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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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Aamir A, Jamil Y, Bilal M, Diwan M, Nashwan AJ, Ullah I. Artificial Intelligence in Enhancing Syncope Management - An Update. Curr Probl Cardiol 2024; 49:102079. [PMID: 37716544 DOI: 10.1016/j.cpcardiol.2023.102079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 09/11/2023] [Indexed: 09/18/2023]
Abstract
This review looks into the use of Artificial Intelligence (AI) in the management of syncope, a condition characterized by a brief loss of consciousness caused by cerebral hypoperfusion. With rising prevalence, high costs, and difficulty in diagnosis and risk stratification, syncope poses significant healthcare challenges. AI has the potential to improve symptom differentiation, risk assessment, and patient management. Machine learning, specifically Artificial Neural Networks, has shown promise in accurate risk stratification. AI-powered clinical decision support tools can improve patient evaluation and resource utilization. While AI holds great promise for syncope management, challenges such as data quality, class imbalance, and defining risk categories remain. Ethical concerns about patient privacy, as well as the need for human empathy, complicate AI integration. Collaboration among data scientists, clinicians, and ethics experts is critical for the successful implementation of AI, which has the potential to improve patient outcomes and healthcare efficiency in syncope management.
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Affiliation(s)
- Alifiya Aamir
- Dow University of Health Sciences, Karachi, Pakistan
| | - Yumna Jamil
- Dow University of Health Sciences, Karachi, Pakistan
| | - Maham Bilal
- Dow University of Health Sciences, Karachi, Pakistan
| | | | | | - Irfan Ullah
- Kabir Medical College, Gandhara University, Peshawar, Pakistan; Department of Internal Medicine, Khyber Teaching Hospital, Peshawar, Pakistan
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Svennberg E, Caiani EG, Bruining N, Desteghe L, Han JK, Narayan SM, Rademakers FE, Sanders P, Duncker D. The digital journey: 25 years of digital development in electrophysiology from an Europace perspective. Europace 2023; 25:euad176. [PMID: 37622574 PMCID: PMC10450797 DOI: 10.1093/europace/euad176] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 06/03/2023] [Indexed: 08/26/2023] Open
Abstract
AIMS Over the past 25 years there has been a substantial development in the field of digital electrophysiology (EP) and in parallel a substantial increase in publications on digital cardiology.In this celebratory paper, we provide an overview of the digital field by highlighting publications from the field focusing on the EP Europace journal. RESULTS In this journey across the past quarter of a century we follow the development of digital tools commonly used in the clinic spanning from the initiation of digital clinics through the early days of telemonitoring, to wearables, mobile applications, and the use of fully virtual clinics. We then provide a chronicle of the field of artificial intelligence, a regulatory perspective, and at the end of our journey provide a future outlook for digital EP. CONCLUSION Over the past 25 years Europace has published a substantial number of papers on digital EP, with a marked expansion in digital publications in recent years.
