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Toy J, Warren J, Wilhelm K, Putnam B, Whitfield D, Gausche-Hill M, Bosson N, Donaldson R, Schlesinger S, Cheng T, Goolsby C. Use of artificial intelligence to support prehospital traumatic injury care: A scoping review. J Am Coll Emerg Physicians Open 2024; 5:e13251. [PMID: 39234533 PMCID: PMC11372236 DOI: 10.1002/emp2.13251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 05/09/2024] [Accepted: 07/03/2024] [Indexed: 09/06/2024] Open
Abstract
Background Artificial intelligence (AI) has transformative potential to support prehospital clinicians, emergency physicians, and trauma surgeons in acute traumatic injury care. This scoping review examines the literature evaluating AI models using prehospital features to support early traumatic injury care. Methods We conducted a systematic search in August 2023 of PubMed, Embase, and Web of Science. Two independent reviewers screened titles/abstracts, with a third reviewer for adjudication, followed by a full-text analysis. We included original research and conference presentations evaluating AI models-machine learning (ML), deep learning (DL), and natural language processing (NLP)-that used prehospital features or features available immediately upon emergency department arrival. Review articles were excluded. The same investigators extracted data and systematically categorized outcomes to ensure consistency and transparency. We calculated kappa for interrater reliability and descriptive statistics. Results We identified 1050 unique publications, with 49 meeting inclusion criteria after title and abstract review (kappa 0.58) and full-text review. Publications increased annually from 2 in 2007 to 10 in 2022. Geographic analysis revealed a 61% focus on data from the United States. Studies were predominantly retrospective (88%), used local (45%) or national level (41%) data, focused on adults only (59%) or did not specify adults or pediatrics (27%), and 57% encompassed both blunt and penetrating injury mechanisms. The majority used machine learning (88%) alone or in conjunction with DL or NLP, and the top three algorithms used were support vector machine, logistic regression, and random forest. The most common study objectives were to predict the need for critical care and life-saving interventions (29%), assist in triage (22%), and predict survival (20%). Conclusions A small but growing body of literature described AI models based on prehospital features that may support decisions made by dispatchers, Emergency Medical Services clinicians, and trauma teams in early traumatic injury care.
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Affiliation(s)
- Jake Toy
- The Lundquist Institute, Department of Emergency Medicine Harbor-UCLA Medical Center Torrance California USA
- Los Angeles Emergency Medical Services Agency Santa Fe Springs California USA
- David Geffen School of Medicine at UCLA Los Angeles California USA
| | - Jonathan Warren
- The Lundquist Institute, Department of Emergency Medicine Harbor-UCLA Medical Center Torrance California USA
- Los Angeles Emergency Medical Services Agency Santa Fe Springs California USA
- David Geffen School of Medicine at UCLA Los Angeles California USA
| | - Kelsey Wilhelm
- The Lundquist Institute, Department of Emergency Medicine Harbor-UCLA Medical Center Torrance California USA
- Los Angeles Emergency Medical Services Agency Santa Fe Springs California USA
- David Geffen School of Medicine at UCLA Los Angeles California USA
| | - Brant Putnam
- Department of Surgery Harbor-UCLA Medical Center Torrance California USA
| | - Denise Whitfield
- The Lundquist Institute, Department of Emergency Medicine Harbor-UCLA Medical Center Torrance California USA
- Los Angeles Emergency Medical Services Agency Santa Fe Springs California USA
- David Geffen School of Medicine at UCLA Los Angeles California USA
| | - Marianne Gausche-Hill
- The Lundquist Institute, Department of Emergency Medicine Harbor-UCLA Medical Center Torrance California USA
- Los Angeles Emergency Medical Services Agency Santa Fe Springs California USA
- David Geffen School of Medicine at UCLA Los Angeles California USA
| | - Nichole Bosson
- The Lundquist Institute, Department of Emergency Medicine Harbor-UCLA Medical Center Torrance California USA
- Los Angeles Emergency Medical Services Agency Santa Fe Springs California USA
- David Geffen School of Medicine at UCLA Los Angeles California USA
| | - Ross Donaldson
