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Petrella RJ. The AI Future of Emergency Medicine. Ann Emerg Med 2024:S0196-0644(24)00043-X. [PMID: 38795081 DOI: 10.1016/j.annemergmed.2024.01.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 05/27/2024]
Abstract
In the coming years, artificial intelligence (AI) and machine learning will likely give rise to profound changes in the field of emergency medicine, and medicine more broadly. This article discusses these anticipated changes in terms of 3 overlapping yet distinct stages of AI development. It reviews some fundamental concepts in AI and explores their relation to clinical practice, with a focus on emergency medicine. In addition, it describes some of the applications of AI in disease diagnosis, prognosis, and treatment, as well as some of the practical issues that they raise, the barriers to their implementation, and some of the legal and regulatory challenges they create.
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Affiliation(s)
- Robert J Petrella
- Emergency Departments, CharterCARE Health Partners, Providence and North Providence, RI; Emergency Department, Boston VA Medical Center, Boston, MA; Emergency Departments, Steward Health Care System, Boston and Methuen, MA; Harvard Medical School, Boston, MA; Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA; Department of Medicine, Brigham and Women's Hospital, Boston, MA.
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Danilatou V, Dimopoulos D, Kostoulas T, Douketis J. Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review. Thromb Haemost 2024. [PMID: 38574756 DOI: 10.1055/a-2299-4758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
BACKGROUND Venous thromboembolism (VTE) is a chronic disorder with a significant health and economic burden. Several VTE-specific clinical prediction models (CPMs) have been used to assist physicians in decision-making but have several limitations. This systematic review explores if machine learning (ML) can enhance CPMs by analyzing extensive patient data derived from electronic health records. We aimed to explore ML-CPMs' applications in VTE for risk stratification, outcome prediction, diagnosis, and treatment. METHODS Three databases were searched: PubMed, Google Scholar, and IEEE electronic library. Inclusion criteria focused on studies using structured data, excluding non-English publications, studies on non-humans, and certain data types such as natural language processing and image processing. Studies involving pregnant women, cancer patients, and children were also excluded. After excluding irrelevant studies, a total of 77 studies were included. RESULTS Most studies report that ML-CPMs outperformed traditional CPMs in terms of receiver operating area under the curve in the four clinical domains that were explored. However, the majority of the studies were retrospective, monocentric, and lacked detailed model architecture description and external validation, which are essential for quality audit. This review identified research gaps and highlighted challenges related to standardized reporting, reproducibility, and model comparison. CONCLUSION ML-CPMs show promise in improving risk assessment and individualized treatment recommendations in VTE. Apparently, there is an urgent need for standardized reporting and methodology for ML models, external validation, prospective and real-world data studies, as well as interventional studies to evaluate the impact of artificial intelligence in VTE.
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Affiliation(s)
- Vasiliki Danilatou
- School of Medicine, European University of Cyprus, Nicosia, Cyprus
- Healthcare Division, Sphynx Technology Solutions, Nicosia, Cyprus
| | - Dimitrios Dimopoulos
- School of Engineering, Department of Information and Communication Systems Engineering, University of the Aegean, North Aegean, Greece
| | - Theodoros Kostoulas
- School of Engineering, Department of Information and Communication Systems Engineering, University of the Aegean, North Aegean, Greece
| | - James Douketis
- Department of Medicine, McMaster University, Hamilton, Canada
- Department of Medicine, St. Joseph's Healthcare Hamilton, Ontario, Canada
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Xi L, Kang H, Deng M, Xu W, Xu F, Gao Q, Xie W, Zhang R, Liu M, Zhai Z, Wang C. A machine learning model for diagnosing acute pulmonary embolism and comparison with Wells score, revised Geneva score, and Years algorithm. Chin Med J (Engl) 2024; 137:676-682. [PMID: 37828028 PMCID: PMC10950185 DOI: 10.1097/cm9.0000000000002837] [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: 05/09/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND Acute pulmonary embolism (APE) is a fatal cardiovascular disease, yet missed diagnosis and misdiagnosis often occur due to non-specific symptoms and signs. A simple, objective technique will help clinicians make a quick and precise diagnosis. In population studies, machine learning (ML) plays a critical role in characterizing cardiovascular risks, predicting outcomes, and identifying biomarkers. This work sought to develop an ML model for helping APE diagnosis and compare it against current clinical probability assessment models. METHODS This is a single-center retrospective study. Patients with suspected APE were continuously enrolled and randomly divided into two groups including training and testing sets. A total of 8 ML models, including random forest (RF), Naïve Bayes, decision tree, K-nearest neighbors, logistic regression, multi-layer perceptron, support vector machine, and gradient boosting decision tree were developed based on the training set to diagnose APE. Thereafter, the model with the best diagnostic performance was selected and evaluated against the current clinical assessment strategies, including the Wells score, revised Geneva score, and Years algorithm. Eventually, the ML model was internally validated to assess the diagnostic performance using receiver operating characteristic (ROC) analysis. RESULTS The ML models were constructed using eight clinical features, including D-dimer, cardiac troponin T (cTNT), arterial oxygen saturation, heart rate, chest pain, lower limb pain, hemoptysis, and chronic heart failure. Among eight ML models, the RF model achieved the best performance with the highest area under the curve (AUC) (AUC = 0.774). Compared to the current clinical assessment strategies, the RF model outperformed the Wells score ( P = 0.030) and was not inferior to any other clinical probability assessment strategy. The AUC of the RF model for diagnosing APE onset in internal validation set was 0.726. CONCLUSIONS Based on RF algorithm, a novel prediction model was finally constructed for APE diagnosis. When compared to the current clinical assessment strategies, the RF model achieved better diagnostic efficacy and accuracy. Therefore, the ML algorithm can be a useful tool in assisting with the diagnosis of APE.
