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Golos AM, Guntuku SC, Buttenheim AM. "Do not inject our babies": a social listening analysis of public opinion about authorizing pediatric COVID-19 vaccines. HEALTH AFFAIRS SCHOLAR 2024; 2:qxae082. [PMID: 38979103 PMCID: PMC11229700 DOI: 10.1093/haschl/qxae082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 05/23/2024] [Accepted: 06/17/2024] [Indexed: 07/10/2024]
Abstract
Designing effective childhood vaccination counseling guidelines, public health campaigns, and school-entry mandates requires a nuanced understanding of the information ecology in which parents make vaccination decisions. However, evidence is lacking on how best to "catch the signal" about the public's attitudes, beliefs, and misperceptions. In this study, we characterize public sentiment and discourse about vaccinating children against SARS-CoV-2 with mRNA vaccines to identify prevalent concerns about the vaccine and to understand anti-vaccine rhetorical strategies. We applied computational topic modeling to 149 897 comments submitted to regulations.gov in October 2021 and February 2022 regarding the Food and Drug Administration's Vaccines and Related Biological Products Advisory Committee's emergency use authorization of the COVID-19 vaccines for children. We used a latent Dirichlet allocation topic modeling algorithm to generate topics and then used iterative thematic and discursive analysis to identify relevant domains, themes, and rhetorical strategies. Three domains emerged: (1) specific concerns about the COVID-19 vaccines; (2) foundational beliefs shaping vaccine attitudes; and (3) rhetorical strategies deployed in anti-vaccine arguments. Computational social listening approaches can contribute to misinformation surveillance and evidence-based guidelines for vaccine counseling and public health promotion campaigns.
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Affiliation(s)
- Aleksandra M Golos
- Department of Family and Community Health, School of Nursing, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Sharath-Chandra Guntuku
- Department of Computer and Information Science, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States
- Leonard Davis Institute for Health Economics, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Alison M Buttenheim
- Department of Family and Community Health, School of Nursing, University of Pennsylvania, Philadelphia, PA 19104, United States
- Leonard Davis Institute for Health Economics, University of Pennsylvania, Philadelphia, PA 19104, United States
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2
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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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Abd-Alrazaq A, Nashwan AJ, Shah Z, Abujaber A, Alhuwail D, Schneider J, AlSaad R, Ali H, Alomoush W, Ahmed A, Aziz S. Machine Learning-Based Approach for Identifying Research Gaps: COVID-19 as a Case Study. JMIR Form Res 2024; 8:e49411. [PMID: 38441952 PMCID: PMC10916961 DOI: 10.2196/49411] [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: 05/28/2023] [Revised: 11/14/2023] [Accepted: 02/06/2024] [Indexed: 03/07/2024] Open
Abstract
BACKGROUND Research gaps refer to unanswered questions in the existing body of knowledge, either due to a lack of studies or inconclusive results. Research gaps are essential starting points and motivation in scientific research. Traditional methods for identifying research gaps, such as literature reviews and expert opinions, can be time consuming, labor intensive, and prone to bias. They may also fall short when dealing with rapidly evolving or time-sensitive subjects. Thus, innovative scalable approaches are needed to identify research gaps, systematically assess the literature, and prioritize areas for further study in the topic of interest. OBJECTIVE In this paper, we propose a machine learning-based approach for identifying research gaps through the analysis of scientific literature. We used the COVID-19 pandemic as a case study. METHODS We conducted an analysis to identify research gaps in COVID-19 literature using the COVID-19 Open Research (CORD-19) data set, which comprises 1,121,433 papers related to the COVID-19 pandemic. Our approach is based on the BERTopic topic modeling technique, which leverages transformers and class-based term frequency-inverse document frequency to create dense clusters allowing for easily interpretable topics. Our BERTopic-based approach involves 3 stages: embedding documents, clustering documents (dimension reduction and clustering), and representing topics (generating candidates and maximizing candidate relevance). RESULTS After applying the study selection criteria, we included 33,206 abstracts in the analysis of this study. The final list of research gaps identified 21 different areas, which were grouped into 6 principal topics. These topics were: "virus of COVID-19," "risk factors of COVID-19," "prevention of COVID-19," "treatment of COVID-19," "health care delivery during COVID-19," "and impact of COVID-19." The most prominent topic, observed in over half of the analyzed studies, was "the impact of COVID-19." CONCLUSIONS The proposed machine learning-based approach has the potential to identify research gaps in scientific literature. This study is not intended to replace individual literature research within a selected topic. Instead, it can serve as a guide to formulate precise literature search queries in specific areas associated with research questions that previous publications have earmarked for future exploration. Future research should leverage an up-to-date list of studies that are retrieved from the most common databases in the target area. When feasible, full texts or, at minimum, discussion sections should be analyzed rather than limiting their analysis to abstracts. Furthermore, future studies could evaluate more efficient modeling algorithms, especially those combining topic modeling with statistical uncertainty quantification, such as conformal prediction.
