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Dolatabadi E, Moyano D, Bales M, Spasojevic S, Bhambhoria R, Bhatti J, Debnath S, Hoell N, Li X, Leng C, Nanda S, Saab J, Sahak E, Sie F, Uppal S, Vadlamudi NK, Vladimirova A, Yakimovich A, Yang X, Kocak SA, Cheung AM. Using Social Media to Help Understand Patient-Reported Health Outcomes of Post-COVID-19 Condition: Natural Language Processing Approach. J Med Internet Res 2023; 25:e45767. [PMID: 37725432 PMCID: PMC10510753 DOI: 10.2196/45767] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 05/18/2023] [Accepted: 06/05/2023] [Indexed: 09/21/2023] Open
Abstract
BACKGROUND While scientific knowledge of post-COVID-19 condition (PCC) is growing, there remains significant uncertainty in the definition of the disease, its expected clinical course, and its impact on daily functioning. Social media platforms can generate valuable insights into patient-reported health outcomes as the content is produced at high resolution by patients and caregivers, representing experiences that may be unavailable to most clinicians. OBJECTIVE In this study, we aimed to determine the validity and effectiveness of advanced natural language processing approaches built to derive insight into PCC-related patient-reported health outcomes from social media platforms Twitter and Reddit. We extracted PCC-related terms, including symptoms and conditions, and measured their occurrence frequency. We compared the outputs with human annotations and clinical outcomes and tracked symptom and condition term occurrences over time and locations to explore the pipeline's potential as a surveillance tool. METHODS We used bidirectional encoder representations from transformers (BERT) models to extract and normalize PCC symptom and condition terms from English posts on Twitter and Reddit. We compared 2 named entity recognition models and implemented a 2-step normalization task to map extracted terms to unique concepts in standardized terminology. The normalization steps were done using a semantic search approach with BERT biencoders. We evaluated the effectiveness of BERT models in extracting the terms using a human-annotated corpus and a proximity-based score. We also compared the validity and reliability of the extracted and normalized terms to a web-based survey with more than 3000 participants from several countries. RESULTS UmlsBERT-Clinical had the highest accuracy in predicting entities closest to those extracted by human annotators. Based on our findings, the top 3 most commonly occurring groups of PCC symptom and condition terms were systemic (such as fatigue), neuropsychiatric (such as anxiety and brain fog), and respiratory (such as shortness of breath). In addition, we also found novel symptom and condition terms that had not been categorized in previous studies, such as infection and pain. Regarding the co-occurring symptoms, the pair of fatigue and headaches was among the most co-occurring term pairs across both platforms. Based on the temporal analysis, the neuropsychiatric terms were the most prevalent, followed by the systemic category, on both social media platforms. Our spatial analysis concluded that 42% (10,938/26,247) of the analyzed terms included location information, with the majority coming from the United States, United Kingdom, and Canada. CONCLUSIONS The outcome of our social media-derived pipeline is comparable with the results of peer-reviewed articles relevant to PCC symptoms. Overall, this study provides unique insights into patient-reported health outcomes of PCC and valuable information about the patient's journey that can help health care providers anticipate future needs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.1101/2022.12.14.22283419.