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Affiliation(s)
- Emma Svennberg
- Department of Medicine Huddinge, Karolinska Institutet, Karolinska University Hospital Huddinge, SE-141 86 Stockholm, Sweden
| | - Enrico G Caiani
- Politecnico di Milano, Electronic, Information and Biomedical Engineering Department, Milan, Italy
- Istituto Auxologico Italiano IRCCS, Milan, Italy
| | - Nico Bruining
- Department of Clinical and Experimental Information processing (Digital Cardiology), Erasmus Medical Center, Thoraxcenter, Rotterdam, The Netherlands
| | - Lien Desteghe
- Research Group Cardiovascular Diseases, University of Antwerp, 2000 Antwerp, Belgium
- Department of Cardiology, Antwerp University Hospital, 2056 Edegem, Belgium
- Faculty of Medicine and Life Sciences, Hasselt University, 3500 Hasselt, Belgium
- Department of Cardiology, Heart Centre Hasselt, Jessa Hospital, 3500 Hasselt, Belgium
| | - Janet K Han
- Division of Cardiology, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA 90073, USA
- Cardiac Arrhythmia Center, University of California Los Angeles, Los Angeles, CA, USA
| | - Sanjiv M Narayan
- Cardiology Division, Cardiovascular Institute and Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
| | | | - Prashanthan Sanders
- Centre for Heart Rhythm Disorders, University of Adelaide and Royal Adelaide Hospital, 5005 Adelaide, Australia
| | - David Duncker
- Hannover Heart Rhythm Center, Department of Cardiology and Angiology, Hannover Medical School, Hannover, Germany
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Cheung HC, De Louche C, Komorowski M. Artificial Intelligence Applications in Space Medicine. Aerosp Med Hum Perform 2023; 94:610-622. [PMID: 37501303 DOI: 10.3357/amhp.6178.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
INTRODUCTION:During future interplanetary space missions, a number of health conditions may arise, owing to the hostile environment of space and the myriad of stressors experienced by the crew. When managing these conditions, crews will be required to make accurate, timely clinical decisions at a high level of autonomy, as telecommunication delays and increasing distances restrict real-time support from the ground. On Earth, artificial intelligence (AI) has proven successful in healthcare, augmenting expert clinical decision-making or enhancing medical knowledge where it is lacking. Similarly, deploying AI tools in the context of a space mission could improve crew self-reliance and healthcare delivery.METHODS: We conducted a narrative review to discuss existing AI applications that could improve the prevention, recognition, evaluation, and management of the most mission-critical conditions, including psychological and mental health, acute radiation sickness, surgical emergencies, spaceflight-associated neuro-ocular syndrome, infections, and cardiovascular deconditioning.RESULTS: Some examples of the applications we identified include AI chatbots designed to prevent and mitigate psychological and mental health conditions, automated medical imaging analysis, and closed-loop systems for hemodynamic optimization. We also discuss at length gaps in current technologies, as well as the key challenges and limitations of developing and deploying AI for space medicine to inform future research and innovation. Indeed, shifts in patient cohorts, space-induced physiological changes, limited size and breadth of space biomedical datasets, and changes in disease characteristics may render the models invalid when transferred from ground settings into space.Cheung HC, De Louche C, Komorowski M. Artificial intelligence applications in space medicine. Aerosp Med Hum Perform. 2023; 94(8):610-622.
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Dipaola F, Gatti M, Giaj Levra A, Menè R, Shiffer D, Faccincani R, Raouf Z, Secchi A, Rovere Querini P, Voza A, Badalamenti S, Solbiati M, Costantino G, Savevski V, Furlan R. Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study. Sci Rep 2023; 13:10868. [PMID: 37407595 DOI: 10.1038/s41598-023-37512-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 06/22/2023] [Indexed: 07/07/2023] Open
Abstract
Predicting clinical deterioration in COVID-19 patients remains a challenging task in the Emergency Department (ED). To address this aim, we developed an artificial neural network using textual (e.g. patient history) and tabular (e.g. laboratory values) data from ED electronic medical reports. The predicted outcomes were 30-day mortality and ICU admission. We included consecutive patients from Humanitas Research Hospital and San Raffaele Hospital in the Milan area between February 20 and May 5, 2020. We included 1296 COVID-19 patients. Textual predictors consisted of patient history, physical exam, and radiological reports. Tabular predictors included age, creatinine, C-reactive protein, hemoglobin, and platelet count. TensorFlow tabular-textual model performance indices were compared to those of models implementing only tabular data. For 30-day mortality, the combined model yielded slightly better performances than the tabular fastai and XGBoost models, with AUC 0.87 ± 0.02, F1 score 0.62 ± 0.10 and an MCC 0.52 ± 0.04 (p < 0.32). As for ICU admission, the combined model MCC was superior (p < 0.024) to the tabular models. Our results suggest that a combined textual and tabular model can effectively predict COVID-19 prognosis which may assist ED physicians in their decision-making process.