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Affiliation(s)
- Alaa Abd-Alrazaq
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | | | - Zubair Shah
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Ahmad Abujaber
- Nursing Department, Hamad Medical Corporation, Doha, Qatar
| | - Dari Alhuwail
- Information Science Department, College of Life Sciences, Kuwait University, Kuwait, Kuwait
- Health Informatics Unit, Dasman Diabetes Institute, Kuwait, Kuwait
| | - Jens Schneider
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Rawan AlSaad
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Hazrat Ali
- Faculty of Computing and Information Technology, Sohar University, Sohar, Oman
| | - Waleed Alomoush
- School of Information Technology, Skyline University College, Sharjah, United Arab Emirates
| | - Arfan Ahmed
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Sarah Aziz
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
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Chafai N, Bonizzi L, Botti S, Badaoui B. Emerging applications of machine learning in genomic medicine and healthcare. Crit Rev Clin Lab Sci 2024; 61:140-163. [PMID: 37815417 DOI: 10.1080/10408363.2023.2259466] [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: 06/19/2023] [Accepted: 09/12/2023] [Indexed: 10/11/2023]
Abstract
The integration of artificial intelligence technologies has propelled the progress of clinical and genomic medicine in recent years. The significant increase in computing power has facilitated the ability of artificial intelligence models to analyze and extract features from extensive medical data and images, thereby contributing to the advancement of intelligent diagnostic tools. Artificial intelligence (AI) models have been utilized in the field of personalized medicine to integrate clinical data and genomic information of patients. This integration allows for the identification of customized treatment recommendations, ultimately leading to enhanced patient outcomes. Notwithstanding the notable advancements, the application of artificial intelligence (AI) in the field of medicine is impeded by various obstacles such as the limited availability of clinical and genomic data, the diversity of datasets, ethical implications, and the inconclusive interpretation of AI models' results. In this review, a comprehensive evaluation of multiple machine learning algorithms utilized in the fields of clinical and genomic medicine is conducted. Furthermore, we present an overview of the implementation of artificial intelligence (AI) in the fields of clinical medicine, drug discovery, and genomic medicine. Finally, a number of constraints pertaining to the implementation of artificial intelligence within the healthcare industry are examined.
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Affiliation(s)
- Narjice Chafai
- Laboratory of Biodiversity, Ecology, and Genome, Faculty of Sciences, Department of Biology, Mohammed V University in Rabat, Rabat, Morocco
| | - Luigi Bonizzi
- Department of Biomedical, Surgical and Dental Science, University of Milan, Milan, Italy
| | - Sara Botti
- PTP Science Park, Via Einstein - Loc. Cascina Codazza, Lodi, Italy
| | - Bouabid Badaoui
- Laboratory of Biodiversity, Ecology, and Genome, Faculty of Sciences, Department of Biology, Mohammed V University in Rabat, Rabat, Morocco
- African Sustainable Agriculture Research Institute (ASARI), Mohammed VI Polytechnic University (UM6P), Laâyoune, Morocco
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Yilmaz G, Sezer S, Bastug A, Singh V, Gopalan R, Aydos O, Ozturk BY, Gokcinar D, Kamen A, Gramz J, Bodur H, Akbiyik F. Concordance and generalization of an AI algorithm with real-world clinical data in the pre-omicron and omicron era. Heliyon 2024; 10:e25410. [PMID: 38356547 PMCID: PMC10864957 DOI: 10.1016/j.heliyon.2024.e25410] [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: 03/05/2023] [Revised: 01/23/2024] [Accepted: 01/25/2024] [Indexed: 02/16/2024] Open
Abstract
All viruses, including SARS-CoV-2, the virus responsible for COVID-19, continue to evolve, which can lead to new variants. The objective of this study is to assess the agreement between real-world clinical data and an algorithm that utilizes laboratory markers and age to predict the progression of disease severity in COVID-19 patients during the pre-Omicron and Omicron variant periods. The study evaluated the performance of a deep learning (DL) algorithm in predicting disease severity scores for COVID-19 patients using data from the USA, Spain, and Turkey (Ankara City Hospital (ACH) data set). The algorithm was developed and validated using pre-Omicron era data and was tested on both pre-Omicron and Omicron-era data. The predictions were compared to the actual clinical outcomes using a multidisciplinary approach. The concordance index values for all datasets ranged from 0.71 to 0.81. In the ACH cohort, a negative predictive value (NPV) of 0.78 or higher was observed for severe patients in both the pre-Omicron and Omicron eras, which is consistent with the algorithm's performance in the development cohort.
