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Guo RX, Tian X, Bazoukis G, Tse G, Hong S, Chen KY, Liu T. Application of artificial intelligence in the diagnosis and treatment of cardiac arrhythmia. Pacing Clin Electrophysiol 2024; 47:789-801. [PMID: 38712484 DOI: 10.1111/pace.14995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 03/29/2024] [Accepted: 04/18/2024] [Indexed: 05/08/2024]
Abstract
The rapid growth in computational power, sensor technology, and wearable devices has provided a solid foundation for all aspects of cardiac arrhythmia care. Artificial intelligence (AI) has been instrumental in bringing about significant changes in the prevention, risk assessment, diagnosis, and treatment of arrhythmia. This review examines the current state of AI in the diagnosis and treatment of atrial fibrillation, supraventricular arrhythmia, ventricular arrhythmia, hereditary channelopathies, and cardiac pacing. Furthermore, ChatGPT, which has gained attention recently, is addressed in this paper along with its potential applications in the field of arrhythmia. Additionally, the accuracy of arrhythmia diagnosis can be improved by identifying electrode misplacement or erroneous swapping of electrode position using AI. Remote monitoring has expanded greatly due to the emergence of contactless monitoring technology as wearable devices continue to develop and flourish. Parallel advances in AI computing power, ChatGPT, availability of large data sets, and more have greatly expanded applications in arrhythmia diagnosis, risk assessment, and treatment. More precise algorithms based on big data, personalized risk assessment, telemedicine and mobile health, smart hardware and wearables, and the exploration of rare or complex types of arrhythmia are the future direction.
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Affiliation(s)
- Rong-Xin Guo
- Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
| | - Xu Tian
- Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
| | - George Bazoukis
- Department of Cardiology, Larnaca General Hospital, Inomenon Polition Amerikis, Larnaca, Cyprus
- Department of Basic and Clinical Sciences, University of Nicosia Medical School, Nicosia, Cyprus
| | - Gary Tse
- Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
- Cardiovascular Analytics Group, PowerHealth Research Institute, Hong Kong, China
- School of Nursing and Health Studies, Hong Kong Metropolitan University, Hong Kong, China
| | - Shenda Hong
- National Institute of Health Data Science, Peking University, Beijing, China
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
| | - Kang-Yin Chen
- Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
| | - Tong Liu
- Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
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Kuziemsky CE, Chrimes D, Minshall S, Mannerow M, Lau F. AI Quality Standards in Health Care: Rapid Umbrella Review. J Med Internet Res 2024; 26:e54705. [PMID: 38776538 PMCID: PMC11153979 DOI: 10.2196/54705] [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: 11/19/2023] [Revised: 04/03/2024] [Accepted: 04/04/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND In recent years, there has been an upwelling of artificial intelligence (AI) studies in the health care literature. During this period, there has been an increasing number of proposed standards to evaluate the quality of health care AI studies. OBJECTIVE This rapid umbrella review examines the use of AI quality standards in a sample of health care AI systematic review articles published over a 36-month period. METHODS We used a modified version of the Joanna Briggs Institute umbrella review method. Our rapid approach was informed by the practical guide by Tricco and colleagues for conducting rapid reviews. Our search was focused on the MEDLINE database supplemented with Google Scholar. The inclusion criteria were English-language systematic reviews regardless of review type, with mention of AI and health in the abstract, published during a 36-month period. For the synthesis, we summarized the AI quality standards used and issues noted in these reviews drawing on a set of published health care AI standards, harmonized the terms used, and offered guidance to improve the quality of future health care AI studies. RESULTS We selected 33 review articles published between 2020 and 2022 in our synthesis. The reviews covered a wide range of objectives, topics, settings, designs, and results. Over 60 AI approaches across different domains were identified with varying levels of detail spanning different AI life cycle stages, making comparisons difficult. Health care AI quality standards were applied in only 39% (13/33) of the reviews and in 14% (25/178) of the original studies from the reviews examined, mostly to appraise their methodological or reporting quality. Only a handful mentioned the transparency, explainability, trustworthiness, ethics, and privacy aspects. A total of 23 AI quality standard-related issues were identified in the reviews. There was a recognized need to standardize the planning, conduct, and reporting of health care AI studies and address their broader societal, ethical, and regulatory implications. CONCLUSIONS Despite the growing number of AI standards to assess the quality of health care AI studies, they are seldom applied in practice. With increasing desire to adopt AI in different health topics, domains, and settings, practitioners and researchers must stay abreast of and adapt to the evolving landscape of health care AI quality standards and apply these standards to improve the quality of their AI studies.