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Affiliation(s)
- Franca Dipaola
- Internal Medicine, Humanitas Clinical and Research Center, IRCCS, Humanitas Research Hospital, Humanitas University, Via A. Manzoni, 56, 20089, Rozzano, Milan, Italy
| | | | - Alessandro Giaj Levra
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Italy
- IRCCS Humanitas Research Hospital, Via A. Manzoni, 56, 20089, Rozzano, Milan, Italy
| | - Roberto Menè
- Department of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
- Heart Rhythm Department, Clinique Pasteur, Toulouse, France
| | - Dana Shiffer
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Italy
| | - Roberto Faccincani
- Emergency Department, Humanitas Mater Domini, Castellanza, Varese, Italy
| | - Zainab Raouf
- IRCCS-Ospedale San Raffaele, Università Vita-Salute San Raffaele, Milan, Italy
| | - Antonio Secchi
- IRCCS-Ospedale San Raffaele, Università Vita-Salute San Raffaele, Milan, Italy
| | | | - Antonio Voza
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Italy
- Emergency Department, IRCCS - Humanitas Clinical and Research Center, Via Manzoni 56, Rozzano, Italy
| | - Salvatore Badalamenti
- Internal Medicine, Humanitas Clinical and Research Center, IRCCS, Humanitas Research Hospital, Humanitas University, Via A. Manzoni, 56, 20089, Rozzano, Milan, Italy
| | - Monica Solbiati
- Emergency Department, Fondazione IRCCS Ca' Granda, Ospedale Maggiore, Milan, Italy
| | - Giorgio Costantino
- Emergency Department, Fondazione IRCCS Ca' Granda, Ospedale Maggiore, Milan, Italy
| | - Victor Savevski
- AI Center, IRCCS - Humanitas Research Hospital, Via Manzoni 56, Rozzano, Italy
| | - Raffaello Furlan
- Internal Medicine, Humanitas Clinical and Research Center, IRCCS, Humanitas Research Hospital, Humanitas University, Via A. Manzoni, 56, 20089, Rozzano, Milan, Italy.
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Italy.
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Statz GM, Evans AZ, Johnston SL, Adhaduk M, Mudireddy AR, Sonka M, Lee S, Barsotti EJ, Ricci F, Dipaola F, Johansson M, Sheldon RS, Thiruganasambandamoorthy V, Kenny RA, Bullis TC, Pasupula DK, Van Heukelom J, Gebska MA, Olshansky B. Can Artificial Intelligence Enhance Syncope Management?: A JACC: Advances Multidisciplinary Collaborative Statement. JACC. ADVANCES 2023; 2:100323. [PMID: 38939607 PMCID: PMC11198330 DOI: 10.1016/j.jacadv.2023.100323] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 02/24/2023] [Indexed: 06/29/2024]
Abstract
Syncope, a form of transient loss of consciousness, remains a complex medical condition for which adverse cardiovascular outcomes, including death, are of major concern but rarely occur. Current risk stratification algorithms have not completely delineated which patients benefit from hospitalization and specific interventions. Patients are often admitted unnecessarily and at high cost. Artificial intelligence (AI) and machine learning may help define the transient loss of consciousness event, diagnose the cause, assess short- and long-term risks, predict recurrence, and determine need for hospitalization and therapeutic intervention; however, several challenges remain, including medicolegal and ethical concerns. This collaborative statement, from a multidisciplinary group of clinicians, investigators, and scientists, focuses on the potential role of AI in syncope management with a goal to inspire creation of AI-derived clinical decision support tools that may improve patient outcomes, streamline diagnostics, and reduce health-care costs.