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Affiliation(s)
- Gulsen Yilmaz
- Department of Medical Biochemistry, Ankara Yıldırım Beyazıt University, Ankara, Turkey
- Department of Medical Biochemistry, Ministry of Health, Ankara Bilkent City Hospital, Ankara, Turkey
| | - Sevilay Sezer
- Department of Medical Biochemistry, Ministry of Health, Ankara Bilkent City Hospital, Ankara, Turkey
| | - Aliye Bastug
- Department of Infectious Disease and Clinical Microbiology, Health Science University of Turkey, Gulhane Medical School, Ankara City Hospital, Ankara, Turkey
| | - Vivek Singh
- Siemens Healthineers, Digital Technology and Innovation, Princeton, NJ, USA
| | - Raj Gopalan
- Siemens Healthineers, Diagnostics, Tarrytown, NY, USA
| | - Omer Aydos
- Department of Infectious Disease and Clinical Microbiology, Ankara Bilkent City Hospital, Ankara, Turkey
| | - Busra Yuce Ozturk
- Department of Infectious Disease and Clinical Microbiology, Ankara Bilkent City Hospital, Ankara, Turkey
| | - Derya Gokcinar
- Department of Anesthesiology and Reanimation, Health Science University Turkey, Ankara Bilkent City Hospital, Ankara, Turkey
| | - Ali Kamen
- Siemens Healthineers, Digital Technology and Innovation, Princeton, NJ, USA
| | - Jamie Gramz
- Siemens Healthineers, Diagnostics, Tarrytown, NY, USA
| | - Hurrem Bodur
- Department of Infectious Disease and Clinical Microbiology, Health Science University of Turkey, Gulhane Medical School, Ankara City Hospital, Ankara, Turkey
| | - Filiz Akbiyik
- Ankara Bilkent City Hospital Laboratory, Medical Director, Siemens Healthineers, Ankara, Turkey
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Hien NTK, Tsai FJ, Chang YH, Burton W, Phuc PT, Nguyen PA, Harnod D, Lam CSK, Lu TC, Chen CI, Hsu MH, Lu CY, Huang CW, Yang HC, Hsu JC. Unveiling the future of COVID-19 patient care: groundbreaking prediction models for severe outcomes or mortality in hospitalized cases. Front Med (Lausanne) 2024; 10:1289968. [PMID: 38249981 PMCID: PMC10797111 DOI: 10.3389/fmed.2023.1289968] [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: 09/06/2023] [Accepted: 12/14/2023] [Indexed: 01/23/2024] Open
Abstract
Background Previous studies have identified COVID-19 risk factors, such as age and chronic health conditions, linked to severe outcomes and mortality. However, accurately predicting severe illness in COVID-19 patients remains challenging, lacking precise methods. Objective This study aimed to leverage clinical real-world data and multiple machine-learning algorithms to formulate innovative predictive models for assessing the risk of severe outcomes or mortality in hospitalized patients with COVID-19. Methods Data were obtained from the Taipei Medical University Clinical Research Database (TMUCRD) including electronic health records from three Taiwanese hospitals in Taiwan. This study included patients admitted to the hospitals who received an initial diagnosis of COVID-19 between January 1, 2021, and May 31, 2022. The primary outcome was defined as the composite of severe infection, including ventilator use, intubation, ICU admission, and mortality. Secondary outcomes consisted of individual indicators. The dataset encompassed demographic data, health status, COVID-19 specifics, comorbidities, medications, and laboratory results. Two modes (full mode and simplified mode) are used; the former includes all features, and the latter only includes the 30 most important features selected based on the algorithm used by the best model in full mode. Seven machine learning was employed algorithms the performance of the models was evaluated using metrics such as the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity. Results The study encompassed 22,192 eligible in-patients diagnosed with COVID-19. In the full mode, the model using the light gradient boosting machine algorithm achieved the highest AUROC value (0.939), with an accuracy of 85.5%, a sensitivity of 0.897, and a specificity of 0.853. Age, vaccination status, neutrophil count, sodium levels, and platelet count were significant features. In the simplified mode, the extreme gradient boosting algorithm yielded an AUROC of 0.935, an accuracy of 89.9%, a sensitivity of 0.843, and a specificity of 0.902. Conclusion This study illustrates the feasibility of constructing precise predictive models for severe outcomes or mortality in COVID-19 patients by leveraging significant predictors and advanced machine learning. These findings can aid healthcare practitioners in proactively predicting and monitoring severe outcomes or mortality among hospitalized COVID-19 patients, improving treatment and resource allocation.