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Affiliation(s)
- Elham Dolatabadi
- Faculty of Health, School of Health Policy and Management, York University, Toronto, ON, Canada
- Vector Institute, Toronto, ON, Canada
- Department of Medicine and Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | | | | | | | - Rohan Bhambhoria
- Electrical and Computer Engineering, Queen's University, Kingston, ON, Canada
| | | | | | | | - Xin Li
- Department of Medicine and Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | | | | | - Jad Saab
- TELUS Health, Montreal, QC, Canada
| | - Esmat Sahak
- Department of Medicine and Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Fanny Sie
- Hoffmann-La Roche Ltd, Toronto, ON, Canada
| | | | - Nirma Khatri Vadlamudi
- Department of Pediatrics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | | | | | | | | | - Angela M Cheung
- Department of Medicine and Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
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Recio-Garcia JA, Diaz-Agudo B, Acuaviva A. Becalm: Intelligent Monitoring of Respiratory Patients. IEEE J Biomed Health Inform 2023; 27:3806-3817. [PMID: 37192034 DOI: 10.1109/jbhi.2023.3276638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
The Becalm project is an open and low-cost solution for the remote monitoring of respiratory support therapies like the ones used in COVID-19 patients. Becalm combines a decision-making system based on Case-Based Reasoning with a low-cost, non-invasive mask that enables the remote monitoring, detection, and explanation of risk situations for respiratory patients. This paper first describes the mask and the sensors that allow remote monitoring. Then, it describes the intelligent decision-making system that detects anomalies and raises early warnings. This detection is based on the comparison of cases that represent patients using a set of static variables plus the dynamic vector of the patient time series from sensors. Finally, personalized visual reports are created to explain the causes of the warning, data patterns, and patient context to the healthcare professional. To evaluate the case-based early-warning system, we use a synthetic data generator that simulates patients' clinical evolution from the physiological features and factors described in healthcare literature. This generation process has been verified with a real dataset and allows the validation of the reasoning system with noisy and incomplete data, threshold values, and life/death situations. The evaluation demonstrates promising results and good accuracy (0.91) for the proposed low-cost solution to monitor respiratory patients.
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Shafi I, Fatima A, Afzal H, Díez IDLT, Lipari V, Breñosa J, Ashraf I. A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health. Diagnostics (Basel) 2023; 13:2196. [PMID: 37443594 DOI: 10.3390/diagnostics13132196] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 06/14/2023] [Accepted: 06/23/2023] [Indexed: 07/15/2023] Open
Abstract
Artificial intelligence has made substantial progress in medicine. Automated dental imaging interpretation is one of the most prolific areas of research using AI. X-ray and infrared imaging systems have enabled dental clinicians to identify dental diseases since the 1950s. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, and machine- and deep-learning models for dental disease diagnoses using X-ray and near-infrared imagery. Despite the notable development of AI in dentistry, certain factors affect the performance of the proposed approaches, including limited data availability, imbalanced classes, and lack of transparency and interpretability. Hence, it is of utmost importance for the research community to formulate suitable approaches, considering the existing challenges and leveraging findings from the existing studies. Based on an extensive literature review, this survey provides a brief overview of X-ray and near-infrared imaging systems. Additionally, a comprehensive insight into challenges faced by researchers in the dental domain has been brought forth in this survey. The article further offers an amalgamative assessment of both performances and methods evaluated on public benchmarks and concludes with ethical considerations and future research avenues.
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Affiliation(s)
- Imran Shafi
- College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
| | - Anum Fatima
- National Centre for Robotics, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
| | - Hammad Afzal
- Military College of Signals (MCS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
| | - Isabel de la Torre Díez
- Department of Signal Theory and Communications and Telematic Engineering, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
| | - Vivian Lipari
- Research Unit in Food Technologies, Agro-Food Industries and Nutrition, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
- Research Unit in Food Technologies, Agro-Food Industries and Nutrition, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
- Research Unit in Food Technologies, Agro-Food Industries and Nutrition, Fundación Universitaria Internacional de Colombia, Bogotá 111311, Colombia
| | - Jose Breñosa
- Research Unit in Food Technologies, Agro-Food Industries and Nutrition, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
- Universidade Internacional do Cuanza, Cuito EN250, Bié, Angola
- Research Unit in Food Technologies, Agro-Food Industries and Nutrition, Universidad Internacional Iberoamericana Arecibo, Puerto Rico, PR 00613, USA
| | - Imran Ashraf
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
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Mijanur Rahman M, Khatun F. Challenges and Prospective of AI and 5G-Enabled Technologies in Emerging Applications during the Pandemic. ARTIF INTELL 2023. [DOI: 10.5772/intechopen.109450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
5G is being implemented in the Internet of things (IoT) era. This book chapter focuses on 5G technology and the integration of other digital technologies, such as artificial intelligence (AI) and machine learning, IoT, big data analytics, cloud computing, robotics, and other digital platforms into new healthcare applications. Now, the healthcare industry is implementing 5G-enabled technology to improve health services, medical research, quality of life, and medical professionals’ and patients’ experiences everywhere, at any time. Technology can facilitate faster medical research progress and better clinical and social services management. Furthermore, AI approaches with 5G connectivity may be able to combat the epidemic challenges with minimal resources. This book chapter underlines how 5G technology is growing to address epidemic concerns. The study highlights many technical issues and future developments for creating 5G-powered healthcare solutions. This chapter also addresses the key challenges AI and 5G technology face in emerging healthcare solutions. In addition, this book chapter highlights perspective, policy recommendations, and future research directions of AI and 5G-enabled technologies in confronting future pandemics. More research will be incorporated into future projects, including studies on developing a digital society based on 5G technology in healthcare emergencies.