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Affiliation(s)
- Giselle M. Statz
- Division of Cardiovascular Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
| | - Aron Z. Evans
- Department of Internal Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
| | - Samuel L. Johnston
- Division of Cardiovascular Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
| | - Mehul Adhaduk
- Department of Internal Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
| | - Avinash R. Mudireddy
- The Iowa Initiative for Artificial Intelligence, University of Iowa, Iowa City, Iowa, USA
| | - Milan Sonka
- The Iowa Initiative for Artificial Intelligence, University of Iowa, Iowa City, Iowa, USA
| | - Sangil Lee
- Department of Emergency Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
| | - E. John Barsotti
- Department of Epidemiology, College of Public Health, University of Iowa, Iowa City, Iowa, USA
| | - Fabrizio Ricci
- Department of Neurosciences, Imaging and Clinical Sciences, Institute for Advanced Biomedical Technologies, University G. d’Annunzio, Chieti, Italy
| | - Franca Dipaola
- Internal Medicine, Syncope Unit, IRCCS Humanitas Research Hospital, Humanitas University, Rozzano, Milan, Italy
| | - Madeleine Johansson
- Department of Cardiology, Skåne University Hospital, Lund University, Malmo, Sweden
| | - Robert S. Sheldon
- Department of Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
| | | | - Rose-Anne Kenny
- Department of Medical Gerontology, School of Medicine, Trinity College, Dublin, Ireland
| | - Tyler C. Bullis
- Department of Internal Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
| | - Deepak K. Pasupula
- Division of Cardiovascular Disease, Department of Internal Medicine, MercyOne North Iowa Heart Center, Mason City, Iowa, USA
| | - Jon Van Heukelom
- Department of Emergency Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
| | - Milena A. Gebska
- Department of Internal Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
| | - Brian Olshansky
- Department of Internal Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
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Lee S, Reddy Mudireddy A, Kumar Pasupula D, Adhaduk M, Barsotti EJ, Sonka M, Statz GM, Bullis T, Johnston SL, Evans AZ, Olshansky B, Gebska MA. Novel Machine Learning Approach to Predict and Personalize Length of Stay for Patients Admitted with Syncope from the Emergency Department. J Pers Med 2022; 13:jpm13010007. [PMID: 36675668 PMCID: PMC9864075 DOI: 10.3390/jpm13010007] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/25/2022] [Accepted: 12/12/2022] [Indexed: 12/24/2022] Open
Abstract
Background: Syncope, a common problem encountered in the emergency department (ED), has a multitude of causes ranging from benign to life-threatening. Hospitalization may be required, but the management can vary substantially depending on specific clinical characteristics. Models predicting admission and hospitalization length of stay (LoS) are lacking. The purpose of this study was to design an effective, exploratory model using machine learning (ML) technology to predict LoS for patients presenting with syncope. Methods: This was a retrospective analysis using over 4 million patients from the National Emergency Department Sample (NEDS) database presenting to the ED with syncope between 2016−2019. A multilayer perceptron neural network with one hidden layer was trained and validated on this data set. Results: Receiver Operator Characteristics (ROC) were determined for each of the five ANN models with varying cutoffs for LoS. A fair area under the curve (AUC of 0.78) to good (AUC of 0.88) prediction performance was achieved based on sequential analysis at different cutoff points, starting from the same day discharge and ending at the longest analyzed cutoff LoS ≤7 days versus >7 days, accordingly. The ML algorithm showed significant sensitivity and specificity in predicting short (≤48 h) versus long (>48 h) LoS, with an AUC of 0.81. Conclusions: Using variables available to triaging ED clinicians, ML shows promise in predicting hospital LoS with fair to good performance for patients presenting with syncope.
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Affiliation(s)
- Sangil Lee
- Department of Emergency Medicine, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA
- Correspondence: (S.L.); (M.S.); (B.O.); (M.A.G.)
| | - Avinash Reddy Mudireddy
- The Iowa Initiative of Artificial Intelligence, University of Iowa, 103 South Capitol Street, Iowa City, IA 52242, USA;
| | - Deepak Kumar Pasupula
- Division of Cardiology, Mercy One North Iowa Heart Center, 250 S Crescent Dr, Mason City, IA 50401, USA;
| | - Mehul Adhaduk
- Department of Internal Medicine, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA; (M.A.); (T.B.); (A.Z.E.)