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Affiliation(s)
- Nguyen Thi Kim Hien
- Master Program in Global Health and Health Security, College of Public Health, Taipei Medical University, Taipei, Taiwan
| | - Feng-Jen Tsai
- Master Program in Global Health and Health Security, College of Public Health, Taipei Medical University, Taipei, Taiwan
- Ph.D. Program in Global Health and Health Security, College of Public Health, Taipei Medical University, Taipei, Taiwan
| | - Yu-Hui Chang
- PharmD Program, Division of Clinical Pharmacy, College of Pharmacy, Taipei Medical University, Taipei, Taiwan
| | - Whitney Burton
- International Ph.D. Program in Biotech and Healthcare Management, College of Management, Taipei Medical University, Taipei, Taiwan
| | - Phan Thanh Phuc
- International Ph.D. Program in Biotech and Healthcare Management, College of Management, Taipei Medical University, Taipei, Taiwan
| | - Phung-Anh Nguyen
- Clinical Data Center, Office of Data Science, Taipei Medical University, Taipei, Taiwan
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan
- Research Center of Health Care Industry Data Science, College of Management, Taipei Medical University, Taipei, Taiwan
| | - Dorji Harnod
- Department of Emergency, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Emergency and Critical Care Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Carlos Shu-Kei Lam
- Department of Emergency, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Division of Emergency, Department of Emergency and Critical Care Medicine, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
- Graduate Institute of Injury Prevention and Control, College of Public Health, Taipei Medical University, Taipei, Taiwan
| | - Tsung-Chien Lu
- Department of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Chang-I Chen
- Department of Healthcare Administration, School of Management, Taipei Medical University, Taipei, Taiwan
| | - Min-Huei Hsu
- Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan
| | - Christine Y. Lu
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, United States
- School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
- Kolling Institute, Faculty of Medicine and Health, The University of Sydney and the Northern Sydney Local Health District, Sydney, NSW, Australia
| | - Chih-Wei Huang
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan
| | - Hsuan-Chia Yang
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Research Center of Big Data and Meta-analysis, Wanfang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Jason C. Hsu
- International Ph.D. Program in Biotech and Healthcare Management, College of Management, Taipei Medical University, Taipei, Taiwan
- Clinical Data Center, Office of Data Science, Taipei Medical University, Taipei, Taiwan
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan
- Research Center of Health Care Industry Data Science, College of Management, Taipei Medical University, Taipei, Taiwan
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Burnazovic E, Yee A, Levy J, Gore G, Abbasgholizadeh Rahimi S. Application of Artificial intelligence in COVID-19-related geriatric care: A scoping review. Arch Gerontol Geriatr 2024; 116:105129. [PMID: 37542917 DOI: 10.1016/j.archger.2023.105129] [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: 12/20/2022] [Revised: 07/11/2023] [Accepted: 07/13/2023] [Indexed: 08/07/2023]
Abstract
BACKGROUND Older adults have been disproportionately affected by the COVID-19 pandemic. This scoping review aimed to summarize the current evidence of artificial intelligence (AI) use in the screening/monitoring, diagnosis, and/or treatment of COVID-19 among older adults. METHOD The review followed the Joanna Briggs Institute and Arksey and O'Malley frameworks. An information specialist performed a comprehensive search from the date of inception until May 2021, in six bibliographic databases. The selected studies considered all populations, and all AI interventions that had been used in COVID-19-related geriatric care. We focused on patient, healthcare provider, and healthcare system-related outcomes. The studies were restricted to peer-reviewed English publications. Two authors independently screened the titles and abstracts of the identified records, read the selected full texts, and extracted data from the included studies using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. RESULTS Six databases were searched , yielding 3,228 articles, of which 10 were included. The majority of articles used a single AI model to assess the association between patients' comorbidities and COVID-19 outcomes. Articles were mainly conducted in high-income countries, with limited representation of females in study participants, and insufficient reporting of participants' race and ethnicity. DISCUSSION This review highlighted how the COVID-19 pandemic has accelerated the application of AI to protect older populations, with most interventions in the pilot testing stage. Further work is required to measure effectiveness of these technologies in a larger scale, use more representative datasets for training of AI models, and expand AI applications to low-income countries.
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Affiliation(s)
- Emina Burnazovic
- Integrated Biomedical Engineering and Health Sciences, Department of Computing and Software, Faculty of Engineering, McMaster University, Hamilton, ON, Canada
| | - Amanda Yee
- Department of Family Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada
| | - Joshua Levy
- Department of Pharmacology and Therapeutics, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada
| | - Genevieve Gore
- Schulich Library of Physical Sciences, Life Sciences and Engineering, McGill University, Montreal, QC, Canada
| | - Samira Abbasgholizadeh Rahimi
- Department of Family Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada; Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada; Mila-Quebec Artificial Intelligence Institute, Montreal, QC, Canada; Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, QC, Canada.
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Liu L, Song W, Patil N, Sainlaire M, Jasuja R, Dykes PC. Predicting COVID-19 severity: Challenges in reproducibility and deployment of machine learning methods. Int J Med Inform 2023; 179:105210. [PMID: 37769368 DOI: 10.1016/j.ijmedinf.2023.105210] [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: 03/27/2023] [Revised: 08/29/2023] [Accepted: 08/30/2023] [Indexed: 09/30/2023]
Abstract
The increasing use of electronic health records (EHR) based computable phenotypes in clinical research is providing new opportunities for development of data-driven medical applications. Adopted widely in the United States and globally, EHRs facilitate systematic collection of patients' longitudinal information, which serves as one of the important foundations for artificial intelligence applications in medicine. Harmonization of input variables and outcome definitions is critically important for wider clinical applicability of artificial intelligence (AI) methodologies. In this review, we focused on Coronavirus Disease 2019 (COVID-19) severity machine learning prediction models and explored the pipeline for standardizing future disease severity model development using EHR information. We identified 2,967 studies published between 01/01/2020 and 02/15/2022 and selected 135 independent studies that had built machine learning prediction models to predict severity related outcomes of COVID-19 patients based on EHR data for the final review. These 135 studies spanning across 27 counties covered a broad range of severity related prediction outcomes. We observed substantial inconsistency in COVID-19 severity phenotype definitions among models in these studies. Moreover, there was a gap between the outcome of these models and clinician-recognized clinical concepts. Accordingly, we recommend that robust clinical input metrics, with outcome definitions which eliminate ambiguity in interpretation, to reduce algorithmic bias, mitigate model brittleness and improve generalizability of a universal model for COVID-19 severity. This framework can potentially be extended to broader clinical application.