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Zhang B, Ming C. Digital Transformation and Open Innovation Planning of Response to COVID-19 Outbreak: A Systematic Literature Review and Future Research Agenda. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:2731. [PMID: 36768096 PMCID: PMC9916385 DOI: 10.3390/ijerph20032731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/30/2023] [Accepted: 01/31/2023] [Indexed: 06/18/2023]
Abstract
The COVID-19 pandemic highlights the importance of digital technology in a specific region's epidemic prevention and control, and the digital transformation strategy based on the open innovation system is an emerging way to tackle conceivable outbreaks. Based on the bibliometric study of relevant literature data, this paper evaluated the research and development status in this field, and conducted a systematic literature review on the basis of the core articles identified. The results of bibliometric analysis software, including CiteSpace, CitNetExplorer and VOSViewer, showed that the development of relevant research presented rapidity and decentralization, and the evolution process of literature topics further implies the necessity of interdisciplinary and multisectoral collaboration. Furthermore, this paper summarized the specific implementation strategies for constructing an open innovation system, and discussed the role and development plan of digital technology in epidemic prevention and control.
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Affiliation(s)
- Ben Zhang
- Law School, Huazhong University of Science and Technology, Wuhan 430074, China
- Sino-European Institute for Intellectual Property, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Chenxu Ming
- Sino-European Institute for Intellectual Property, Huazhong University of Science and Technology, Wuhan 430074, China
- School of Management, Huazhong University of Science and Technology, Wuhan 430074, China
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Wang S, Li M, Ng SB. Research on Infant Health Diagnosis and Intelligence Development Based on Machine Learning and Health Information Statistics. Front Public Health 2022; 10:846598. [PMID: 35719653 PMCID: PMC9201248 DOI: 10.3389/fpubh.2022.846598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 02/22/2022] [Indexed: 11/18/2022] Open
Abstract
Intelligent health diagnosis for young children aims at maintaining and promoting the healthy development of young children, aiming to make young children have a healthy state and provide a better future for their physical and mental health development. The biological basis of intelligence is the structure and function of human brain and the key to improve the intelligence level of infants is to improve the quality of brain development, especially the early development of brain. Based on machine learning and health information statistics, this paper studies the development of infant health diagnosis and intelligence, physical and mental health. Pre-process the sample data, and use the filtering method based on machine learning and health information statistics for feature screening. Compared with traditional statistical methods, machine learning and health information statistical methods can better obtain the hidden information in the big data of children's physical and mental health development, and have better learning ability and generalization ability. The machine learning theory is used to analyze and mine the infant's health diagnosis and intelligence development, establish a health state model, and intuitively show people the health status of their infant's physical and mental health development by means of data. Moreover, the accumulation of these big data is very important in the field of medical and health research driven by big data.