| | - E. John Barsotti
- Department of Epidemiology, College of Public Health, University of Iowa, 145 N. Riverside Drive, Iowa City, IA 52242, USA;
| | - Milan Sonka
- The Iowa Initiative of Artificial Intelligence, University of Iowa, 103 South Capitol Street, Iowa City, IA 52242, USA;
- Correspondence: (S.L.); (M.S.); (B.O.); (M.A.G.)
| | - Giselle M. Statz
- Division of Cardiovascular Medicine, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA; (G.M.S.); (S.L.J.)
| | - Tyler Bullis
- Department of Internal Medicine, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA; (M.A.); (T.B.); (A.Z.E.)
| | - Samuel L. Johnston
- Division of Cardiovascular Medicine, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA; (G.M.S.); (S.L.J.)
| | - Aron Z. Evans
- Department of Internal Medicine, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA; (M.A.); (T.B.); (A.Z.E.)
| | - Brian Olshansky
- Division of Cardiovascular Medicine, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA; (G.M.S.); (S.L.J.)
- Correspondence: (S.L.); (M.S.); (B.O.); (M.A.G.)
| | - Milena A. Gebska
- Division of Cardiovascular Medicine, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA; (G.M.S.); (S.L.J.)
- Correspondence: (S.L.); (M.S.); (B.O.); (M.A.G.)
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Ekelund U, de Capretz PO. Moving forward with machine learning models in acute chest pain. Lancet Digit Health 2022; 4:e291-e292. [DOI: 10.1016/s2589-7500(22)00046-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 03/08/2022] [Indexed: 10/18/2022]
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Ippoliti R, Falavigna G, Zanelli C, Bellini R, Numico G. Neural networks and hospital length of stay: an application to support healthcare management with national benchmarks and thresholds. COST EFFECTIVENESS AND RESOURCE ALLOCATION 2021; 19:67. [PMID: 34627288 PMCID: PMC8502324 DOI: 10.1186/s12962-021-00322-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 09/23/2021] [Indexed: 11/25/2022] Open
Abstract
Background The problem of correct inpatient scheduling is extremely significant for healthcare management. Extended length of stay can have negative effects on the supply of healthcare treatments, reducing patient accessibility and creating missed opportunities to increase hospital revenues by means of other treatments and additional hospitalizations. Methods Adopting available national reference values and focusing on a Department of Internal and Emergency Medicine located in the North-West of Italy, this work assesses prediction models of hospitalizations with length of stay longer than the selected benchmarks and thresholds. The prediction models investigated in this case study are based on Artificial Neural Networks and examine risk factors for prolonged hospitalizations in 2018. With respect current alternative approaches (e.g., logistic models), Artificial Neural Networks give the opportunity to identify whether the model will maximize specificity or sensitivity. Results Our sample includes administrative data extracted from the hospital database, collecting information on more than 16,000 hospitalizations between January 2018 and December 2019. Considering the overall department in 2018, 40% of the hospitalizations lasted more than the national average, and almost 3.74% were outliers (i.e., they lasted more than the threshold). According to our results, the adoption of the prediction models in 2019 could reduce the average length of stay by up to 2 days, guaranteeing more than 2000 additional hospitalizations in a year. Conclusions The proposed models might represent an effective tool for administrators and medical professionals to predict the outcome of hospital admission and design interventions to improve hospital efficiency and effectiveness.
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Affiliation(s)
- Roberto Ippoliti
- Faculty of Business Administration and Economics, Bielefeld University, Bielefeld, North Rhine-Westphalia, Germany.
