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Laffafchi S, Ebrahimi A, Kafan S. Efficient management of pulmonary embolism diagnosis using a two-step interconnected machine learning model based on electronic health records data. Health Inf Sci Syst 2024; 12:17. [PMID: 38464464 PMCID: PMC10917730 DOI: 10.1007/s13755-024-00276-9] [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: 12/15/2022] [Accepted: 01/17/2024] [Indexed: 03/12/2024] Open
Abstract
Pulmonary Embolism (PE) is a life-threatening clinical disease with no specific clinical symptoms and Computed Tomography Angiography (CTA) is used for diagnosis. Clinical decision support scoring systems like Wells and rGeneva based on PE risk factors have been developed to estimate the pre-test probability but are underused, leading to continuous overuse of CTA imaging. This diagnostic study aimed to propose a novel approach for efficient management of PE diagnosis using a two-step interconnected machine learning framework directly by analyzing patients' Electronic Health Records data. First, we performed feature importance analysis according to the result of LightGBM superiority for PE prediction, then four state-of-the-art machine learning methods were applied for PE prediction based on the feature importance results, enabling swift and accurate pre-test diagnosis. Throughout the study patients' data from different departments were collected from Sina educational hospital, affiliated with the Tehran University of medical sciences in Iran. Generally, the Ridge classification method obtained the best performance with an F1 score of 0.96. Extensive experimental findings showed the effectiveness and simplicity of this diagnostic process of PE in comparison with the existing scoring systems. The main strength of this approach centered on PE disease management procedures, which would reduce avoidable invasive CTA imaging and be applied as a primary prognosis of PE, hence assisting the healthcare system, clinicians, and patients by reducing costs and promoting treatment quality and patient satisfaction.
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Affiliation(s)
- Soroor Laffafchi
- Department of Business Administration and Entrepreneurship, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Daneshgah Blvd, Simon Bulivar Blvd, Tehran, Iran
| | - Ahmad Ebrahimi
- Department of Industrial and Technology Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Daneshgah Blvd, Simon Bulivar Blvd, Tehran, Iran
| | - Samira Kafan
- Department of Pulmonary Medicine, Sina Hospital, International Relations Office, Medical School, Tehran University of Medical Sciences, PourSina St., Tehran, 1417613151 Iran
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2
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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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3
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Zhang L, Yu R, Chen K, Zhang Y, Li Q, Chen Y. Enhancing deep vein thrombosis prediction in patients with coronavirus disease 2019 using improved machine learning model. Comput Biol Med 2024; 173:108294. [PMID: 38537565 DOI: 10.1016/j.compbiomed.2024.108294] [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: 12/11/2023] [Revised: 02/21/2024] [Accepted: 03/12/2024] [Indexed: 04/17/2024]
Abstract
BACKGROUND Deep vein thrombosis (DVT) is a significant complication in coronavirus disease 2019 patients, arising from coagulation issues in the deep venous system. Among 424 scheduled patients, 202 developed DVT (47.64%). DVT increases hospitalization risk, and complications, and impacts prognosis. Accurate prognostication and timely intervention are crucial to prevent DVT progression and improve patient outcomes. METHODS This study introduces an effective DVT prediction model, named bSES-AC-RUN-FKNN, which integrates fuzzy k-nearest neighbor (FKNN) with enhanced Runge-Kutta optimizer (RUN). Recognizing the insufficient effectiveness of RUN in local search capability and its convergence accuracy, spherical evolutionary search (SES) and differential evolution-inspired knowledge adaptive crossover (AC) are incorporated, termed SES-AC-RUN, to enhance its optimization capability. RESULTS Based on the benchmark set by CEC 2017 and comparative analyses with several peers, it is evident that SES-AC-RUN significantly enhances search performance compared to traditional RUN, even standing comparably against leading championship algorithms. The proposed bSES-AC-RUN-FKNN model was applied to predict a dataset comprising 424 cases of DVT patients, totaling 7208 records. Remarkably, the model demonstrates outstanding accuracy, reaching 91.02%, alongside commendable sensitivity at 91.07%. CONCLUSIONS The bSES-AC-RUN-FKNN emerges as a robust and efficient predictive tool, significantly enhancing the accuracy of DVT prediction. This model can be used to manage the risk of thrombosis in the care of COVID-19 patients. Nursing staff can combine the model's predictions with clinical judgment to formulate comprehensive treatment approaches.