- The Lundquist Institute, Department of Emergency Medicine Harbor-UCLA Medical Center Torrance California USA
- David Geffen School of Medicine at UCLA Los Angeles California USA
- Critical Innovations LLC Los Angeles California USA
| | - Shira Schlesinger
- The Lundquist Institute, Department of Emergency Medicine Harbor-UCLA Medical Center Torrance California USA
- Los Angeles Emergency Medical Services Agency Santa Fe Springs California USA
- David Geffen School of Medicine at UCLA Los Angeles California USA
| | - Tabitha Cheng
- The Lundquist Institute, Department of Emergency Medicine Harbor-UCLA Medical Center Torrance California USA
- David Geffen School of Medicine at UCLA Los Angeles California USA
| | - Craig Goolsby
- The Lundquist Institute, Department of Emergency Medicine Harbor-UCLA Medical Center Torrance California USA
- David Geffen School of Medicine at UCLA Los Angeles California USA
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Raff D, Stewart K, Yang MC, Shang J, Cressman S, Tam R, Wong J, Tammemägi MC, Ho K. Improving Triage Accuracy in Prehospital Emergency Telemedicine: Scoping Review of Machine Learning-Enhanced Approaches. Interact J Med Res 2024; 13:e56729. [PMID: 39259967 PMCID: PMC11429666 DOI: 10.2196/56729] [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: 01/24/2024] [Revised: 05/13/2024] [Accepted: 07/18/2024] [Indexed: 09/13/2024] Open
Abstract
BACKGROUND Prehospital telemedicine triage systems combined with machine learning (ML) methods have the potential to improve triage accuracy and safely redirect low-acuity patients from attending the emergency department. However, research in prehospital settings is limited but needed; emergency department overcrowding and adverse patient outcomes are increasingly common. OBJECTIVE In this scoping review, we sought to characterize the existing methods for ML-enhanced telemedicine emergency triage. In order to support future research, we aimed to delineate what data sources, predictors, labels, ML models, and performance metrics were used, and in which telemedicine triage systems these methods were applied. METHODS A scoping review was conducted, querying multiple databases (MEDLINE, PubMed, Scopus, and IEEE Xplore) through February 24, 2023, to identify potential ML-enhanced methods, and for those eligible, relevant study characteristics were extracted, including prehospital triage setting, types of predictors, ground truth labeling method, ML models used, and performance metrics. Inclusion criteria were restricted to the triage of emergency telemedicine services using ML methods on an undifferentiated (disease nonspecific) population. Only primary research studies in English were considered. Furthermore, only those studies using data collected remotely (as opposed to derived from physical assessments) were included. In order to limit bias, we exclusively included articles identified through our predefined search criteria and had 3 researchers (DR, JS, and KS) independently screen the resulting studies. We conducted a narrative synthesis of findings to establish a knowledge base in this domain and identify potential gaps to be addressed in forthcoming ML-enhanced methods. RESULTS A total of 165 unique records were screened for eligibility and 15 were included in the review. Most studies applied ML methods during emergency medical dispatch (7/15, 47%) or used chatbot applications (5/15, 33%). Patient demographics and health status variables were the most common predictors, with a notable absence of social variables. Frequently used ML models included support vector machines and tree-based methods. ML-enhanced models typically outperformed conventional triage algorithms, and we found a wide range of methods used to establish ground truth labels. CONCLUSIONS This scoping review observed heterogeneity in dataset size, predictors, clinical setting (triage process), and reported performance metrics. Standard structured predictors, including age, sex, and comorbidities, across articles suggest the importance of these inputs; however, there was a notable absence of other potentially useful data, including medications, social variables, and health system exposure. Ground truth labeling practices should be reported in a standard fashion as the true model performance hinges on these labels. This review calls for future work to form a standardized framework, thereby supporting consistent reporting and performance comparisons across ML-enhanced prehospital triage systems.