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Affiliation(s)
- Luwei Liu
- Department of Medicine, Brigham & Women's Hospital, Boston, MA, USA
| | - Wenyu Song
- Department of Medicine, Brigham & Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Namrata Patil
- Department of Surgery, Brigham & Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | | | - Ravi Jasuja
- Department of Medicine, Brigham & Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Patricia C Dykes
- Department of Medicine, Brigham & Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
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Tukur M, Saad G, AlShagathrh FM, Househ M, Agus M. Telehealth interventions during COVID-19 pandemic: a scoping review of applications, challenges, privacy and security issues. BMJ Health Care Inform 2023; 30:e100676. [PMID: 37541739 PMCID: PMC10407386 DOI: 10.1136/bmjhci-2022-100676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 07/25/2023] [Indexed: 08/06/2023] Open
Abstract
BACKGROUND The COVID-19, caused by the SARS-CoV-2 virus, proliferated worldwide, leading to a pandemic. Many governmental and non-governmental organisations and research institutes are contributing to the COVID-19 fight to control the pandemic. MOTIVATION Numerous telehealth applications have been proposed and adopted during the pandemic to combat the spread of the disease. To this end, powerful tools such as artificial intelligence (AI)/robotic technologies, tracking, monitoring, consultation apps and other telehealth interventions have been extensively used. However, there are several issues and challenges that are currently facing this technology. OBJECTIVE The purpose of this scoping review is to analyse the primary goal of these techniques; document their contribution to tackling COVID-19; identify and categorise their main challenges and future direction in fighting against the COVID-19 or future pandemic outbreaks. METHODS Four digital libraries (ACM, IEEE, Scopus and Google Scholar) were searched to identify relevant sources. Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) was used as a guideline procedure to develop a comprehensive scoping review. General telehealth features were extracted from the studies reviewed and analysed in the context of the intervention type, technology used, contributions, challenges, issues and limitations. RESULTS A collection of 27 studies were analysed. The reported telehealth interventions were classified into two main categories: AI-based and non-AI-based interventions; their main contributions to tackling COVID-19 are in the aspects of disease detection and diagnosis, pathogenesis and virology, vaccine and drug development, transmission and epidemic predictions, online patient consultation, tracing, and observation; 28 telehealth intervention challenges/issues have been reported and categorised into technical (14), non-technical (10), and privacy, and policy issues (4). The most critical technical challenges are: network issues, system reliability issues, performance, accuracy and compatibility issues. Moreover, the most critical non-technical issues are: the skills required, hardware/software cost, inability to entirely replace physical treatment and people's uncertainty about using the technology. Stringent laws/regulations, ethical issues are some of the policy and privacy issues affecting the development of the telehealth interventions reported in the literature. CONCLUSION This study provides medical and scientific scholars with a comprehensive overview of telehealth technologies' current and future applications in the fight against COVID-19 to motivate researchers to continue to maximise the benefits of these techniques in the fight against pandemics. Lastly, we recommend that the identified challenges, privacy, and security issues and solutions be considered when designing and developing future telehealth applications.
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Affiliation(s)
- Muhammad Tukur
- ICT, Hamad Bin Khalifa University College of Science and Engineering, Doha, Qatar
- Computer Science, Gombe State University, Gombe, Nigeria
| | - Ghassan Saad
- ICT, Hamad Bin Khalifa University College of Science and Engineering, Doha, Qatar
| | - Fahad M AlShagathrh
- ICT, Hamad Bin Khalifa University College of Science and Engineering, Doha, Qatar
| | - Mowafa Househ
- ICT, Hamad Bin Khalifa University College of Science and Engineering, Doha, Qatar
| | - Marco Agus
- ICT, Hamad Bin Khalifa University College of Science and Engineering, Doha, Qatar
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10
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Suárez Fernández C, Armario P, Cepeda JM, López Carmona MD, Miramontes González JP, Said-Criado I. Recommendations for the care of patients with cardiovascular disease in health emergency situations: a call to action. Curr Med Res Opin 2023; 39:827-832. [PMID: 37129909 DOI: 10.1080/03007995.2023.2201100] [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: 01/19/2023] [Revised: 04/05/2023] [Accepted: 04/06/2023] [Indexed: 05/03/2023]
Abstract
COVID-19 has had a negative impact on the health care of patients with cardiovascular disease and patients at high risk of cardiovascular disease. The restrictions affecting access to the health care system have conditioned the care received, resulting in poorer control and a higher risk of events. Taking action to improve the care provided during health emergencies is mandatory. It is important to promote the development of telemedicine and patient empowerment by fostering health literacy and a higher degree of self-care. In addition, primary care and coordination between health care levels should be improved. Moreover, the simplification and optimization of treatment, for example, using the cardiovascular polypill, have led to an improvement in adherence, better control of vascular risk factors, and a reduced risk of events. The present document provides specific recommendations for improving the care provided to patients under a health emergency.