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Affiliation(s)
- Lufang Zhang
- The First Clinical College, Wenzhou Medical University, Wenzhou, 325000, China.
| | - Renyue Yu
- Cardiac Care Unit, Sir RUN RUN Shaw Hospital, Hangzhou, 310000, China.
| | - Keya Chen
- The First Clinical College, Wenzhou Medical University, Wenzhou, 325000, China.
| | - Ying Zhang
- Wenzhou Medical University School of Nursing, 325000, Wenzhou, 325000, China; Cixi Biomedical Research Institute, Wenzhou Medical University, Cixi, 315300, China.
| | - Qiang Li
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China.
| | - Yu Chen
- Nursing Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
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4
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Chen R, Petrazzini BO, Malick W, Rosenson R, Do R. Prediction of Venous Thromboembolism in Diverse Populations Using Machine Learning and Structured Electronic Health Records. Arterioscler Thromb Vasc Biol 2024; 44:491-504. [PMID: 38095106 PMCID: PMC10872966 DOI: 10.1161/atvbaha.123.320331] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 12/04/2023] [Indexed: 01/26/2024]
Abstract
BACKGROUND Venous thromboembolism (VTE) is a major cause of morbidity and mortality worldwide. Current risk assessment tools, such as the Caprini and Padua scores and Wells criteria, have limitations in their applicability and accuracy. This study aimed to develop machine learning models using structured electronic health record data to predict diagnosis and 1-year risk of VTE. METHODS We trained and validated models on data from 159 001 participants in the Mount Sinai Data Warehouse. We then externally tested them on 401 723 participants in the UK Biobank and 123 039 participants in All of Us. All data sets contain populations of diverse ancestries and clinical histories. We used these data sets to develop small, medium, and large models with increasing features on a range of optimizing portability to maximizing performance. We make trained models publicly available in click-and-run format at https://doi.org/10.17632/tkwzysr4y6.6. RESULTS In the holdout and external test sets, respectively, models achieved areas under the receiver operating characteristic curve of 0.80 to 0.83 and 0.72 to 0.82 for VTE diagnosis prediction and 0.76 to 0.78 and 0.64 to 0.69 for 1-year risk prediction, significantly outperforming the Padua score. Models also demonstrated robust performance across different VTE types and patient subsets, including ethnicity, age, and surgical and hospitalization status. Models identified both established and novel clinical features contributing to VTE risk, offering valuable insights into its underlying pathophysiology. CONCLUSIONS Machine learning models using structured electronic health record data can significantly improve VTE diagnosis and 1-year risk prediction in diverse populations. Model probability scores exist on a continuum, affecting mortality risk in both healthy individuals and VTE cases. Integrating these models into electronic health record systems to generate real-time predictions may enhance VTE risk assessment, early detection, and preventative measures, ultimately reducing the morbidity and mortality associated with VTE.
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Affiliation(s)
- Robert Chen
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ben Omega Petrazzini
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Genomic Data Analytics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Waqas Malick
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Robert Rosenson
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Genomic Data Analytics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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5
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Su L, Liu S, Long Y, Chen C, Chen K, Chen M, Chen Y, Cheng Y, Cui Y, Ding Q, Ding R, Duan M, Gao T, Gu X, He H, He J, Hu B, Hu C, Huang R, Huang X, Jiang H, Jiang J, Lan Y, Li J, Li L, Li L, Li W, Li Y, Lin J, Luo X, Lyu F, Mao Z, Miao H, Shang X, Shang X, Shang Y, Shen Y, Shi Y, Sun Q, Sun W, Tang Z, Wang B, Wang H, Wang H, Wang L, Wang L, Wang S, Wang Z, Wang Z, Wei D, Wu J, Wu Q, Xing X, Yang J, Yang X, Yu J, Yu W, Yu Y, Yuan H, Zhai Q, Zhang H, Zhang L, Zhang M, Zhang Z, Zhao C, Zheng R, Zhong L, Zhou F, Zhu W. Chinese experts' consensus on the application of intensive care big data. Front Med (Lausanne) 2024; 10:1174429. [PMID: 38264049 PMCID: PMC10804886 DOI: 10.3389/fmed.2023.1174429] [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: 02/26/2023] [Accepted: 11/09/2023] [Indexed: 01/25/2024] Open
Abstract
The development of intensive care medicine is inseparable from the diversified monitoring data. Intensive care medicine has been closely integrated with data since its birth. Critical care research requires an integrative approach that embraces the complexity of critical illness and the computational technology and algorithms that can make it possible. Considering the need of standardization of application of big data in intensive care, Intensive Care Medicine Branch of China Health Information and Health Care Big Data Society, Standard Committee has convened expert group, secretary group and the external audit expert group to formulate Chinese Experts' Consensus on the Application of Intensive Care Big Data (2022). This consensus makes 29 recommendations on the following five parts: Concept of intensive care big data, Important scientific issues, Standards and principles of database, Methodology in solving big data problems, Clinical application and safety consideration of intensive care big data. The consensus group believes this consensus is the starting step of application big data in the field of intensive care. More explorations and big data based retrospective research should be carried out in order to enhance safety and reliability of big data based models of critical care field.