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Affiliation(s)
- Daniel Raff
- Department of Family Practice, Faculty of Medicine, The University of British Columbia, Vancouver, BC, Canada
| | - Kurtis Stewart
- Department of Emergency Medicine, Faculty of Medicine, The University of British Columbia, Vancouver, BC, Canada
| | - Michelle Christie Yang
- Department of Emergency Medicine, Faculty of Medicine, The University of British Columbia, Vancouver, BC, Canada
| | - Jessie Shang
- Department of Emergency Medicine, Faculty of Medicine, The University of British Columbia, Vancouver, BC, Canada
| | - Sonya Cressman
- Department of Emergency Medicine, Faculty of Medicine, The University of British Columbia, Vancouver, BC, Canada
- Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Roger Tam
- School of Biomedical Engineering, Faculty of Applied Science, The University of British Columbia, Vancouver, BC, Canada
- Faculty of Medicine, The University of British Columbia, Vancouver, BC, Canada
| | - Jessica Wong
- Computer Science, Faculty of Science, The University of British Columbia, Vancouver, BC, Canada
| | - Martin C Tammemägi
- Faculty of Applied Health Sciences, Brock University, St. Catharines, ON, Canada
| | - Kendall Ho
- Department of Emergency Medicine, Faculty of Medicine, The University of British Columbia, Vancouver, BC, Canada
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Syyrilä T, Koskiniemi S, Manias E, Härkänen M. Taxonomy development methods regarding patient safety in health sciences - A systematic review. Int J Med Inform 2024; 187:105438. [PMID: 38579660 DOI: 10.1016/j.ijmedinf.2024.105438] [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: 11/23/2023] [Revised: 03/01/2024] [Accepted: 03/25/2024] [Indexed: 04/07/2024]
Abstract
BACKGROUND Taxonomies are needed for automated analysis of clinical data in healthcare. Few reviews of the taxonomy development methods used in health sciences are found. This systematic review aimed to describe the scope of the available taxonomies relative to patient safety, the methods used for taxonomy development, and the strengths and limitations of the methods. The purpose of this systematic review is to guide future taxonomy development projects. METHODS The CINAHL, PubMed, Scopus, and Web of Science databases were searched for studies from January 2012 to April 25, 2023. Two authors selected the studies using inclusion and exclusion criteria and critical appraisal checklists. The data were analysed inductively, and the results were reported narratively. RESULTS The studies (n = 13) across healthcare concerned mainly taxonomies of adverse events and medication safety but little for specialised fields and information technology. Critical appraisal indicated inadequate reporting of the used taxonomy development methods. Ten phases of taxonomy development were identified: (1) defining purpose and (2) the theory base for development, (3) relevant data sources' identification, (4) main terms' identification and definitions, (5) items' coding and pooling, (6) reliability and validity evaluation of coding and/or codes, (7) development of a hierarchical structure, (8) testing the structure, (9) piloting the taxonomy and (10) reporting application and validation of the final taxonomy. Seventeen statistical tests and seven software systems were utilised, but automated data extraction methods were used rarely. Multimethod and multi-stakeholder approach, code- and hierarchy testing and piloting were strengths and time consumption and small samples in testing limitations. CONCLUSION New taxonomies are needed on diverse specialities and information technology related to patient safety. Structured method is needed for taxonomy development, reporting and appraisal to strengthen taxonomies' quality. A new guide was proposed for taxonomy development, for which testing is required. Prospero registration number CRD42023411022.
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Affiliation(s)
- Tiina Syyrilä
- Department of Nursing Science, University of Eastern Finland, Finland.
| | - Saija Koskiniemi
- Department of Nursing Science, University of Eastern Finland, Finland
| | | | - Marja Härkänen
- Department of Nursing Science, University of Eastern Finland, Finland; Research Centre for Nursing Science and Social and Health Management, Kuopio University Hospital, Wellbeing Services County of North Savo, Finland
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Vakili Ojarood M, Yaghoubi T, Farzan R. Machine learning for prehospital care of patients with severe burns. Burns 2024; 50:1041-1043. [PMID: 38461082 DOI: 10.1016/j.burns.2024.02.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 02/02/2024] [Accepted: 02/21/2024] [Indexed: 03/11/2024]
Affiliation(s)
| | - Tahereh Yaghoubi
- Traditional and Complementary Medicine Research Center, Addiction Institute, Mazandaran University of Medical Sciences, Sari, Iran.
| | - Ramyar Farzan
- Department of Plastic & Reconstructive Surgery, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran.