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Affiliation(s)
| | - Dillon Chrimes
- School of Health Information Science, University of Victoria, Victoria, BC, Canada
| | - Simon Minshall
- School of Health Information Science, University of Victoria, Victoria, BC, Canada
| | | | - Francis Lau
- School of Health Information Science, University of Victoria, Victoria, BC, Canada
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Franklin G, Stephens R, Piracha M, Tiosano S, Lehouillier F, Koppel R, Elkin PL. The Sociodemographic Biases in Machine Learning Algorithms: A Biomedical Informatics Perspective. Life (Basel) 2024; 14:652. [PMID: 38929638 PMCID: PMC11204917 DOI: 10.3390/life14060652] [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: 01/21/2024] [Revised: 04/24/2024] [Accepted: 04/26/2024] [Indexed: 06/28/2024] Open
Abstract
Artificial intelligence models represented in machine learning algorithms are promising tools for risk assessment used to guide clinical and other health care decisions. Machine learning algorithms, however, may house biases that propagate stereotypes, inequities, and discrimination that contribute to socioeconomic health care disparities. The biases include those related to some sociodemographic characteristics such as race, ethnicity, gender, age, insurance, and socioeconomic status from the use of erroneous electronic health record data. Additionally, there is concern that training data and algorithmic biases in large language models pose potential drawbacks. These biases affect the lives and livelihoods of a significant percentage of the population in the United States and globally. The social and economic consequences of the associated backlash cannot be underestimated. Here, we outline some of the sociodemographic, training data, and algorithmic biases that undermine sound health care risk assessment and medical decision-making that should be addressed in the health care system. We present a perspective and overview of these biases by gender, race, ethnicity, age, historically marginalized communities, algorithmic bias, biased evaluations, implicit bias, selection/sampling bias, socioeconomic status biases, biased data distributions, cultural biases and insurance status bias, conformation bias, information bias and anchoring biases and make recommendations to improve large language model training data, including de-biasing techniques such as counterfactual role-reversed sentences during knowledge distillation, fine-tuning, prefix attachment at training time, the use of toxicity classifiers, retrieval augmented generation and algorithmic modification to mitigate the biases moving forward.
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Affiliation(s)
- Gillian Franklin
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14203, USA; (G.F.); (R.S.); (M.P.); (F.L.); (R.K.)
- Department of Veterans Affairs, Knowledge Based Systems and Western New York, Veterans Affairs, Buffalo, NY 14215, USA
| | - Rachel Stephens
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14203, USA; (G.F.); (R.S.); (M.P.); (F.L.); (R.K.)
| | - Muhammad Piracha
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14203, USA; (G.F.); (R.S.); (M.P.); (F.L.); (R.K.)
| | - Shmuel Tiosano
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14203, USA; (G.F.); (R.S.); (M.P.); (F.L.); (R.K.)
| | - Frank Lehouillier
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14203, USA; (G.F.); (R.S.); (M.P.); (F.L.); (R.K.)
- Department of Veterans Affairs, Knowledge Based Systems and Western New York, Veterans Affairs, Buffalo, NY 14215, USA
| | - Ross Koppel
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14203, USA; (G.F.); (R.S.); (M.P.); (F.L.); (R.K.)
- Institute for Biomedical Informatics, Perelman School of Medicine, and Sociology Department, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Peter L. Elkin
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14203, USA; (G.F.); (R.S.); (M.P.); (F.L.); (R.K.)
- Department of Veterans Affairs, Knowledge Based Systems and Western New York, Veterans Affairs, Buffalo, NY 14215, USA
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王 星, 李 茜, 马 彩, 张 铄, 林 钰, 李 建, 刘 澄. [Artificial intelligence in wearable electrocardiogram monitoring]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2023; 40:1084-1092. [PMID: 38151930 PMCID: PMC10753313 DOI: 10.7507/1001-5515.202301032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 10/31/2023] [Indexed: 12/29/2023]
Abstract
Electrocardiogram (ECG) monitoring owns important clinical value in diagnosis, prevention and rehabilitation of cardiovascular disease (CVD). With the rapid development of Internet of Things (IoT), big data, cloud computing, artificial intelligence (AI) and other advanced technologies, wearable ECG is playing an increasingly important role. With the aging process of the population, it is more and more urgent to upgrade the diagnostic mode of CVD. Using AI technology to assist the clinical analysis of long-term ECGs, and thus to improve the ability of early detection and prediction of CVD has become an important direction. Intelligent wearable ECG monitoring needs the collaboration between edge and cloud computing. Meanwhile, the clarity of medical scene is conducive for the precise implementation of wearable ECG monitoring. This paper first summarized the progress of AI-related ECG studies and the current technical orientation. Then three cases were depicted to illustrate how the AI in wearable ECG cooperate with the clinic. Finally, we demonstrated the two core issues-the reliability and worth of AI-related ECG technology and prospected the future opportunities and challenges.