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Affiliation(s)
- Longxiang Su
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Shengjun Liu
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Yun Long
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Chaodong Chen
- Department of Surgical Intensive Critical Unit, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China
| | - Kai Chen
- Department of Critical Care Medicine, Fujian Provincial Key Laboratory of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian, China
| | - Ming Chen
- Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Yaolong Chen
- Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Yisong Cheng
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Yating Cui
- Department of Critical Care Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Qi Ding
- Department of Surgical Intensive Critical Unit, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China
| | - Renyu Ding
- Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Meili Duan
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Tao Gao
- Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Xiaohua Gu
- Department of Critical Care Medicine, Northern Jiangsu People’s Hospital; Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Hongli He
- Intensive Care Unit, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, School of Medicine of University of Electronic Science and Technology, Chengdu, China
| | - Jiawei He
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Bo Hu
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Chang Hu
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Rui Huang
- Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Xiaobo Huang
- Intensive Care Unit, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, School of Medicine of University of Electronic Science and Technology, Chengdu, China
| | - Huizhen Jiang
- Department of Information Center, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Jing Jiang
- Department of Critical Care Medicine, Chongqing General Hospital, Chongqing, China
| | - Yunping Lan
- Intensive Care Unit, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, School of Medicine of University of Electronic Science and Technology, Chengdu, China
| | - Jun Li
- Department of Critical Care Medicine, Fujian Provincial Key Laboratory of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian, China
| | - Linfeng Li
- Medical Data Research Institute, Chongqing Medical University, Chongqing, China
| | - Lu Li
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Wenxiong Li
- Department of Surgical Intensive Critical Unit, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China
| | - Yongzai Li
- Information Network Center, QiLu Hospital, ShanDong University, Jinan, China
| | - Jin Lin
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Xufei Luo
- Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Feng Lyu
- Department of Computer Science and Engineering, Central South University, Changsha, China
| | - Zhi Mao
- Department of Critical Care Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - He Miao
- Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Xiaopu Shang
- Department of Information Management, Beijing Jiaotong University, Beijing, China
| | - Xiuling Shang
- Department of Critical Care Medicine, Fujian Provincial Key Laboratory of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian, China
| | - You Shang
- Department of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuwen Shen
- Intensive Care Unit of Cardiovascular Surgery Department, Qilu Hospital of Shandong University, Jinan, China
| | - Yinghuan Shi
- National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
| | - Qihang Sun
- British Chinese Society of Health Informatics, Beijing, China
| | - Weijun Sun
- Faculty of Automation, Guangdong University of Technology, Guangzhou, China
| | - Zhiyun Tang
- Department of Intensive Care Unit, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Emergency and Intensive Care Unit Center, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Bo Wang
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Haijun Wang
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hongliang Wang
- Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Li Wang
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences; School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Luhao Wang
- Department of Critical Care Medicine, Sun Yat-Sen University First Affiliated Hospital, Guangzhou, China
| | - Sicong Wang
- Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Zhanwen Wang
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiang Ya Hospital, Central South University, Changsha, China
- Hunan Provincial Clinical Research Center for Critical Care Medicine, Xiang Ya Hospital, Central South University, Changsha, China
| | - Zhong Wang
- Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Dong Wei
- National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
| | - Jianfeng Wu
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
| | - Qin Wu
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Xuezhong Xing
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences; School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Jin Yang
- Department of Critical Care Medicine, Chongqing General Hospital, Chongqing, China
| | - Xianghong Yang
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiangquan Yu
- Department of Critical Care Medicine, Northern Jiangsu People’s Hospital; Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Wenkui Yu
- Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Yuan Yu
- Intensive Care Unit of Cardiovascular Surgery Department, Qilu Hospital of Shandong University, Jinan, China
| | - Hao Yuan
- Department of Critical Care Medicine, Sun Yat-Sen University First Affiliated Hospital, Guangzhou, China
| | - Qian Zhai
- National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
| | - Hao Zhang
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lina Zhang
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiang Ya Hospital, Central South University, Changsha, China
- Hunan Provincial Clinical Research Center for Critical Care Medicine, Xiang Ya Hospital, Central South University, Changsha, China
| | - Meng Zhang
- Department of Critical Care Medicine, Chongqing General Hospital, Chongqing, China
| | - Zhongheng Zhang
- Department of Emergency Medicine, Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chunguang Zhao
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiang Ya Hospital, Central South University, Changsha, China
- Hunan Provincial Clinical Research Center for Critical Care Medicine, Xiang Ya Hospital, Central South University, Changsha, China
| | - Ruiqiang Zheng
- Department of Critical Care Medicine, Northern Jiangsu People’s Hospital; Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Lei Zhong
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Feihu Zhou
- Department of Critical Care Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Weiguo Zhu
- Department of General Medicine, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
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Adelson RP, Garikipati A, Maharjan J, Ciobanu M, Barnes G, Singh NP, Dinenno FA, Mao Q, Das R. Machine Learning Approach for Improved Longitudinal Prediction of Progression from Mild Cognitive Impairment to Alzheimer's Disease. Diagnostics (Basel) 2023; 14:13. [PMID: 38201322 PMCID: PMC10795823 DOI: 10.3390/diagnostics14010013] [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: 10/27/2023] [Revised: 12/08/2023] [Accepted: 12/15/2023] [Indexed: 01/12/2024] Open
Abstract
Mild cognitive impairment (MCI) is cognitive decline that can indicate future risk of Alzheimer's disease (AD). We developed and validated a machine learning algorithm (MLA), based on a gradient-boosted tree ensemble method, to analyze phenotypic data for individuals 55-88 years old (n = 493) diagnosed with MCI. Data were analyzed within multiple prediction windows and averaged to predict progression to AD within 24-48 months. The MLA outperformed the mini-mental state examination (MMSE) and three comparison models at all prediction windows on most metrics. Exceptions include sensitivity at 18 months (MLA and MMSE each achieved 0.600); and sensitivity at 30 and 42 months (MMSE marginally better). For all prediction windows, the MLA achieved AUROC ≥ 0.857 and NPV ≥ 0.800. With averaged data for the 24-48-month lookahead timeframe, the MLA outperformed MMSE on all metrics. This study demonstrates that machine learning may provide a more accurate risk assessment than the standard of care. This may facilitate care coordination, decrease healthcare expenditures, and maintain quality of life for patients at risk of progressing from MCI to AD.
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Affiliation(s)
| | | | | | | | | | | | | | - Qingqing Mao
- Montera, Inc. dba Forta, 548 Market St, PMB 89605, San Francisco, CA 94104-5401, USA; (R.P.A.); (A.G.); (J.M.); (M.C.); (G.B.); (N.P.S.); (F.A.D.); (R.D.)
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7
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Sheng W, Wang X, Xu W, Hao Z, Ma H, Zhang S. Development and validation of machine learning models for venous thromboembolism risk assessment at admission: a retrospective study. Front Cardiovasc Med 2023; 10:1198526. [PMID: 37705687 PMCID: PMC10497101 DOI: 10.3389/fcvm.2023.1198526] [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: 04/01/2023] [Accepted: 08/10/2023] [Indexed: 09/15/2023] Open
Abstract
Introduction Venous thromboembolism (VTE) risk assessment at admission is of great importance for early screening and timely prophylaxis and management during hospitalization. The purpose of this study is to develop and validate novel risk assessment models at admission based on machine learning (ML) methods. Methods In this retrospective study, a total of 3078 individuals were included with their Caprini variables within 24 hours at admission. Then several ML models were built, including logistic regression (LR), random forest (RF), and extreme gradient boosting (XGB). The prediction performance of ML models and the Caprini risk score (CRS) was then validated and compared through a series of evaluation metrics. Results The values of AUROC and AUPRC were 0.798 and 0.303 for LR, 0.804 and 0.360 for RF, and 0.796 and 0.352 for XGB, respectively, which outperformed CRS significantly (0.714 and 0.180, P < 0.001). When prediction scores were stratified into three risk levels for application, RF could obtain more reasonable results than CRS, including smaller false positive alerts and larger lower-risk proportions. The boosting results of stratification were further verified by the net-reclassification-improvement (NRI) analysis. Discussion This study indicated that machine learning models could improve VTE risk prediction at admission compared with CRS. Among the ML models, RF was found to have superior performance and great potential in clinical practice.