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Michel J, Manns A, Boudersa S, Jaubert C, Dupic L, Vivien B, Burgun A, Campeotto F, Tsopra R. Clinical decision support system in emergency telephone triage: A scoping review of technical design, implementation and evaluation. Int J Med Inform 2024; 184:105347. [PMID: 38290244 DOI: 10.1016/j.ijmedinf.2024.105347] [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] [Revised: 01/09/2024] [Accepted: 01/21/2024] [Indexed: 02/01/2024]
Abstract
OBJECTIVES Emergency department overcrowding could be improved by upstream telephone triage. Emergency telephone triage aims at managing and orientating adequately patients as early as possible and distributing limited supply of staff and materials. This complex task could be improved with the use of Clinical decision support systems (CDSS). The aim of this scoping review was to identify literature gaps for the future development and evaluation of CDSS for Emergency telephone triage. MATERIALS AND METHODS We present here a scoping review of CDSS designed for emergency telephone triage, and compared them in terms of functional characteristics, technical design, health care implementation and methodologies used for evaluation, following the PRISMA-ScR guidelines. RESULTS Regarding design, 19 CDSS were retrieved: 12 were knowledge based CDSS (decisional algorithms built according to guidelines or clinical expertise) and 7 were data driven (statistical, machine learning, or deep learning models). Most of them aimed at assisting nurses or non-medical staff by providing patient orientation and/or severity/priority assessment. Eleven were implemented in real life, and only three were connected to the Electronic Health Record. Regarding evaluation, CDSS were assessed through various aspects: intrinsic characteristics, impact on clinical practice or user apprehension. Only one pragmatic trial and one randomized controlled trial were conducted. CONCLUSION This review highlights the potential of a hybrid system, user tailored, flexible, connected to the electronic health record, which could work with oral, video and digital data; and the need to evaluate CDSS on intrinsic characteristics and impact on clinical practice, iteratively at each distinct stage of the IT lifecycle.
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Affiliation(s)
- Julie Michel
- SAMU 93-UF Recherche-Enseignement-Qualité, Université Paris 13, Sorbonne Paris Cité, Inserm U942, Hôpital Avicenne, 125, rue de Stalingrad, 93009 Bobigny, France
| | - Aurélia Manns
- Université Paris Cité, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, F-75006 Paris, France; Department of Medical Informatics, AP-HP, Hôpital Européen Georges-Pompidou et Hôpital Necker-Enfants Malades, F-75015 Paris, France.
| | - Sofia Boudersa
- Department of Medical Informatics, AP-HP, Hôpital Européen Georges-Pompidou et Hôpital Necker-Enfants Malades, F-75015 Paris, France
| | - Côme Jaubert
- Université Paris Cité, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, F-75006 Paris, France
| | - Laurent Dupic
- Régulation Régionale Pédiatrique, SAMU de Paris, Hôpital Necker - Enfants Malades, AP-HP, Paris, France
| | - Benoit Vivien
- Digital Health Program of Université de Paris Cité, Paris, France; Régulation Régionale Pédiatrique, SAMU de Paris, Hôpital Necker - Enfants Malades, AP-HP, Paris, France
| | - Anita Burgun
- Université Paris Cité, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, F-75006 Paris, France; Department of Medical Informatics, AP-HP, Hôpital Européen Georges-Pompidou et Hôpital Necker-Enfants Malades, F-75015 Paris, France
| | - Florence Campeotto
- Digital Health Program of Université de Paris Cité, Paris, France; Régulation Régionale Pédiatrique, SAMU de Paris, Hôpital Necker - Enfants Malades, AP-HP, Paris, France; Faculté de Pharmacie, Université de Paris Cité, Inserm UMR S1139, Paris, France
| | - Rosy Tsopra
- Université Paris Cité, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, F-75006 Paris, France; Department of Medical Informatics, AP-HP, Hôpital Européen Georges-Pompidou et Hôpital Necker-Enfants Malades, F-75015 Paris, France
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Shih BH, Yeh CC. Advancements in Artificial Intelligence in Emergency Medicine in Taiwan: A Narrative Review. J Acute Med 2024; 14:9-19. [PMID: 38487757 PMCID: PMC10938302 DOI: 10.6705/j.jacme.202403_14(1).0002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 12/19/2023] [Accepted: 12/21/2023] [Indexed: 03/17/2024]
Abstract
The rapid progression of artificial intelligence (AI) in healthcare has greatly influenced emergency medicine, particularly in Taiwan-a nation celebrated for its technological innovation and advanced public healthcare. This narrative review examines the current status of AI applications in Taiwan's emergency medicine and highlights notable achievements and potential areas for growth. AI has wide capabilities encompass a broad range, including disease prediction, diagnostic imaging interpretation, and workflow enhancement. While the integration of AI presents promising advancements, it is not devoid of challenges. Concerns about the interpretability of AI models, the importance of dataset accuracy, the necessity for external validation, and ethical quandaries emphasize the need for a balanced approach. Regulatory oversight also plays a crucial role in ensuring the safe and effective deployment of AI tools in clinical settings. As its footprint continues to expand in medical education and other areas, addressing these challenges is imperative to harness the full potential of AI for transforming emergency medicine in Taiwan.