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Affiliation(s)
- Siyu Wang
- Teachers College, Chengdu University, Chengdu, China
| | - Min Li
- Teachers College, Chengdu University, Chengdu, China
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Li D, Liu X. Design of an Incremental Music Teaching and Assisted Therapy System Based on Artificial Intelligence Attention Mechanism. Occup Ther Int 2022; 2022:7117986. [PMID: 35821708 PMCID: PMC9225859 DOI: 10.1155/2022/7117986] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 05/30/2022] [Accepted: 06/03/2022] [Indexed: 12/25/2022] Open
Abstract
With the continuous updating and advancement of artificial intelligence technology, it gradually begins to shine in various industries, especially playing an increasingly important role in incremental music teaching and assisted therapy systems. This study designs artificial intelligence models from the perspectives of attention mechanism, contextual information guidance, and distant dependencies combined with incremental music teaching for the segmentation of MS (multiple sclerosis) lesions and achieves the automatic and accurate segmentation of MS lesions through the multidimensional analysis of multimodal magnetic resonance imaging data, which provides a basis for physicians to quantitatively analyze MS lesions, thus assisting them in the diagnosis and treatment of MS. To address the highly variable characteristics of MS lesion location, size, number, and shape, this paper firstly designs a 3D context-guided module based on Kronecker convolution to integrate lesion information from different fields of view, starting from lesion contextual information capture. Then, a 3D spatial attention module is introduced to enhance the representation of lesion features in MRI images. The experiments in this paper confirm that the context-guided module, cross-dimensional cross-attention module, and multidimensional feature similarity module designed for the characteristics of MS lesions are effective, and the proposed attentional context U-Net and multidimensional cross-attention U-Net have greater advantages in the objective evaluation index of lesion segmentation, while being combined with the incremental music teaching approach to assist treatment, which provides a new idea for the intelligent assisted treatment approach. In this paper, from algorithm design to experimental validation, both in terms of accuracy, the operational difficulty of the experiment, consumption of arithmetic power, and time cost, the unique superiority of the artificial intelligence attention-based combined with incremental music teaching adjunctive therapy system proposed in this paper can be seen in the MS lesion segmentation task.
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Affiliation(s)
- Dapeng Li
- Department of Music and Dance, Changzhi University, Changzhi, Shanxi 046011, China
| | - Xiaoguang Liu
- Changzhi Medical College, Changzhi, Shanxi 046011, China
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Farhang-Sardroodi S, Ghaemi MS, Craig M, Ooi HK, Heffernan JM. A machine learning approach to differentiate between COVID-19 and influenza infection using synthetic infection and immune response data. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:5813-5831. [PMID: 35603380 DOI: 10.3934/mbe.2022272] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Data analysis is widely used to generate new insights into human disease mechanisms and provide better treatment methods. In this work, we used the mechanistic models of viral infection to generate synthetic data of influenza and COVID-19 patients. We then developed and validated a supervised machine learning model that can distinguish between the two infections. Influenza and COVID-19 are contagious respiratory illnesses that are caused by different pathogenic viruses but appeared with similar initial presentations. While having the same primary signs COVID-19 can produce more severe symptoms, illnesses, and higher mortality. The predictive model performance was externally evaluated by the ROC AUC metric (area under the receiver operating characteristic curve) on 100 virtual patients from each cohort and was able to achieve at least AUC = 91% using our multiclass classifier. The current investigation highlighted the ability of machine learning models to accurately identify two different diseases based on major components of viral infection and immune response. The model predicted a dominant role for viral load and productively infected cells through the feature selection process.
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Affiliation(s)
- Suzan Farhang-Sardroodi
- Modelling Infection and Immunity Lab, Mathematics Statistics, York University, Toronto, Canada
- Centre for Disease Modelling (CDM), Mathematics Statistics, York University, Toronto, Canada
| | - Mohammad Sajjad Ghaemi
- Digital Technologies Research Centre, National Research Council Canada, Toronto, ON, Canada
| | - Morgan Craig
- Sainte-Justine University Hospital Research Centre and Department of Mathematics and Statistics, Université de Montréal, Montreal, Quebec, Canada
| | - Hsu Kiang Ooi
- Digital Technologies Research Centre, National Research Council Canada, Toronto, ON, Canada
| | - Jane M Heffernan