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Affiliation(s)
- 星尧 王
- 东南大学 仪器科学与工程学院(南京 210096)School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China
- 东南大学 生物电子学国家重点实验室(南京 210096)State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, P. R. China
| | - 茜 李
- 东南大学 仪器科学与工程学院(南京 210096)School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China
- 东南大学 生物电子学国家重点实验室(南京 210096)State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, P. R. China
| | - 彩云 马
- 东南大学 仪器科学与工程学院(南京 210096)School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China
- 东南大学 生物电子学国家重点实验室(南京 210096)State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, P. R. China
| | - 铄 张
- 东南大学 仪器科学与工程学院(南京 210096)School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China
- 东南大学 生物电子学国家重点实验室(南京 210096)State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, P. R. China
| | - 钰洁 林
- 东南大学 仪器科学与工程学院(南京 210096)School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China
- 东南大学 生物电子学国家重点实验室(南京 210096)State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, P. R. China
| | - 建清 李
- 东南大学 仪器科学与工程学院(南京 210096)School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China
- 东南大学 生物电子学国家重点实验室(南京 210096)State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, P. R. China
| | - 澄玉 刘
- 东南大学 仪器科学与工程学院(南京 210096)School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China
- 东南大学 生物电子学国家重点实验室(南京 210096)State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, P. R. China
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Schopow N, Osterhoff G, Baur D. Applications of the Natural Language Processing Tool ChatGPT in Clinical Practice: Comparative Study and Augmented Systematic Review. JMIR Med Inform 2023; 11:e48933. [PMID: 38015610 DOI: 10.2196/48933] [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: 05/11/2023] [Revised: 06/20/2023] [Accepted: 08/25/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND This research integrates a comparative analysis of the performance of human researchers and OpenAI's ChatGPT in systematic review tasks and describes an assessment of the application of natural language processing (NLP) models in clinical practice through a review of 5 studies. OBJECTIVE This study aimed to evaluate the reliability between ChatGPT and human researchers in extracting key information from clinical articles, and to investigate the practical use of NLP in clinical settings as evidenced by selected studies. METHODS The study design comprised a systematic review of clinical articles executed independently by human researchers and ChatGPT. The level of agreement between and within raters for parameter extraction was assessed using the Fleiss and Cohen κ statistics. RESULTS The comparative analysis revealed a high degree of concordance between ChatGPT and human researchers for most parameters, with less agreement for study design, clinical task, and clinical implementation. The review identified 5 significant studies that demonstrated the diverse applications of NLP in clinical settings. These studies' findings highlight the potential of NLP to improve clinical efficiency and patient outcomes in various contexts, from enhancing allergy detection and classification to improving quality metrics in psychotherapy treatments for veterans with posttraumatic stress disorder. CONCLUSIONS Our findings underscore the potential of NLP models, including ChatGPT, in performing systematic reviews and other clinical tasks. Despite certain limitations, NLP models present a promising avenue for enhancing health care efficiency and accuracy. Future studies must focus on broadening the range of clinical applications and exploring the ethical considerations of implementing NLP applications in health care settings.