| | - Greta Falavigna
- Research Institute on Sustainable Economic Growth (IRCrES), National Research Council of Italy (CNR), Moncalieri, TO, Italy
| | - Cristian Zanelli
- Quality and Management Control Unit, Azienda Ospedaliera SS Antonio e Biagio e Cesare Arrigo, Alessandria, AL, Italy
| | - Roberta Bellini
- Quality and Management Control Unit, Azienda Ospedaliera SS Antonio e Biagio e Cesare Arrigo, Alessandria, AL, Italy
| | - Gianmauro Numico
- Medical Oncology Unit, Azienda Ospedaliera Santa Croce e Carle, Cuneo, CN, Italy
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Dipaola F, Shiffer D, Gatti M, Menè R, Solbiati M, Furlan R. Machine Learning and Syncope Management in the ED: The Future Is Coming. ACTA ACUST UNITED AC 2021; 57:medicina57040351. [PMID: 33917508 PMCID: PMC8067452 DOI: 10.3390/medicina57040351] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Revised: 03/30/2021] [Accepted: 04/02/2021] [Indexed: 11/16/2022]
Abstract
In recent years, machine learning (ML) has been promisingly applied in many fields of clinical medicine, both for diagnosis and prognosis prediction. Aims of this narrative review were to summarize the basic concepts of ML applied to clinical medicine and explore its main applications in the emergency department (ED) setting, with a particular focus on syncope management. Through an extensive literature search in PubMed and Embase, we found increasing evidence suggesting that the use of ML algorithms can improve ED triage, diagnosis, and risk stratification of many diseases. However, the lacks of external validation and reliable diagnostic standards currently limit their implementation in clinical practice. Syncope represents a challenging problem for the emergency physician both because its diagnosis is not supported by specific tests and the available prognostic tools proved to be inefficient. ML algorithms have the potential to overcome these limitations and, in the future, they could support the clinician in managing syncope patients more efficiently. However, at present only few studies have addressed this issue, albeit with encouraging results.
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Affiliation(s)
- Franca Dipaola
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy; (D.S.); (R.F.)
- Internal Medicine, Humanitas Clinical and Research Center—IRCCS, Rozzano, 20089 Milan, Italy
- Correspondence: ; Tel.: +39-0282247266
| | - Dana Shiffer
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy; (D.S.); (R.F.)
| | - Mauro Gatti
- IBM, Active Intelligence Center, 40121 Bologna, Italy;
| | - Roberto Menè
- Department of Medicine and Surgery, University of Milano-Bicocca, 20126 Milan, Italy;
| | - Monica Solbiati
- Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy;
- Dipartimento di Scienze Cliniche e di Comunità, Università degli Studi di Milano, 20122 Milan, Italy
| | - Raffaello Furlan
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy; (D.S.); (R.F.)
- Internal Medicine, Humanitas Clinical and Research Center—IRCCS, Rozzano, 20089 Milan, Italy
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11
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Kirubarajan A, Taher A, Khan S, Masood S. Artificial intelligence in emergency medicine: A scoping review. J Am Coll Emerg Physicians Open 2020; 1:1691-1702. [PMID: 33392578 PMCID: PMC7771825 DOI: 10.1002/emp2.12277] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Revised: 09/04/2020] [Accepted: 09/22/2020] [Indexed: 01/08/2023] Open
Abstract
INTRODUCTION Despite the growing investment in and adoption of artificial intelligence (AI) in medicine, the applications of AI in an emergency setting remain unclear. This scoping review seeks to identify available literature regarding the applications of AI in emergency medicine. METHODS The scoping review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for scoping reviews using Medline-OVID, EMBASE, CINAHL, and IEEE, with a double screening and extraction process. The search included articles published until February 28, 2020. Articles were excluded if they did not self-classify as studying an AI intervention, were not relevant to the emergency department (ED), or did not report outcomes or evaluation. RESULTS Of the 1483 original database citations, 395 were eligible for full-text evaluation. Of these articles, a total of 150 were included in the scoping review. The majority of included studies were retrospective in nature (n = 124, 82.7%), with only 3 (2.0%) prospective controlled trials. We found 37 (24.7%) interventions aimed at improving diagnosis within the ED. Among the 150 studies, 19 (12.7%) focused on diagnostic imaging within the ED. A total of 16 (10.7%) studies were conducted in the out-of-hospital environment (eg, emergency medical services, paramedics) with the remainder occurring either in the ED or the trauma bay. Of the 24 (16%) studies that had human comparators, there were 12 (8%) studies in which AI interventions outperformed clinicians in at least 1 measured outcome. CONCLUSION AI-related research is rapidly increasing in emergency medicine. There are several promising AI interventions that can improve emergency care, particularly for acute radiographic imaging and prediction-based diagnoses. Higher quality evidence is needed to further assess both short- and long-term clinical outcomes.