- Modelling Infection and Immunity Lab, Mathematics Statistics, York University, Toronto, Canada
- Centre for Disease Modelling (CDM), Mathematics Statistics, York University, Toronto, Canada
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Gomes BM, Rebelo CB, Alves de Sousa L. Public health, surveillance systems and preventive medicine in an interconnected world. One Health 2022. [DOI: 10.1016/b978-0-12-822794-7.00006-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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10
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Al Khalili S, Al Maani A, Al Wahaibi A, Al Yaquobi F, Al-Jardani A, Al Harthi K, Alqayoudhi A, Al Manji A, Al Rawahi B, Al-Abri S. Challenges and Opportunities for Public Health Service in Oman From the COVID-19 Pandemic: Learning Lessons for a Better Future. Front Public Health 2021; 9:770946. [PMID: 34957024 PMCID: PMC8695806 DOI: 10.3389/fpubh.2021.770946] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Accepted: 11/16/2021] [Indexed: 12/25/2022] Open
Abstract
Despite the apparent challenges inflicted by COVID-19 globally, the pandemic provided an opportunity to utilize and expand existing public health capacities for a more adaptive and resilient system during and after each wave of the disease. This paper provides a narrative review of Oman's public health response to the COVID-19 pandemic from January 2020 to July 2021, and the challenges it faced for a more rapid and efficient response. The review demonstrates that the three main pillars influencing the direction of the pandemic and aiding the control are Oman's unified governmental leadership, the move to expand the capacity of the health care system at all levels, and community partnership in all stages of the response including the COVID-19 vaccination campaign. The opportunities identified during response stages in the harmonization of the multisectoral response, streamlining communication channels, addressing vulnerable communities (dormitories, residences at border regions), and providing professional technical leadership provide an excellent precursor for expediting the transformation of Oman's health care system to one with a multisectoral holistic approach. Some of the major challenges faced are the shortage of the public health cadre, lack of a fully integrated digital platform for surveillance, and the scarcity of experts in risk communication and community engagement. A future health system where the center for diseases surveillance and control acts as a nucleus for multisectoral expertise and leadership, which includes community representatives, is crucial to attain optimum health. The destruction inflicted by this prolong COVID-19 pandemic at all levels of human life had valued the importance of investing on preventive and preparedness strategies.
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Affiliation(s)
- Sulien Al Khalili
- Directorate General for Disease Surveillance and Control, Ministry of Health, Muscat, Oman
| | - Amal Al Maani
- Directorate General for Disease Surveillance and Control, Ministry of Health, Muscat, Oman
| | - Adil Al Wahaibi
- Directorate General for Disease Surveillance and Control, Ministry of Health, Muscat, Oman
| | - Fatma Al Yaquobi
- Directorate General for Disease Surveillance and Control, Ministry of Health, Muscat, Oman
| | - Amina Al-Jardani
- Directorate General for Disease Surveillance and Control, Ministry of Health, Muscat, Oman
| | - Khalid Al Harthi
- Directorate General for Disease Surveillance and Control, Ministry of Health, Muscat, Oman
| | - Abdullah Alqayoudhi
- Directorate General for Disease Surveillance and Control, Ministry of Health, Muscat, Oman
| | - Abdullah Al Manji
- Directorate General for Disease Surveillance and Control, Ministry of Health, Muscat, Oman
| | - Bader Al Rawahi
- Directorate General for Disease Surveillance and Control, Ministry of Health, Muscat, Oman
| | - Seif Al-Abri
- Directorate General for Disease Surveillance and Control, Ministry of Health, Muscat, Oman
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Smit JM, van Genderen ME, Reinders MJT, Gommers DAMPJ, Krijthe JH, Van Bommel J. Demystifying machine learning for mortality prediction. Crit Care 2021; 25:447. [PMID: 34949229 PMCID: PMC8697544 DOI: 10.1186/s13054-021-03868-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 11/27/2021] [Indexed: 11/24/2022] Open
Affiliation(s)
- J M Smit
- Department of Intensive Care, Erasmus University Medical Center, Rotterdam, Netherlands. .,EEMCS, Pattern Recognition and Bio-informatics Group, Delft University of Technology, Delft, Netherlands.
| | - M E van Genderen
- Department of Intensive Care, Erasmus University Medical Center, Rotterdam, Netherlands
| | - M J T Reinders
- EEMCS, Pattern Recognition and Bio-informatics Group, Delft University of Technology, Delft, Netherlands
| | - D A M P J Gommers
- Department of Intensive Care, Erasmus University Medical Center, Rotterdam, Netherlands
| | - J H Krijthe
- EEMCS, Pattern Recognition and Bio-informatics Group, Delft University of Technology, Delft, Netherlands
| | - J Van Bommel
- Department of Intensive Care, Erasmus University Medical Center, Rotterdam, Netherlands
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