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Affiliation(s)
- Nikolas Schopow
- Department for Orthopedics, Trauma Surgery and Plastic Surgery, University Hospital Leipzig, Leipzig, Germany
| | - Georg Osterhoff
- Department for Orthopedics, Trauma Surgery and Plastic Surgery, University Hospital Leipzig, Leipzig, Germany
| | - David Baur
- Department for Orthopedics, Trauma Surgery and Plastic Surgery, University Hospital Leipzig, Leipzig, Germany
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Liang J, He Y, Xie J, Fan X, Liu Y, Wen Q, Shen D, Xu J, Gu S, Lei J. Mining electronic health records using artificial intelligence: Bibliometric and content analyses for current research status and product conversion. J Biomed Inform 2023; 146:104480. [PMID: 37657713 DOI: 10.1016/j.jbi.2023.104480] [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: 02/16/2023] [Revised: 07/16/2023] [Accepted: 08/27/2023] [Indexed: 09/03/2023]
Abstract
BACKGROUND The use of Electronic Health Records is the most important milestone in the digitization and intelligence of the entire medical industry. AI can effectively mine the immense medical information contained in EHRs, potentially assist doctors in reducing many medical errors. OBJECTIVE This article aims to summarize the research status and trends in using AI to mine medical information from EHRs for the past thirteen years and investigate its information application. METHODS A systematic search was carried out in 5 databases, including Web of Science Core Collection and PubMed, to identify research using AI to mine medical information from EHRs for the past thirteen years. Furthermore, bibliometric and content analysis were used to explore the research hotspots and trends, and systematically analyze the conversion rate of research resources in this field. RESULTS A total of 631 articles were included and analyzed. The number of published articles has increased rapidly after 2017, with an average annual growth rate of 55.73%. The US (41.68%) and China (19.65%) publish the most articles, but there is a lack of international cooperation. The extraction of disease lesions is a hot topic at present, and the research topic is gradually shifting from disease risk grading to disease risk prediction. Classification (66%), and regress (15%) are the main implemented AI tasks. For AI algorithms, deep learning (31.70%), decision tree algorithms family (26.47%), and regression algorithms family (17.43%) are used most frequently. The funding rate for publications is 69.26%, and the input-output conversion rate is 21.05%. CONCLUSIONS Over the past decade, the use of AI to mine medical information from EHRs has been developing rapidly. However, it is necessary to strengthen international cooperation, improve EHRs data availability, focus on interpretable AI algorithms, and improve the resource conversion rate in future research.
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Affiliation(s)
- Jun Liang
- IT Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China; Center for Health Policy Studies, School of Public Health, Zhejiang University, Hangzhou, Zhejiang Province, China; Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China; School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, China
| | - Yunfan He
- Center for Health Policy Studies, School of Public Health, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Jun Xie
- Information Technology Center, West China Hospital of Sichuan University/Engineering Research Center of Medical Information Technology, Ministry of Education, Chengdu, Sichuan Province, China
| | - Xianming Fan
- Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China
| | - Yiqi Liu
- Department of Infectious Disease, Center for Liver Disease, Peking University First Hospital, Beijing, China
| | - Qinglian Wen
- Department of Oncology, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China
| | - Dongxia Shen
- Editorial Department of Journal of Practical Oncology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Jie Xu
- IT Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Shuo Gu
- Hainan Provincial Center for Neurological Diseases, Department of Pediatric Neurosurgery of The First Affiliated Hospital, Hainan Medical University, Haikou, Hainan Province, China.
| | - Jianbo Lei
- Clinical Research Center, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China; School of Medical Information and Engineering, SouthWest Medical University, Luzhou, Sichuan Province, China; Institute of Medical Technology, Health Science Center, Peking University, Beijing, China.
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De Rosario H, Pitarch-Corresa S, Pedrosa I, Vidal-Pedrós M, de Otto-López B, García-Mieres H, Álvarez-Rodríguez L. Applications of Natural Language Processing for the Management of Stroke Disorders: Scoping Review. JMIR Med Inform 2023; 11:e48693. [PMID: 37672328 PMCID: PMC10512117 DOI: 10.2196/48693] [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: 05/03/2023] [Revised: 07/26/2023] [Accepted: 07/28/2023] [Indexed: 09/07/2023] Open
Abstract
BACKGROUND Recent advances in natural language processing (NLP) have heightened the interest of the medical community in its application to health care in general, in particular to stroke, a medical emergency of great impact. In this rapidly evolving context, it is necessary to learn and understand the experience already accumulated by the medical and scientific community. OBJECTIVE The aim of this scoping review was to explore the studies conducted in the last 10 years using NLP to assist the management of stroke emergencies so as to gain insight on the state of the art, its main contexts of application, and the software tools that are used. METHODS Data were extracted from Scopus and Medline through PubMed, using the keywords "natural language processing" and "stroke." Primary research questions were related to the phases, contexts, and types of textual data used in the studies. Secondary research questions were related to the numerical and statistical methods and the software used to process the data. The extracted data were structured in tables and their relative frequencies were calculated. The relationships between categories were analyzed through multiple correspondence analysis. RESULTS Twenty-nine papers were included in the review, with the majority being cohort studies of ischemic stroke published in the last 2 years. The majority of papers focused on the use of NLP to assist in the diagnostic phase, followed by the outcome prognosis, using text data from diagnostic reports and in many cases annotations on medical images. The most frequent approach was based on general machine learning techniques applied to the results of relatively simple NLP methods with the support of ontologies and standard vocabularies. Although smaller in number, there has been an increasing body of studies using deep learning techniques on numerical and vectorized representations of the texts obtained with more sophisticated NLP tools. CONCLUSIONS Studies focused on NLP applied to stroke show specific trends that can be compared to the more general application of artificial intelligence to stroke. The purpose of using NLP is often to improve processes in a clinical context rather than to assist in the rehabilitation process. The state of the art in NLP is represented by deep learning architectures, among which Bidirectional Encoder Representations from Transformers has been found to be especially widely used in the medical field in general, and for stroke in particular, with an increasing focus on the processing of annotations on medical images.