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Affiliation(s)
- Wenbo Sheng
- Research and Development Department, Shanghai Synyi Medical Technology Co., Ltd., Shanghai, China
| | - Xiaoli Wang
- Pudong Institute for Health Development, Shanghai, China
| | - Wenxiang Xu
- Research and Development Department, Shanghai Synyi Medical Technology Co., Ltd., Shanghai, China
| | - Zedong Hao
- Research and Development Department, Shanghai Synyi Medical Technology Co., Ltd., Shanghai, China
| | - Handong Ma
- Research and Development Department, Shanghai Synyi Medical Technology Co., Ltd., Shanghai, China
| | - Shaodian Zhang
- Research and Development Department, Shanghai Synyi Medical Technology Co., Ltd., Shanghai, China
- Division of Medical Affairs, Shanghai Tenth People's Hospital, Shanghai, China
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8
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Gedefaw L, Liu CF, Ip RKL, Tse HF, Yeung MHY, Yip SP, Huang CL. Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders. Cells 2023; 12:1755. [PMID: 37443789 PMCID: PMC10340428 DOI: 10.3390/cells12131755] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 06/21/2023] [Accepted: 06/28/2023] [Indexed: 07/15/2023] Open
Abstract
Artificial intelligence (AI) is a rapidly evolving field of computer science that involves the development of computational programs that can mimic human intelligence. In particular, machine learning and deep learning models have enabled the identification and grouping of patterns within data, leading to the development of AI systems that have been applied in various areas of hematology, including digital pathology, alpha thalassemia patient screening, cytogenetics, immunophenotyping, and sequencing. These AI-assisted methods have shown promise in improving diagnostic accuracy and efficiency, identifying novel biomarkers, and predicting treatment outcomes. However, limitations such as limited databases, lack of validation and standardization, systematic errors, and bias prevent AI from completely replacing manual diagnosis in hematology. In addition, the processing of large amounts of patient data and personal information by AI poses potential data privacy issues, necessitating the development of regulations to evaluate AI systems and address ethical concerns in clinical AI systems. Nonetheless, with continued research and development, AI has the potential to revolutionize the field of hematology and improve patient outcomes. To fully realize this potential, however, the challenges facing AI in hematology must be addressed and overcome.
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Affiliation(s)
- Lealem Gedefaw
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (L.G.); (C.-F.L.); (M.H.Y.Y.)
| | - Chia-Fei Liu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (L.G.); (C.-F.L.); (M.H.Y.Y.)
| | - Rosalina Ka Ling Ip
- Department of Pathology, Pamela Youde Nethersole Eastern Hospital, Hong Kong, China; (R.K.L.I.); (H.-F.T.)
| | - Hing-Fung Tse
- Department of Pathology, Pamela Youde Nethersole Eastern Hospital, Hong Kong, China; (R.K.L.I.); (H.-F.T.)
| | - Martin Ho Yin Yeung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (L.G.); (C.-F.L.); (M.H.Y.Y.)
| | - Shea Ping Yip
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (L.G.); (C.-F.L.); (M.H.Y.Y.)
| | - Chien-Ling Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (L.G.); (C.-F.L.); (M.H.Y.Y.)