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Affiliation(s)
- Linfeng Xi
- Capital Medical University, Beijing 100069, China
- National Center for Respiratory Medicine; State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing 100029, China
| | - Han Kang
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd., Beijing 100025, China
| | - Mei Deng
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, China
- Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Wenqing Xu
- Department of Radiology, Peking University China-Japan Friendship School of Clinical Medicine, Beijing 100191, China
| | - Feiya Xu
- Capital Medical University, Beijing 100069, China
- National Center for Respiratory Medicine; State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing 100029, China
| | - Qian Gao
- National Center for Respiratory Medicine; State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing 100029, China
| | - Wanmu Xie
- National Center for Respiratory Medicine; State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing 100029, China
| | - Rongguo Zhang
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd., Beijing 100025, China
| | - Min Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, China
| | - Zhenguo Zhai
- National Center for Respiratory Medicine; State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing 100029, China
| | - Chen Wang
- Capital Medical University, Beijing 100069, China
- National Center for Respiratory Medicine; State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing 100029, China
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Liu L, Li Y, Liu N, Luo J, Deng J, Peng W, Bai Y, Zhang G, Zhao G, Yang N, Li C, Long X. Establishment of machine learning-based tool for early detection of pulmonary embolism. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107977. [PMID: 38113803 DOI: 10.1016/j.cmpb.2023.107977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 09/11/2023] [Accepted: 12/11/2023] [Indexed: 12/21/2023]
Abstract
BACKGROUND AND OBJECTIVES Pulmonary embolism (PE) is a complex disease with high mortality and morbidity rate, leading to increasing society burden. However, current diagnosis is solely based on symptoms and laboratory data despite its complex pathology, which easily leads to misdiagnosis and missed diagnosis by inexperienced doctors. Especially, CT pulmonary angiography, the gold standard method, is not widely available. In this study, we aim to establish a rapid and accurate screening model for pulmonary embolism using machine learning technology. Importantly, data required for disease prediction are easily accessed, including routine laboratory data and medical record information of patients. METHODS We extracted features from patients' routine laboratory results and medical records, including blood routine, biochemical group, blood coagulation routine and other test results, as well as symptoms and medical history information. Samples with a feature loss rate greater than 0.8 were deleted from the original database. Data from 4723 cases were retained, 231 of which were positive for pulmonary embolism. 50 features were retained through the positive and negative statistical hypothesis testing which was used to build the predictive model. In order to avoid identification as majority-class samples caused by the imbalance of sample proportion, we used the method of Synthetic Minority Oversampling Technique (SMOTE) to increase the amount of information on minority samples. Five typical machine learning algorithms were used to model the screening of pulmonary embolism, including Support Vector Machines, Logistic Regression, Random Forest, XGBoost, and Back Propagation Neural Networks. To evaluate model performance, sensitivity, specificity and AUC curve were analyzed as the main evaluation indicators. Furthermore, a baseline model was established using the characteristics of the pulmonary embolism guidelines as a comparison model. RESULTS We found that XGBoost showed better performance compared to other models, with the highest sensitivity and specificity (0.99 and 0.99, respectively). Moreover, it showed significant improvement in performance compared to the baseline model (sensitivity and specificity were 0.76 and 0.76 respectively). More important, our model showed low missed diagnosis rate (0.46) and high AUC value (0.992). Finally, the calculation time of our model is only about 0.05 s to obtain the possibility of pulmonary embolism. CONCLUSIONS In this study, five machine learning classification models were established to assess the likelihood of patients suffering from pulmonary embolism, and the XGBoost model most significantly improved the precision, sensitivity, and AUC for pulmonary embolism screening. Collectively, we have established an AI-based model to accurately predict pulmonary embolism at early stage.
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Affiliation(s)
- Lijue Liu
- School of Automation, Central South University, Changsha, Hunan 410083, China; Xiangjiang Laboratory, Changsha 410205, China; Hunan Zixing Intelligent Medical Technology Co., Ltd, Changsha, Hunan 410000, China
| | - Yaming Li
- School of Automation, Central South University, Changsha, Hunan 410083, China
| | - Na Liu
- Xiangya Hospital, Central South University, Xiangya Road 87#, Changsha 410008, China
| | - Jingmin Luo
- Xiangya Hospital, Central South University, Xiangya Road 87#, Changsha 410008, China
| | - Jinhai Deng
- Hunan Zixing Intelligent Medical Technology Co., Ltd, Changsha, Hunan 410000, China; Richard Dimbleby Laboratory of Cancer Research, School of Cancer & Pharmaceutical Sciences, King's College London, London SE1 1UL, UK
| | - Weixiong Peng
- Hunan Zixing Intelligent Medical Technology Co., Ltd, Changsha, Hunan 410000, China; Department of Electrical and Electronic Engineering, College of Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, Guangdong 518055, China
| | - Yongping Bai
- Xiangya Hospital, Central South University, Xiangya Road 87#, Changsha 410008, China
| | - Guogang Zhang
- Department of Cardiovascular Medicine, The Third Xiangya Hospital, Central South University, Tongzipo Road 138#, Changsha 410008,China.
| | - Guihu Zhao
- Xiangya Hospital, Central South University, Xiangya Road 87#, Changsha 410008, China
| | - Ning Yang
- Xiangya Hospital, Central South University, Xiangya Road 87#, Changsha 410008, China
| | - Chuanchang Li
- Xiangya Hospital, Central South University, Xiangya Road 87#, Changsha 410008, China
| | - Xueying Long
- Xiangya Hospital, Central South University, Xiangya Road 87#, Changsha 410008, China
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