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Affiliation(s)
| | - Pedro Armario
- Internal Medicine Department, Complex Hospitalari Universitari Moisès Broggi, Universitat de Barcelona, Barcelona, Spain
| | | | | | - José Pablo Miramontes González
- Internal Medicine Department, Hospital Universitario Río Hortega. Departamento de Medicina, Facultad de Medicina, Universidad de Valladolid, Valladolid, Spain
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11
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Mulenga C, Kaonga P, Hamoonga R, Mazaba ML, Chabala F, Musonda P. Predicting Mortality in Hospitalized COVID-19 Patients in Zambia: An Application of Machine Learning. Glob Health Epidemiol Genom 2023; 2023:8921220. [PMID: 37260675 PMCID: PMC10228226 DOI: 10.1155/2023/8921220] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 02/23/2023] [Accepted: 04/27/2023] [Indexed: 06/02/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) has wreaked havoc globally, resulting in millions of cases and deaths. The objective of this study was to predict mortality in hospitalized COVID-19 patients in Zambia using machine learning (ML) methods based on factors that have been shown to be predictive of mortality and thereby improve pandemic preparedness. This research employed seven powerful ML models that included decision tree (DT), random forest (RF), support vector machines (SVM), logistic regression (LR), Naïve Bayes (NB), gradient boosting (GB), and XGBoost (XGB). These classifiers were trained on 1,433 hospitalized COVID-19 patients from various health facilities in Zambia. The performances achieved by these models were checked using accuracy, recall, F1-Score, area under the receiver operating characteristic curve (ROC_AUC), area under the precision-recall curve (PRC_AUC), and other metrics. The best-performing model was the XGB which had an accuracy of 92.3%, recall of 94.2%, F1-Score of 92.4%, and ROC_AUC of 97.5%. The pairwise Mann-Whitney U-test analysis showed that the second-best model (GB) and the third-best model (RF) did not perform significantly worse than the best model (XGB) and had the following: GB had an accuracy of 91.7%, recall of 94.2%, F1-Score of 91.9%, and ROC_AUC of 97.1%. RF had an accuracy of 90.8%, recall of 93.6%, F1-Score of 91.0%, and ROC_AUC of 96.8%. Other models showed similar results for the same metrics checked. The study successfully derived and validated the selected ML models and predicted mortality effectively with reasonably high performance in the stated metrics. The feature importance analysis found that knowledge of underlying health conditions about patients' hospital length of stay (LOS), white blood cell count, age, and other factors can help healthcare providers offer lifesaving services on time, improve pandemic preparedness, and decongest health facilities in Zambia and other countries with similar settings.
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Affiliation(s)
- Clyde Mulenga
- Department of Epidemiology and Biostatistics, University of Zambia, Lusaka, Zambia
- Institute of Basic and Biomedical Sciences, Levy Mwanawasa Medical University, Lusaka, Zambia
| | - Patrick Kaonga
- Department of Epidemiology and Biostatistics, University of Zambia, Lusaka, Zambia
| | - Raymond Hamoonga
- The Health Press, Zambia National Public Health Institute, Lusaka, Zambia
| | - Mazyanga Lucy Mazaba
- Communication Information and Research, Zambia National Public Health Institute, Lusaka, Zambia
| | - Freeman Chabala
- Institute of Basic and Biomedical Sciences, Levy Mwanawasa Medical University, Lusaka, Zambia
| | - Patrick Musonda
- Department of Epidemiology and Biostatistics, University of Zambia, Lusaka, Zambia
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12
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Jiao Z, Ji H, Yan J, Qi X. Application of big data and artificial intelligence in epidemic surveillance and containment. INTELLIGENT MEDICINE 2023; 3:36-43. [PMID: 36373090 PMCID: PMC9636598 DOI: 10.1016/j.imed.2022.10.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 10/12/2022] [Accepted: 10/13/2022] [Indexed: 11/07/2022]
Abstract
Faced with the current time-sensitive COVID-19 pandemic, the overburdened healthcare systems have resulted in a strong demand to develop newer methods to control the spread of the pandemic. Big data and artificial intelligence (AI) have been leveraged amid the COVID-19 pandemic; however, little is known about their use for supporting public health efforts. In epidemic surveillance and containment, efforts are needed to treat critical patients, track and manage the health status of residents, isolate suspected cases, and develop vaccines and antiviral drugs. The applications of emerging practices of artificial intelligence and big data have become powerful "weapons" to fight against the pandemic and provide strong support in pandemic prevention and control, such as early warning, analysis and judgment, interruption and intervention of epidemic, to achieve goals of early detection, early report, early diagnosis, early isolation and early treatment. These are the decisive factors to control the spread of the epidemic and reduce the mortality. This paper systematically summarized the application of big data and AI in epidemic, and describes practical cases and challenges with emphasis on epidemic prevention and control. The included studies showed that big data and AI have the potential strength to fight against COVID-19. However, many of the proposed methods are not yet widely accepted. Thus, the most rewarding research would be on methods that promise value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for practice.