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9
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Wilson S, Chen X, Cronin M, Dengler N, Enker P, Krauss ES, Laberko L, Lobastov K, Obi AT, Powell CA, Schastlivtsev I, Segal A, Simonson B, Siracuse J, Wakefield TW, McAneny D, Caprini JA, Caprini JA. Thrombosis prophylaxis in surgical patients using the Caprini Risk Score. Curr Probl Surg 2022; 59:101221. [PMID: 36372452 DOI: 10.1016/j.cpsurg.2022.101221] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
| | - Xialan Chen
- Beijing Shijitan Hospital, Capital Medical University, Beijing, P.R. China
| | - MaryAnne Cronin
- Department of Orthopedic Surgery, Syosset Hospital, Syosset, NY
| | - Nancy Dengler
- Department of Orthopedic Surgery, Syosset Hospital, Syosset, NY
| | - Paul Enker
- Zucker School of Medicine, Hofstra University, Uniondale, NY
| | - Eugene S Krauss
- Department of Orthopedic Surgery, Syosset Hospital, Syosset, NY
| | - Leonid Laberko
- Pirogov Russian National Research Medical University, Moscow, Russia
| | - Kirill Lobastov
- Pirogov Russian National Research Medical University, Moscow, Russia
| | - Andrea T Obi
- Section of Vascular Surgery, Department of Surgery, University of Michigan, Ann Arbor, MI
| | - Chloé A Powell
- Department of Surgery, University of Michigan, Ann Arbor, MI
| | | | - Ayal Segal
- Department of Orthopedic Surgery, Syosset Hospital, Syosset, NY
| | - Barry Simonson
- Zucker School of Medicine, Hofstra University, Uniondale, NY
| | | | | | - David McAneny
- Boston University School of Medicine, Boston Medical Center, Boston, MA
| | - Joseph A Caprini
- Emeritus, NorthShore University Health System, University of Chicago Pritzker School of Medicine, Chicago, IL
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10
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Ma H, Dong Z, Chen M, Sheng W, Li Y, Zhang W, Zhang S, Yu Y. A gradient boosting tree model for multi-department venous thromboembolism risk assessment with imbalanced data. J Biomed Inform 2022; 134:104210. [PMID: 36122879 DOI: 10.1016/j.jbi.2022.104210] [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: 12/30/2021] [Revised: 08/17/2022] [Accepted: 09/12/2022] [Indexed: 11/19/2022]
Abstract
Venous thromboembolism (VTE) is the world's third most common cause of vascular mortality and a serious complication from multiple departments. Risk assessment of VTE guides clinical intervention in time and is of great importance to in-hospital patients. Traditional VTE risk assessment methods based on scaling tools, which always require rules carefully designed by human experts, are difficult to apply to large-population scenarios since the manually designed rules are not guaranteed to be accurate to all populations. In contrast, with the development of the electronic health record (EHR) datasets, data-driven machine-learning-based risk assessment methods have proven superior predictability in many studies in recent years. This paper uses the gradient boosting tree model to study the VTE risk assessment problem with multi-department data. There exist two distinct characteristics of VTE data collected at the level of the entire hospital: its wide distribution and heterogeneity across multiple departments. To this end, we consider the prediction task over multiple departments as a multi-task learning process, and introduce the algorithm of a task-aware tree-based method TSGB to tackle the multi-task prediction problem. Although the introduction of multi-task learning improves overall across-department performance, we reveal the problem of task-wise performance decline while dealing with imbalanced VTE data volume. According to the analysis, we finally propose two variants of TSGB to alleviate the problems and further boost the prediction performance. Compared with state-of-the-art rule-based and multi-task tree-based methods, the experimental results show the proposed methods not only improve the overall across-department AUC performance effectively, but also ensure the improvement of performance over every single department prediction.
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Affiliation(s)
- Handong Ma
- Shanghai Jiao Tong University, Shanghai, China.
| | | | | | - Wenbo Sheng
- Shanghai Synyi Medical Technology Co., Ltd, Shanghai, China.
| | - Yao Li
- Shanghai Jiao Tong University, Shanghai, China.
| | | | - Shaodian Zhang
- Shanghai Synyi Medical Technology Co., Ltd, Shanghai, China; Shanghai Tenth People's Hospital, Shanghai, China.
| | - Yong Yu
- Shanghai Jiao Tong University, Shanghai, China.