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Affiliation(s)
- Helios De Rosario
- Instituto de Biomecánica de Valencia, Universitat Politècnica de València, Valencia, Spain
| | | | - Ignacio Pedrosa
- CTIC Centro Tecnológico de la Información y la Comunicación, Gijón, Spain
| | - Marina Vidal-Pedrós
- Instituto de Biomecánica de Valencia, Universitat Politècnica de València, Valencia, Spain
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Abstract
This Viewpoint discusses ways that artificial intelligence (AI) may improve the productivity of primary care physicians with easier and more accurate use of AI-enhanced electronic health records.
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Affiliation(s)
- Jeffrey E Harris
- Adult Medicine Department, Eisner Health, Los Angeles, California
- Department of Economics, Massachusetts Institute of Technology, Cambridge
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Laursen MS, Pedersen JS, Hansen RS, Savarimuthu TR, Lynggaard RB, Vinholt PJ. Doctors Identify Hemorrhage Better during Chart Review when Assisted by Artificial Intelligence. Appl Clin Inform 2023; 14:743-751. [PMID: 37399838 PMCID: PMC10511273 DOI: 10.1055/a-2121-8380] [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: 04/04/2023] [Accepted: 06/29/2023] [Indexed: 07/05/2023] Open
Abstract
OBJECTIVES This study evaluated if medical doctors could identify more hemorrhage events during chart review in a clinical setting when assisted by an artificial intelligence (AI) model and medical doctors' perception of using the AI model. METHODS To develop the AI model, sentences from 900 electronic health records were labeled as positive or negative for hemorrhage and categorized into one of 12 anatomical locations. The AI model was evaluated on a test cohort consisting of 566 admissions. Using eye-tracking technology, we investigated medical doctors' reading workflow during manual chart review. Moreover, we performed a clinical use study where medical doctors read two admissions with and without AI assistance to evaluate performance when using and perception of using the AI model. RESULTS The AI model had a sensitivity of 93.7% and a specificity of 98.1% on the test cohort. In the use studies, we found that medical doctors missed more than 33% of relevant sentences when doing chart review without AI assistance. Hemorrhage events described in paragraphs were more often overlooked compared with bullet-pointed hemorrhage mentions. With AI-assisted chart review, medical doctors identified 48 and 49 percentage points more hemorrhage events than without assistance in two admissions, and they were generally positive toward using the AI model as a supporting tool. CONCLUSION Medical doctors identified more hemorrhage events with AI-assisted chart review and they were generally positive toward using the AI model.
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Affiliation(s)
- Martin S. Laursen
- SDU Robotics, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark
| | - Jannik S. Pedersen
- SDU Robotics, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark
| | - Rasmus S. Hansen
- Department of Clinical Biochemistry, Odense University Hospital, Odense, Denmark
| | - Thiusius R. Savarimuthu
- SDU Robotics, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark
| | - Rasmus B. Lynggaard
- Department of Clinical Biochemistry, Odense University Hospital, Odense, Denmark
| | - Pernille J. Vinholt
- Department of Clinical Biochemistry, Odense University Hospital, Odense, Denmark
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Jaques-Albuquerque LT, Dos Anjos-Martins E, Torres-Nunes L, Valério-Penha AG, Coelho-Oliveira AC, da Silva Sarandy VL, Reis-Silva A, Seixas A, Bernardo-Filho M, Taiar R, de Sá-Caputo DC. Effectiveness of Using the FreeStyle Libre ® System for Monitoring Blood Glucose during the COVID-19 Pandemic in Diabetic Individuals: Systematic Review. Diagnostics (Basel) 2023; 13:diagnostics13081499. [PMID: 37189600 DOI: 10.3390/diagnostics13081499] [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: 03/15/2023] [Revised: 03/31/2023] [Accepted: 04/10/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND Artificial Intelligence (AI) is an area of computer science/engineering that is aiming to spread technological systems. The COVID-19 pandemic caused economic and public health turbulence around the world. Among the many possibilities