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Affiliation(s)
- Abirami Kirubarajan
- Faculty of MedicineUniversity of TorontoTorontoOntarioCanada
- Institute of Health Policy Management and EvaluationUniversity of TorontoTorontoOntarioCanada
| | - Ahmed Taher
- Division of Emergency Medicine, Department of MedicineUniversity of TorontoTorontoOntarioCanada
| | - Shawn Khan
- Faculty of MedicineUniversity of TorontoTorontoOntarioCanada
| | - Sameer Masood
- Division of Emergency Medicine, Department of MedicineUniversity of TorontoTorontoOntarioCanada
- Toronto General Hospital Research InstituteUniversity Health NetworkTorontoOntarioCanada
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12
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Falavigna G. Prediction of general medical admission length of stay with natural language processing and deep learning: a pilot study. Intern Emerg Med 2020; 15:917-918. [PMID: 32062745 DOI: 10.1007/s11739-020-02291-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 01/30/2020] [Indexed: 11/27/2022]
Affiliation(s)
- Greta Falavigna
- Research Institute on Sustainable Economic Growth of Italian National Council of Research (IRCrES-CNR), Via Real Collegio 30, 10024, Moncalieri, TO, Italy.
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13
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Tsoi KKF, Chan NB, Yiu KKL, Poon SKS, Lin B, Ho K. Machine Learning Clustering for Blood Pressure Variability Applied to Systolic Blood Pressure Intervention Trial (SPRINT) and the Hong Kong Community Cohort. Hypertension 2020; 76:569-576. [PMID: 32594794 DOI: 10.1161/hypertensionaha.119.14213] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Visit-to-visit blood pressure variability (BPV) has been shown to be a predictor of cardiovascular disease. We aimed to classify the BPV levels using different machine learning algorithms. Visit-to-visit blood pressure readings were extracted from the SPRINT study in the United States and eHealth cohort in Hong Kong (HK cohort). Patients were clustered into low, medium, and high BPV levels with the traditional quantile clustering and 5 machine learning algorithms including K-means. Clustering methods were assessed by Stability Index. Similarities were assessed by Davies-Bouldin Index and Silhouette Index. Cox proportional hazard regression models were fitted to compare the risk of myocardial infarction, stroke, and heart failure. A total of 8133 participants had average blood pressure measurement 14.7 times in 3.28 years in SPRINT and 1094 participants who had average blood pressure measurement 165.4 times in 1.37 years in HK cohort. Quantile clustering assigned one-third participants as high BPV level, but machine learning methods only assigned 10% to 27%. Quantile clustering is the most stable method (stability index: 0.982 in the SPRINT and 0.948 in the HK cohort) with some levels of clustering similarities (Davies-Bouldin Index: 0.752 and 0.764, respectively). K-means clustering is the most stable across the machine learning algorithms (stability index: 0.975 and 0.911, respectively) with the lowest clustering similarities (Davies-Bouldin Index: 0.653 and 0.680, respectively). One out of 7 in the population was classified with high BPV level, who showed to have higher risk of stroke and heart failure. Machine learning methods can improve BPV classification for better prediction of cardiovascular diseases.
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Affiliation(s)
- Kelvin K F Tsoi
- From the Stanley Ho Big Data Decision Analytics Research Centre (K.K.F.T., N.B.C.), The Chinese University of Hong Kong.,School of Public Health and Primary Care (K.K.F.T., K.K.L.Y.), The Chinese University of Hong Kong
| | - Nicholas B Chan
- From the Stanley Ho Big Data Decision Analytics Research Centre (K.K.F.T., N.B.C.), The Chinese University of Hong Kong
| | - Karen K L Yiu
- School of Public Health and Primary Care (K.K.F.T., K.K.L.Y.), The Chinese University of Hong Kong
| | - Simon K S Poon
- School of Information Technologies, The University of Sydney, Australia (S.K.S.P.)
| | - Bryant Lin
- School of Medicine, Stanford University (B.L.)
| | - Kendall Ho
- The University of British Columbia, Canada (K.H.)