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11
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Meng L, Wei T, Fan R, Su H, Liu J, Wang L, Huang X, Qi Y, Li X. Development and validation of a machine learning model to predict venous thromboembolism among hospitalized cancer patients. Asia Pac J Oncol Nurs 2022; 9:100128. [PMID: 36276886 PMCID: PMC9583033 DOI: 10.1016/j.apjon.2022.100128] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Accepted: 07/30/2022] [Indexed: 11/02/2022] Open
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12
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A Broad Learning System to Predict the 28-Day Mortality of Patients Hospitalized with Community-Acquired Pneumonia: A Case-Control Study. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:7003272. [PMID: 35281948 PMCID: PMC8916852 DOI: 10.1155/2022/7003272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 01/20/2022] [Accepted: 01/31/2022] [Indexed: 11/17/2022]
Abstract
This study was to conduct a model based on the broad learning system (BLS) for predicting the 28-day mortality of patients hospitalized with community-acquired pneumonia (CAP). A total of 1,210 eligible CAP cases from Chifeng Municipal Hospital were finally included in this retrospective case-control study. Random forest (RF) and an eXtreme Gradient Boosting (XGB) models were used to develop the prediction models. The data features extracted from BLS are utilized in RF and XGB models to predict the 28-day mortality of CAP patients, which established two integrated models BLS-RF and BLS-XGB. Our results showed the integrated model BLS-XGB as an efficient broad learning system (BLS) for predicting the death risk of patients, which not only performed better than the two basic models but also performed better than the integrated model BLS-RF and two well-known deep learning systems-deep neural network (DNN) and convolutional neural network (CNN). In conclusion, BLS-XGB may be recommended as an efficient model for predicting the 28-day mortality of CAP patients after hospital admission.
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13
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Evaluation of Machine Learning Algorithms for Early Diagnosis of Deep Venous Thrombosis. MATHEMATICAL AND COMPUTATIONAL APPLICATIONS 2022. [DOI: 10.3390/mca27020024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Deep venous thrombosis (DVT) is a disease that must be diagnosed quickly, as it can trigger the death of patients. Nowadays, one can find different ways to determine it, including clinical scoring, D-dimer, ultrasonography, etc. Recently, scientists have focused efforts on using machine learning (ML) and neural networks for disease diagnosis, progressively increasing the accuracy and efficacy. Patients with suspected DVT have no apparent symptoms. Using pattern recognition techniques, aiding good timely diagnosis, as well as well-trained ML models help to make good decisions and validation. The aim of this paper is to propose several ML models for a more efficient and reliable DVT diagnosis through its implementation on an edge device for the development of instruments that are smart, portable, reliable, and cost-effective. The dataset was obtained from a state-of-the-art article. It is divided into 85% for training and cross-validation and 15% for testing. The input data in this study are the Wells criteria, the patient’s age, and the patient’s gender. The output data correspond to the patient’s diagnosis. This study includes the evaluation of several classifiers such as Decision Trees (DT), Extra Trees (ET), K-Nearest Neighbor (KNN), Multi-Layer Perceptron Neural Network (MLP-NN), Random Forest (RF), and Support Vector Machine (SVM). Finally, the implementation of these ML models on a high-performance embedded system is proposed to develop an intelligent system for early DVT diagnosis. It is reliable, portable, open source, and low cost. The performance of different ML algorithms was evaluated, where KNN achieved the highest accuracy of 90.4% and specificity of 80.66% implemented on personal computer (PC) and Raspberry Pi 4 (RPi4). The accuracy of all trained models on PC and Raspberry Pi 4 is greater than 85%, while the area under the curve (AUC) values are between 0.81 and 0.86. In conclusion, as compared to traditional methods, the best ML classifiers are effective at predicting DVT in an early and efficient manner.
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14
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Mi YH, Xu MY. Trauma-induced pulmonary thromboembolism: What's update? Chin J Traumatol 2022; 25:67-76. [PMID: 34404569 PMCID: PMC9039469 DOI: 10.1016/j.cjtee.2021.08.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 07/26/2021] [Accepted: 08/02/2021] [Indexed: 02/04/2023] Open
Abstract
Trauma-induced pulmonary thromboembolism is the second leading cause of death in severe trauma patients. Primary fibrinolytic hyperactivity combined with hemorrhage and consequential hypercoagulability in severe trauma patients create a huge challenge for clinicians. It is crucial to ensure a safe anticoagulant therapy for trauma patients, but a series of clinical issues need to be answered first, for example, what are the risk factors for traumatic venous thromboembolism? How to assess and determine the status of coagulation dysfunction of patients? When is the optimal timing to initiate pharmacologic prophylaxis for venous thromboembolism? What types of prophylactic agents should be used? How to manage the anticoagulation-related hemorrhage and to determine the optimal timing of restarting chemoprophylaxis? The present review attempts to answer the above questions.