for using AI in the medical field is FreeStyle Libre® (FSL), which uses a disposable sensor inserted into the user's arm, and a touchscreen device/reader is used to scan and retrieve other continuous monitoring of glucose (CMG) readings. The aim of this systematic review is to summarize the effectiveness of FSL blood glucose monitoring during the COVID-19 pandemic. METHODS This systematic review was carried out in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) and was registered in the international prospective register of systematic reviews (PROSPERO: CRD42022340562). The inclusion criteria considered studies involving the use of the FSL device during the COVID-19 pandemic and published in English. No publication date restrictions were set. The exclusion criteria were abstracts, systematic reviews, studies with patients with other diseases, monitoring with other equipment, patients with COVID-19, and bariatrics patients. Seven databases were searched (PubMed, Scopus, Embase, Web of Science, Scielo, PEDro and Cochrane Library). The ACROBAT-NRSI tool (A Cochrane Risk of Bias Assessment Tool for Non-Randomized Studies) was used to evaluate the risk of bias in the selected articles. RESULTS A total of 113 articles were found. Sixty-four were excluded because they were duplicates, 39 were excluded after reading the titles and abstracts, and twenty articles were considered for full reading. Of the 10 articles analyzed, four articles were excluded because they did not meet the inclusion criteria. Thus, six articles were included in the current systematic review. It was observed that among the selected articles, only two were classified as having serious risk of bias. It was shown that FSL had a positive impact on glycemic control and on reducing the number of individuals with hypoglycemia. CONCLUSION The findings suggest that the implementation of FSL during COVID-19 confinement in this population can be confidently stated to have been effective in diabetes mellitus patients.
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Affiliation(s)
- Luelia Teles Jaques-Albuquerque
- Laboratório de Vibrações Mecânicas e Práticas Integrativas, Departamento de Biofísica e Biometria, Instituto de Biologia Roberto Alcântara Gomes, Policlínica Universitária Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil
| | - Elzi Dos Anjos-Martins
- Laboratório de Vibrações Mecânicas e Práticas Integrativas, Departamento de Biofísica e Biometria, Instituto de Biologia Roberto Alcântara Gomes, Policlínica Universitária Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil
- Mestrado Profissional em Saúde, Medicina Laboratorial e Tecnologia Forense, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil
| | - Luiza Torres-Nunes
- Laboratório de Vibrações Mecânicas e Práticas Integrativas, Departamento de Biofísica e Biometria, Instituto de Biologia Roberto Alcântara Gomes, Policlínica Universitária Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil
- Programa de Pós-Graduação em Fisiopatologia Clínica e Experimental, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil
| | - Ana Gabriellie Valério-Penha
- Laboratório de Vibrações Mecânicas e Práticas Integrativas, Departamento de Biofísica e Biometria, Instituto de Biologia Roberto Alcântara Gomes, Policlínica Universitária Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil
| | - Ana Carolina Coelho-Oliveira
- Laboratório de Vibrações Mecânicas e Práticas Integrativas, Departamento de Biofísica e Biometria, Instituto de Biologia Roberto Alcântara Gomes, Policlínica Universitária Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil
- Mestrado Profissional em Saúde, Medicina Laboratorial e Tecnologia Forense, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil
| | - Viviani Lopes da Silva Sarandy
- Laboratório de Vibrações Mecânicas e Práticas Integrativas, Departamento de Biofísica e Biometria, Instituto de Biologia Roberto Alcântara Gomes, Policlínica Universitária Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil
| | - Aline Reis-Silva
- Laboratório de Vibrações Mecânicas e Práticas Integrativas, Departamento de Biofísica e Biometria, Instituto de Biologia Roberto Alcântara Gomes, Policlínica Universitária Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil
- Programa de Pós-Graduação em Ciências Médicas, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil
| | - Adérito Seixas
- Escola Superior de Saúde Fernando Pessoa, 4200-256 Porto, Portugal