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Affiliation(s)
- Bing-Hung Shih
- Cathay General Hospital Department of Emergency Medicine Taipei Taiwan
| | - Chien-Chun Yeh
- Cathay General Hospital Department of Emergency Medicine Taipei Taiwan
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Ding X, Wang Y, Ma W, Peng Y, Huang J, Wang M, Zhu H. Development of early prediction model of in-hospital cardiac arrest based on laboratory parameters. Biomed Eng Online 2023; 22:116. [PMID: 38057823 DOI: 10.1186/s12938-023-01178-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 11/23/2023] [Indexed: 12/08/2023] Open
Abstract
BACKGROUND In-hospital cardiac arrest (IHCA) is an acute disease with a high fatality rate that burdens individuals, society, and the economy. This study aimed to develop a machine learning (ML) model using routine laboratory parameters to predict the risk of IHCA in rescue-treated patients. METHODS This retrospective cohort study examined all rescue-treated patients hospitalized at the First Medical Center of the PLA General Hospital in Beijing, China, from January 2016 to December 2020. Five machine learning algorithms, including support vector machine, random forest, extra trees classifier (ETC), decision tree, and logistic regression algorithms, were trained to develop models for predicting IHCA. We included blood counts, biochemical markers, and coagulation markers in the model development. We validated model performance using fivefold cross-validation and used the SHapley Additive exPlanation (SHAP) for model interpretation. RESULTS A total of 11,308 participants were included in the study, of which 7779 patients remained. Among these patients, 1796 (23.09%) cases of IHCA occurred. Among five machine learning models for predicting IHCA, the ETC algorithm exhibited better performance, with an AUC of 0.920, compared with the other four machine learning models in the fivefold cross-validation. The SHAP showed that the top ten factors accounting for cardiac arrest in rescue-treated patients are prothrombin activity, platelets, hemoglobin, N-terminal pro-brain natriuretic peptide, neutrophils, prothrombin time, serum albumin, sodium, activated partial thromboplastin time, and potassium. CONCLUSIONS We developed a reliable machine learning-derived model that integrates readily available laboratory parameters to predict IHCA in patients treated with rescue therapy.
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Affiliation(s)
- Xinhuan Ding
- Medical School of Chinese PLA, Beijing, 100853, China
- Department of Emergency, The First Medical Center, Chinese PLA General Hospital, Fuxing Road 28, Beijing, 100853, People's Republic of China
| | - Yingchan Wang
- Department of Emergency, The First Medical Center, Chinese PLA General Hospital, Fuxing Road 28, Beijing, 100853, People's Republic of China
| | - Weiyi Ma
- Medical School of Chinese PLA, Beijing, 100853, China
- Department of Emergency, The First Medical Center, Chinese PLA General Hospital, Fuxing Road 28, Beijing, 100853, People's Republic of China
| | - Yaojun Peng
- Medical School of Chinese PLA, Beijing, 100853, China
- Department of Emergency, The First Medical Center, Chinese PLA General Hospital, Fuxing Road 28, Beijing, 100853, People's Republic of China
| | - Jingjing Huang
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510000, Guangdong, China
- Department of Emergency, Hainan Hospital of PLA General Hospital, Sanya, 572013, Hainan, China
| | - Meng Wang
- Medical School of Chinese PLA, Beijing, 100853, China
- Department of Emergency, The First Medical Center, Chinese PLA General Hospital, Fuxing Road 28, Beijing, 100853, People's Republic of China
| | - Haiyan Zhu
- Medical School of Chinese PLA, Beijing, 100853, China.
- Department of Emergency, The First Medical Center, Chinese PLA General Hospital, Fuxing Road 28, Beijing, 100853, People's Republic of China.