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Lam C, Thapa R, Maharjan J, Rahmani K, Tso CF, Singh NP, Casie Chetty S, Mao Q. Multi-Task Learning with Recurrent Neural Networks for ARDS Prediction using only EHR Data: Model Development and Validation Study (Preprint). JMIR Med Inform 2022; 10:e36202. [PMID: 35704370 PMCID: PMC9244659 DOI: 10.2196/36202] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 04/07/2022] [Accepted: 05/02/2022] [Indexed: 11/24/2022] Open
Abstract
Background Acute respiratory distress syndrome (ARDS) is a condition that is often considered to have broad and subjective diagnostic criteria and is associated with significant mortality and morbidity. Early and accurate prediction of ARDS and related conditions such as hypoxemia and sepsis could allow timely administration of therapies, leading to improved patient outcomes. Objective The aim of this study is to perform an exploration of how multilabel classification in the clinical setting can take advantage of the underlying dependencies between ARDS and related conditions to improve early prediction of ARDS in patients. Methods The electronic health record data set included 40,703 patient encounters from 7 hospitals from April 20, 2018, to March 17, 2021. A recurrent neural network (RNN) was trained using data from 5 hospitals, and external validation was conducted on data from 2 hospitals. In addition to ARDS, 12 target labels for related conditions such as sepsis, hypoxemia, and COVID-19 were used to train the model to classify a total of 13 outputs. As a comparator, XGBoost models were developed for each of the 13 target labels. Model performance was assessed using the area under the receiver operating characteristic curve. Heat maps to visualize attention scores were generated to provide interpretability to the neural networks. Finally, cluster analysis was performed to identify potential phenotypic subgroups of patients with ARDS. Results The single RNN model trained to classify 13 outputs outperformed the individual XGBoost models for ARDS prediction, achieving an area under the receiver operating characteristic curve of 0.842 on the external test sets. Models trained on an increasing number of tasks resulted in improved performance. Earlier prediction of ARDS nearly doubled the rate of in-hospital survival. Cluster analysis revealed distinct ARDS subgroups, some of which had similar mortality rates but different clinical presentations. Conclusions The RNN model presented in this paper can be used as an early warning system to stratify patients who are at risk of developing one of the multiple risk outcomes, hence providing practitioners with the means to take early action.
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Affiliation(s)
- Carson Lam
- Dascena, Inc, Houston, TX, United States
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16
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Thapa R, Garikipati A, Shokouhi S, Hurtado M, Barnes G, Hoffman J, Calvert J, Katzmann L, Mao Q, Das R. Usability of Electronic Health records in Predicting Short-term falls: Machine learning Applications in Senior Care Facilities (Preprint). JMIR Aging 2021; 5:e35373. [PMID: 35363146 PMCID: PMC9015781 DOI: 10.2196/35373] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/16/2022] [Accepted: 02/07/2022] [Indexed: 11/23/2022] Open
Abstract
Background Short-term fall prediction models that use electronic health records (EHRs) may enable the implementation of dynamic care practices that specifically address changes in individualized fall risk within senior care facilities. Objective The aim of this study is to implement machine learning (ML) algorithms that use EHR data to predict a 3-month fall risk in residents from a variety of senior care facilities providing different levels of care. Methods This retrospective study obtained EHR data (2007-2021) from Juniper Communities’ proprietary database of 2785 individuals primarily residing in skilled nursing facilities, independent living facilities, and assisted living facilities across the United States. We assessed the performance of 3 ML-based fall prediction models and the Juniper Communities’ fall risk assessment. Additional analyses were conducted to examine how changes in the input features, training data sets, and prediction windows affected the performance of these models. Results The Extreme Gradient Boosting model exhibited the highest performance, with an area under the receiver operating characteristic curve of 0.846 (95% CI 0.794-0.894), specificity of 0.848, diagnostic odds ratio of 13.40, and sensitivity of 0.706, while achieving the best trade-off in balancing true positive and negative rates. The number of active medications was the most significant feature associated with fall risk, followed by a resident’s number of active diseases and several variables associated with vital signs, including diastolic blood pressure and changes in weight and respiratory rates. The combination of vital signs with traditional risk factors as input features achieved higher prediction accuracy than using either group of features alone. Conclusions This study shows that the Extreme Gradient Boosting technique can use a large number of features from EHR data to make short-term fall predictions with a better performance than that of conventional fall risk assessments and other ML models. The integration of routinely collected EHR data, particularly vital signs, into fall prediction models may generate more accurate fall risk surveillance than models without vital signs. Our data support the use of ML models for dynamic, cost-effective, and automated fall predictions in different types of senior care facilities.
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