olshansky
- Division of Cardiovascular Medicine, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA
| | - Milena A Gebska
- Division of Cardiovascular Medicine, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA
| | - Samuel L Johnston
- Division of Cardiovascular Medicine, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA
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Zou H, Yang W, Wang M, Zhu Q, Liang H, Wu H, Tang L. Predicting length of stay ranges by using novel deep neural networks. Heliyon 2023; 9:e13573. [PMID: 36852025 PMCID: PMC9958433 DOI: 10.1016/j.heliyon.2023.e13573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 01/31/2023] [Accepted: 02/02/2023] [Indexed: 02/11/2023] Open
Abstract
Background and aims Accurately predicting length of stay (LOS) is considered a challenging task for health care systems globally. In previous studies on LOS range prediction, researchers commonly pre-classified the LOS ranges, which were the same for all patients in the same classification, and then utilized a classifier for prediction. In this study, we innovatively aimed to predict the specific LOS range for each patient (the LOS range was different for each patient). Methods In the modified deep neural network (DNN), the overall sample error (root mean square error (RMSE) method), the estimated sample error (ERRpred method), the probability distribution with different loss functions (Dispred_Loss1, Dispred_Loss2, and Dispred_Loss3 method), and the generative adversarial networks (WGAN-GP for LOS method) are used for LOS range prediction. The Medical Information Mart for Intensive Care III (MIMIC-III) database is used to validate these methods. Results The RMSE method is convenient for LOS range prediction, but the predicted ranges are all consistent in the same batch of samples. The ERRpred method can achieve better prediction results in samples with low errors. However, the prediction effect is worse in samples with larger errors. The Dispred_Loss1 method encounters a training instability problem. The Dispred_Loss2 and Dispred_Loss3 methods perform well in making predictions. Although WGAN-GP for LOS method does not show a substantial advantage over other methods, this method might have the potential to improve the predictive performance. Conclusion The results show that it is possible to achieve an acceptable accurate LOS range prediction through a reasonable model design, which may help physicians in the clinic.
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Affiliation(s)
- Hong Zou
- Department of General Surgery, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China.,Department of Liver Surgery & Liver Transplantation, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu 610044, Sichuan Province, China
| | - Wei Yang
- Department of Urology, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China
| | - Meng Wang
- Department of Traditional Chinese Medicine, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China
| | - Qiao Zhu
- Department of Obstetrics and Gynecology, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China
| | - Hongyin Liang
- Department of General Surgery, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China
| | - Hong Wu
- Department of Liver Surgery & Liver Transplantation, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu 610044, Sichuan Province, China
| | - Lijun Tang
- Department of General Surgery, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China
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