| | - Mario Bernardo-Filho
- Laboratório de Vibrações Mecânicas e Práticas Integrativas, Departamento de Biofísica e Biometria, Instituto de Biologia Roberto Alcântara Gomes, Policlínica Universitária Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil
| | - Redha Taiar
- MATériaux et Ingénierie Mécanique (MATIM), Université de Reims Champagne Ardenne, 51100 Reims, France
| | - Danúbia Cunha de Sá-Caputo
- Laboratório de Vibrações Mecânicas e Práticas Integrativas, Departamento de Biofísica e Biometria, Instituto de Biologia Roberto Alcântara Gomes, Policlínica Universitária Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil
- Mestrado Profissional em Saúde, Medicina Laboratorial e Tecnologia Forense, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil
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11
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Zhao Y, Cao LY, Zhao YX, Wang F, Xie LL, Xing HY, Wang Q. Medical record data-enabled machine learning can enhance prediction of left atrial appendage thrombosis in nonvalvular atrial fibrillation. Thromb Res 2023; 223:174-183. [PMID: 36764084 DOI: 10.1016/j.thromres.2023.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/16/2022] [Accepted: 01/03/2023] [Indexed: 01/09/2023]
Abstract
BACKGROUND As a major complication of non-valvular atrial fibrillation (NVAF), left atrial appendage (LAA) thrombosis is associated with cerebral ischemic strokes, as well as high morbidity. Due to insufficient incorporation of risk factors, most current scoring methods are limited to the analysis of relationships between clinical characteristics and LAA thrombosis rather than detecting potential risk. Therefore, this study proposes a clinical data-driven machine learning method to predict LAA thrombosis of NVAF. METHODS Patients with NVAF from January 2014 to June 2022 were enrolled from Southwest Hospital. We selected 40 variables for analysis, including demographic data, medical history records, laboratory results, and the structure of LAA. Three machine learning algorithms were adopted to construct classifiers for the prediction of LAA thrombosis risk. The most important variables related to LAA thrombosis and their influences were recognized by SHapley Addictive exPlanations method. In addition, we compared our model with CHADS2 and CHADS2-VASc scoring methods. RESULTS A total of 713 participants were recruited, including 127 patients with LAA thrombosis and 586 patients with no obvious thrombosis. The consensus models based on Random Forest and eXtreme Gradient Boosting LAA thrombosis prediction (RXTP) achieved the best accuracy of 0.865, significantly outperforming CHADS2 score and CHA2DS2-VASc score (0.757 and 0.754, respectively). The SHAP results showed that B-type natriuretic peptide, left atrial appendage width, C-reactive protein, Fibrinogen and estimated glomerular filtration rate are closely related to the risk of LAA thrombosis in nonvalvular atrial fibrillation. CONCLUSIONS The RXTP-NVAF model is the most effective model with the greatest ROC value and recall rate. The summarized risk factors obtained from SHAP enable the optimization of the treatment strategy, thereby preventing thromboembolism events and the occurrence of cardiogenic ischemic stroke.
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Affiliation(s)
- Yue Zhao
- Department of Pharmacy, the First Affiliated Hospital of Army Medical University (Third Military Medical University),Chongqing, China
| | - Li-Ya Cao
- Department of Pharmacy, the First Affiliated Hospital of Army Medical University (Third Military Medical University),Chongqing, China
| | - Ying-Xin Zhao
- Department of Pharmacy, Army Medical Center, Army Medical University (Third Military Medical University),Chongqing, China
| | - Fei Wang
- Medical Big Data and Artificial Intelligence Center, the First Affiliated Hospital of Army Medical University (Third Military Medical University), Chongqing, China
| | - Lin-Li Xie
- Department of Pharmacy, the First Affiliated Hospital of Army Medical University (Third Military Medical University),Chongqing, China
| | - Hai-Yan Xing
- Department of Pharmacy, Army Medical Center, Army Medical University (Third Military Medical University),Chongqing, China.
| | - Qian Wang
- Department of Pharmacy, the First Affiliated Hospital of Army Medical University (Third Military Medical University),Chongqing, China.