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Affiliation(s)
- Zengtao Jiao
- AI lab, Yidu Cloud (Beijing) Technology Co., Ltd., Beijing 100083, China
| | - Hanran Ji
- Center for Global Public Health, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - Jun Yan
- AI lab, Yidu Cloud (Beijing) Technology Co., Ltd., Beijing 100083, China
| | - Xiaopeng Qi
- Center for Global Public Health, Chinese Center for Disease Control and Prevention, Beijing 102206, China
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13
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Burzyńska J, Bartosiewicz A, Januszewicz P. Dr. Google: Physicians-The Web-Patients Triangle: Digital Skills and Attitudes towards e-Health Solutions among Physicians in South Eastern Poland-A Cross-Sectional Study in a Pre-COVID-19 Era. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:978. [PMID: 36673740 PMCID: PMC9858975 DOI: 10.3390/ijerph20020978] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 12/20/2022] [Accepted: 12/30/2022] [Indexed: 05/27/2023]
Abstract
The investment in digital e-health services is a priority direction in the development of global healthcare systems. While people are increasingly using the Web for health information, it is not entirely clear what physicians' attitudes are towards digital transformation, as well as the acceptance of new technologies in healthcare. The aim of this cross-sectional survey study was to investigate physicians' self-digital skills and their opinions on obtaining online health knowledge by patients, as well as the recognition of physicians' attitudes towards e-health solutions. Principal component analysis (PCA) was performed to emerge the variables from self-designed questionnaire and cross-sectional analysis, comparing descriptive statistics and correlations for dependent variables using the one-way ANOVA (F-test). A total of 307 physicians participated in the study, reported as using the internet mainly several times a day (66.8%). Most participants (70.4%) were familiar with new technologies and rated their e-health literacy high, although 84.0% reported the need for additional training in this field and reported a need to introduce a larger number of subjects shaping digital skills (75.9%). 53.4% of physicians perceived Internet-sourced information as sometimes reliable and, in general, assessed the effects of its use by their patients negatively (41.7%). Digital skills increased significantly with frequency of internet use (F = 13.167; p = 0.0001) and decreased with physicians' age and the need for training. Those who claimed that patients often experienced health benefits from online health showed higher digital skills (-1.06). Physicians most often recommended their patients to obtain laboratory test results online (32.2%) and to arrange medical appointments via the Internet (27.0%). Along with the deterioration of physicians' digital skills, the recommendation of e-health solutions decreased (r = 0.413) and lowered the assessment of e-health solutions for the patient (r = 0.449). Physicians perceive digitization as a sign of the times and frequently use its tools in daily practice. The evaluation of Dr. Google's phenomenon and online health is directly related to their own e-health literacy skills, but there is still a need for practical training to deal with the digital revolution.
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Affiliation(s)
- Joanna Burzyńska
- Institute of Health Sciences, Medical College of Rzeszow University, 35-959 Rzeszów, Poland
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14
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Bartlett LK, Pirrone A, Javed N, Gobet F. Computational Scientific Discovery in Psychology. PERSPECTIVES ON PSYCHOLOGICAL SCIENCE 2023; 18:178-189. [PMID: 35943820 PMCID: PMC9902966 DOI: 10.1177/17456916221091833] [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] [Indexed: 01/31/2023]
Abstract
Scientific discovery is a driving force for progress involving creative problem-solving processes to further our understanding of the world. The process of scientific discovery has historically been intensive and time-consuming; however, advances in computational power and algorithms have provided an efficient route to make new discoveries. Complex tools using artificial intelligence (AI) can efficiently analyze data as well as generate new hypotheses and theories. Along with AI becoming increasingly prevalent in our daily lives and the services we access, its application to different scientific domains is becoming more widespread. For example, AI has been used for the early detection of medical conditions, identifying treatments and vaccines (e.g., against COVID-19), and predicting protein structure. The application of AI in psychological science has started to become popular. AI can assist in new discoveries both as a tool that allows more freedom to scientists to generate new theories and by making creative discoveries autonomously. Conversely, psychological concepts such as heuristics have refined and improved artificial systems. With such powerful systems, however, there are key ethical and practical issues to consider. This article addresses the current and future directions of computational scientific discovery generally and its applications in psychological science more specifically.
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Affiliation(s)
- Laura K. Bartlett
- Laura K. Bartlett, Centre for Philosophy of Natural and Social Science, London School of Economics and Political Science
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15
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Pre-hospital prediction of adverse outcomes in patients with suspected COVID-19: Development, application and comparison of machine learning and deep learning methods. Comput Biol Med 2022; 151:106024. [PMID: 36327887 PMCID: PMC9420071 DOI: 10.1016/j.compbiomed.2022.106024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 08/02/2022] [Accepted: 08/20/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND COVID-19 infected millions of people and increased mortality worldwide. Patients with suspected COVID-19 utilised emergency medical services (EMS) and attended emergency departments, resulting in increased pressures and waiting times. Rapid and accurate decision-making is required to identify patients at high-risk of clinical deterioration following COVID-19 infection, whilst also avoiding unnecessary hospital admissions. Our study aimed to develop artificial intelligence models to predict adverse outcomes in suspected COVID-19 patients attended by EMS clinicians. METHOD Linked ambulance service data were obtained for 7,549 adult patients with suspected COVID-19 infection attended by EMS clinicians in the Yorkshire and Humber region (England) from 18-03-2020 to 29-06-2020. We used support vector machines (SVM), extreme gradient boosting, artificial neural network (ANN) models, ensemble learning methods and logistic regression to predict the primary outcome (death or need for organ support within 30 days). Models were compared with two baselines: the decision made by EMS clinicians to convey patients to hospital, and the PRIEST clinical severity score. RESULTS Of the 7,549 patients attended by EMS clinicians, 1,330 (17.6%) experienced the primary outcome. Machine Learning methods showed slight improvements in sensitivity over baseline results. Further improvements were obtained using stacking ensemble methods, the best geometric mean (GM) results were obtained using SVM and ANN as base learners when maximising sensitivity and specificity. CONCLUSIONS These methods could potentially reduce the numbers of patients conveyed to hospital without a concomitant increase in adverse outcomes. Further work is required to test the models externally and develop an automated system for use in clinical settings.