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14
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Falavigna G, Costantino G, Furlan R, Quinn JV, Ungar A, Ippoliti R. Artificial neural networks and risk stratification in emergency departments. Intern Emerg Med 2019; 14:291-299. [PMID: 30353271 DOI: 10.1007/s11739-018-1971-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Accepted: 10/16/2018] [Indexed: 11/28/2022]
Abstract
Emergency departments are characterized by the need for quick diagnosis under pressure. To select the most appropriate treatment, a series of rules to support decision-making has been offered by scientific societies. The effectiveness of these rules affects the appropriateness of treatment and the hospitalization of patients. Analyzing a sample of 1844 patients and focusing on the decision to hospitalize a patient after a syncope event to prevent severe short-term outcomes, this work proposes a new algorithm based on neural networks. Artificial neural networks are a non-parametric technique with the well-known ability to generalize behaviors, and they can thus predict severe short-term outcomes with pre-selected levels of sensitivity and specificity. This innovative technique can outperform the traditional models, since it does not require a specific functional form, i.e., the data are not supposed to be distributed following a specific design. Based on our results, the innovative model can predict hospitalization with a sensitivity of 100% and a specificity of 79%, significantly increasing the appropriateness of medical treatment and, as a result, hospital efficiency. According to Garson's Indexes, the most significant variables are exertion, the absence of symptoms, and the patient's gender. On the contrary, cardio-vascular history, hypertension, and age have the lowest impact on the determination of the subject's health status. The main application of this new technology is the adoption of smart solutions (e.g., a mobile app) to customize the stratification of patients admitted to emergency departments (ED)s after a syncope event. Indeed, the adoption of these smart solutions gives the opportunity to customize risk stratification according to the specific clinical case (i.e., the patient's health status) and the physician's decision-making process (i.e., the desired levels of sensitivity and specificity). Moreover, a decision-making process based on these smart solutions might ensure a more effective use of available resources, improving the management of syncope patients and reducing the cost of inappropriate treatment and hospitalization.
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Affiliation(s)
- Greta Falavigna
- CNR-IRCrES, Research Institute on Sustainable Economic Growth, Moncalieri, Italy.
| | - Giorgio Costantino
- Clinical Medicine Department, Fondazione IRCCS, Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Raffaello Furlan
- Department of Biomedical Sciences, Humanitas University, Humanitas Research Hospital, Rozzano, Italy
| | - James V Quinn
- Division of Emergency Medicine, Stanford University, Stanford, CA, USA
| | - Andrea Ungar
- Syncope Unit, Geriatric Medicine and Cardiology, Careggi University Hospital, Florence, Italy
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15
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du Fay de Lavallaz J, Badertscher P, Nestelberger T, Isenrich R, Miró Ò, Salgado E, Geigy N, Christ M, Cullen L, Than M, Martin-Sanchez FJ, Bustamante Mandrión J, Di Somma S, Peacock WF, Kawecki D, Boeddinghaus J, Twerenbold R, Puelacher C, Wussler D, Strebel I, Keller DI, Poepping I, Kühne M, Mueller C, Reichlin T, Giménez MR, Walter J, Kozhuharov N, Shrestha S, Mueller D, Sazgary L, Morawiec B, Muzyk P, Nowalany-Kozielska E, Freese M, Stelzig C, Meissner K, Kulangara C, Hartmann B, Ferel I, Sabti Z, Greenslade J, Hawkins T, Rentsch K, von Eckardstein A, Buser A, Kloos W, Lohrmann J, Osswald S. Prospective validation of prognostic and diagnostic syncope scores in the emergency department. Int J Cardiol 2018; 269:114-121. [DOI: 10.1016/j.ijcard.2018.06.088] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Accepted: 06/20/2018] [Indexed: 10/28/2022]
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