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Hong D, Chang H, He X, Zhan Y, Tong R, Wu X, Li G. Construction of an Early Alert System for Intradialytic Hypotension before Initiating Hemodialysis Based on Machine Learning. KIDNEY DISEASES (BASEL, SWITZERLAND) 2023; 9:433-442. [PMID: 37901708 PMCID: PMC10601920 DOI: 10.1159/000531619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 06/05/2023] [Indexed: 10/31/2023]
Abstract
Introduction Intradialytic hypotension (IDH) is prevalent and associated with high hospitalization and mortality rates. The purpose of this study was to explore the risk factors for IDH and use artificial intelligence to establish an early alert system before hemodialysis sessions to identify patients at high risk of IDH. Materials and Methods We obtained data on 314,534 hemodialysis sessions conducted at Sichuan Provincial People's Hospital from the renal disease treatment information system. IDH was defined as a systolic blood pressure drop ≥20 mm Hg, a mean arterial pressure drop ≥10 mm Hg during dialysis, or the occurrence of clinical hypotensive events requiring nursing intervention. After pre-processing, the data were randomly divided into training (80%) and testing (20%) sets. Four interpolation methods, three feature selection methods, and 18 machine learning algorithms were used to construct predictive models. The area under the receiver operating characteristic curve (AUC) was the main indicator for evaluating the performance of the models, while Shapley Additive ExPlanation was used to explain the contribution of each variable to the best predictive model. Results A total of 3,906 patients and 314,534 dialysis sessions were included, of which 142,237 cases showed IDH (incidence rate, 45.2%). Nineteen parameters were identified through artificial intelligence feature screening. They included age, pre-dialysis weight, dry weight, pre-dialysis blood pressure, heart rate, prescribed ultrafiltration, blood cell counts (neutrophil, lymphocyte, monocyte, eosinophil, lymphocyte, and platelet counts), hematocrit, serum calcium, creatinine, urea, glucose, and uric acid. Random forest, gradient boosting, and logistic regression were the three best models, and the AUCs were 0.812 (95% confidence interval [CI], 0.811-0.813), 0.748 (95% CI, 0.747-0.749), and 0.743 (95% CI, 0.742-0.744), respectively. Conclusion Our dialysis software-based artificial intelligence alert system can be used to predict IDH occurrence, enabling the initiation of relevant interventions.
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Affiliation(s)
- Daqing Hong
- Department of Nephrology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Huan Chang
- Department of Pharmacy, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Xin He
- Department of Nephrology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Department of Nephrology, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Ya Zhan
- Department of Nephrology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Department of Nephrology, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Rongsheng Tong
- Department of Pharmacy, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Xingwei Wu
- Department of Pharmacy, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Guisen Li
- Department of Nephrology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
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Okada Y, Mertens M, Liu N, Lam SSW, Ong MEH. AI and machine learning in resuscitation: Ongoing research, new concepts, and key challenges. Resusc Plus 2023; 15:100435. [PMID: 37547540 PMCID: PMC10400904 DOI: 10.1016/j.resplu.2023.100435] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/08/2023] Open
Abstract
Aim Artificial intelligence (AI) and machine learning (ML) are important areas of computer science that have recently attracted attention for their application to medicine. However, as techniques continue to advance and become more complex, it is increasingly challenging for clinicians to stay abreast of the latest research. This overview aims to translate research concepts and potential concerns to healthcare professionals interested in applying AI and ML to resuscitation research but who are not experts in the field. Main text We present various research including prediction models using structured and unstructured data, exploring treatment heterogeneity, reinforcement learning, language processing, and large-scale language models. These studies potentially offer valuable insights for optimizing treatment strategies and clinical workflows. However, implementing AI and ML in clinical settings presents its own set of challenges. The availability of high-quality and reliable data is crucial for developing accurate ML models. A rigorous validation process and the integration of ML into clinical practice is essential for practical implementation. We furthermore highlight the potential risks associated with self-fulfilling prophecies and feedback loops, emphasizing the importance of transparency, interpretability, and trustworthiness in AI and ML models. These issues need to be addressed in order to establish reliable and trustworthy AI and ML models. Conclusion In this article, we overview concepts and examples of AI and ML research in the resuscitation field. Moving forward, appropriate understanding of ML and collaboration with relevant experts will be essential for researchers and clinicians to overcome the challenges and harness the full potential of AI and ML in resuscitation.
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Affiliation(s)
- Yohei Okada
- Duke-NUS Medical School, National University of Singapore, Singapore
- Preventive Services, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Mayli Mertens
- Antwerp Center for Responsible AI, Antwerp University, Belgium
- Centre for Ethics, Department of Philosophy, Antwerp University, Belgium
| | - Nan Liu
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Sean Shao Wei Lam
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Marcus Eng Hock Ong
- Duke-NUS Medical School, National University of Singapore, Singapore
- Department of Emergency Medicine, Singapore General Hospital
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