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12
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Shipley E, Joddrell M, Lip GY, Zheng Y. Bridging the Gap Between Artificial Intelligence Research and Clinical Practice in Cardiovascular Science: What the Clinician Needs to Know. Arrhythm Electrophysiol Rev 2022; 11:e03. [PMID: 35519510 PMCID: PMC9062708 DOI: 10.15420/aer.2022.07] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 02/04/2022] [Indexed: 12/02/2022] Open
Affiliation(s)
- Emily Shipley
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart and Chest Hospital, Liverpool, UK.,Department of Cardiovascular and Metabolic Medicine, University of Liverpool, Liverpool, UK
| | - Martha Joddrell
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart and Chest Hospital, Liverpool, UK.,Department of Cardiovascular and Metabolic Medicine, University of Liverpool, Liverpool, UK
| | - Gregory Yh Lip
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart and Chest Hospital, Liverpool, UK.,Department of Cardiovascular and Metabolic Medicine, University of Liverpool, Liverpool, UK
| | - Yalin Zheng
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart and Chest Hospital, Liverpool, UK.,Department of Eye and Vision Science, University of Liverpool, Liverpool, UK
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13
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Chiavi D, Haag C, Chan A, Kamm CP, Sieber C, Stanikić M, Rodgers S, Pot C, Kesselring J, Salmen A, Rapold I, Calabrese P, Manjaly ZM, Gobbi C, Zecca C, Walther S, Stegmayer K, Hoepner R, Puhan M, von Wyl V. Studying Real-World Experiences of Persons with Multiple Sclerosis during the first Covid-19 Lockdown: An Application of Natural Language Processing (Preprint). JMIR Med Inform 2022; 10:e37945. [DOI: 10.2196/37945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 10/02/2022] [Indexed: 11/06/2022] Open
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14
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Li S, Deng L, Zhang X, Chen L, Yang T, Qi Y, Jiang T. Deep Phenotyping on Chinese Electronic Health Records by Recognizing Linguistic Patterns of Phenotypic Narratives with a Sequence Motif Discovery Tool: Algorithm Development and Validation (Preprint). J Med Internet Res 2022; 24:e37213. [PMID: 35657661 PMCID: PMC9206202 DOI: 10.2196/37213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 04/21/2022] [Accepted: 05/12/2022] [Indexed: 11/23/2022] Open
Abstract
Background Phenotype information in electronic health records (EHRs) is mainly recorded in unstructured free text, which cannot be directly used for clinical research. EHR-based deep-phenotyping methods can structure phenotype information in EHRs with high fidelity, making it the focus of medical informatics. However, developing a deep-phenotyping method for non-English EHRs (ie, Chinese EHRs) is challenging. Although numerous EHR resources exist in China, fine-grained annotation data that are suitable for developing deep-phenotyping methods are limited. It is challenging to develop a deep-phenotyping method for Chinese EHRs in such a low-resource scenario. Objective In this study, we aimed to develop a deep-phenotyping method with good generalization ability for Chinese EHRs based on limited fine-grained annotation data. Methods The core of the methodology was to identify linguistic patterns of phenotype descriptions in Chinese EHRs with a sequence motif discovery tool and perform deep phenotyping of Chinese EHRs by recognizing linguistic patterns in free text. Specifically, 1000 Chinese EHRs were manually annotated based on a fine-grained information model, PhenoSSU (Semantic Structured Unit of Phenotypes). The annotation data set was randomly divided into a training set (n=700, 70%) and a testing set (n=300, 30%). The process for mining linguistic patterns was divided into three steps. First, free text in the training set was encoded as single-letter sequences (P: phenotype, A: attribute). Second, a biological sequence analysis tool—MEME (Multiple Expectation Maximums for Motif Elicitation)—was used to identify motifs in the single-letter sequences. Finally, the identified motifs were reduced to a series of regular expressions representing linguistic patterns of PhenoSSU instances in Chinese EHRs. Based on the discovered linguistic patterns, we developed a deep-phenotyping method for Chinese EHRs, including a deep learning–based method for named entity recognition and a pattern recognition–based method for attribute prediction. Results In total, 51 sequence motifs with statistical significance were mined from 700 Chinese EHRs in the training set and were combined into six regular expressions. It was found that these six regular expressions could be learned from a mean of 134 (SD 9.7) annotated EHRs in the training set. The deep-phenotyping algorithm for Chinese EHRs could recognize PhenoSSU instances with an overall accuracy of 0.844 on the test set. For the subtask of entity recognition, the algorithm achieved an F1 score of 0.898 with the Bidirectional Encoder Representations from Transformers–bidirectional long short-term memory and conditional random field model; for the subtask of attribute prediction, the algorithm achieved a weighted accuracy of 0.940 with the linguistic pattern–based method. Conclusions We developed a simple but effective strategy to perform deep phenotyping of Chinese EHRs with limited fine-grained annotation data. Our work will promote the second use of Chinese EHRs and give inspiration to other non–English-speaking countries.
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Affiliation(s)
- Shicheng Li
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Suzhou Institute of Systems Medicine, Suzhou, China
| | - Lizong Deng
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Suzhou Institute of Systems Medicine, Suzhou, China
| | - Xu Zhang
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Suzhou Institute of Systems Medicine, Suzhou, China
| | - Luming Chen
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Suzhou Institute of Systems Medicine, Suzhou, China
- Guangzhou Laboratory, Guangzhou, China
| | - Tao Yang
- Guangzhou Laboratory, Guangzhou, China
- Guangzhou Medical University, Guangzhou, China
| | - Yifan Qi
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Suzhou Institute of Systems Medicine, Suzhou, China
| | - Taijiao Jiang
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Suzhou Institute of Systems Medicine, Suzhou, China
- Guangzhou Laboratory, Guangzhou, China
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