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Al-Garadi MA, Yang YC, Sarker A. The Role of Natural Language Processing during the COVID-19 Pandemic: Health Applications, Opportunities, and Challenges. Healthcare (Basel) 2022; 10:2270. [PMID: 36421593 PMCID: PMC9690240 DOI: 10.3390/healthcare10112270] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/03/2022] [Accepted: 11/06/2022] [Indexed: 07/30/2023] Open
Abstract
The COVID-19 pandemic is the most devastating public health crisis in at least a century and has affected the lives of billions of people worldwide in unprecedented ways. Compared to pandemics of this scale in the past, societies are now equipped with advanced technologies that can mitigate the impacts of pandemics if utilized appropriately. However, opportunities are currently not fully utilized, particularly at the intersection of data science and health. Health-related big data and technological advances have the potential to significantly aid the fight against such pandemics, including the current pandemic's ongoing and long-term impacts. Specifically, the field of natural language processing (NLP) has enormous potential at a time when vast amounts of text-based data are continuously generated from a multitude of sources, such as health/hospital systems, published medical literature, and social media. Effectively mitigating the impacts of the pandemic requires tackling challenges associated with the application and deployment of NLP systems. In this paper, we review the applications of NLP to address diverse aspects of the COVID-19 pandemic. We outline key NLP-related advances on a chosen set of topics reported in the literature and discuss the opportunities and challenges associated with applying NLP during the current pandemic and future ones. These opportunities and challenges can guide future research aimed at improving the current health and social response systems and pandemic preparedness.
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Affiliation(s)
- Mohammed Ali Al-Garadi
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37240, USA
| | - Yuan-Chi Yang
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Abeed Sarker
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA
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17
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Horvath A, Lind T, Frece N, Wurzer H, Stadlbauer V. Validation of a simple risk stratification tool for COVID-19 mortality. Front Med (Lausanne) 2022; 9:1016180. [PMID: 36304183 PMCID: PMC9592707 DOI: 10.3389/fmed.2022.1016180] [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: 08/10/2022] [Accepted: 09/22/2022] [Indexed: 11/30/2022] Open
Abstract
Risk prediction is an essential part of clinical care, in order to allocate resources and provide care appropriately. During the COVID-19 pandemic risk prediction became a matter of political and public debate as a major clinical need to guide medical and organizational decisions. We previously presented a simplified risk stratification score based on a nomogram developed in Wuhan, China in the early phase of the pandemic. Here we aimed to validate this simplified risk stratification score in a larger patient cohort from one city in Austria. Age, oxygen saturation, C-reactive protein levels and creatinine levels were used to estimate the in-hospital mortality risk for COVID-19 patients in a point based score: 1 point per age decade, 4 points for oxygen saturation <92%, 8 points for CRP > 10 mg/l and 4 points for creatinine > 84 μmol/l. Between June 2020 and March 2021, during the “second wave” of the pandemic, 1,472 patients with SARS-CoV-2 infection were admitted to two hospitals in Graz, Austria. In 961 patients the necessary dataset to calculate the simplified risk stratification score was available. In this cohort, as in the cohort that was used to develop the score, a score above 22 was associated with a significantly higher mortality (p < 0.001). Cox regression confirmed that an increase of one point in the risk stratification score increases the 28-day-mortality risk approximately 1.2-fold. Patients who were categorized as high risk (≥22 points) showed a 3–4 fold increased mortality risk. Our simplified risk stratification score performed well in a separate, larger validation cohort. We therefore propose that our risk stratification score, that contains only two routine laboratory parameter, age and oxygen saturation as variables can be a useful and easy to implement tool for COVID-19 risk stratification and beyond. The clinical usefulness of a risk prediction/stratification tool needs to be assessed prospectively (https://www.cbmed.at/covid-19-risk-calculator/).
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Affiliation(s)
- Angela Horvath
- Medical University of Graz, Graz, Austria,Center for Biomarker Research in Medicine (CBmed), Graz, Austria
| | | | | | - Herbert Wurzer
- Department of Internal Medicine, State Hospital Graz II, Graz, Austria
| | - Vanessa Stadlbauer
- Medical University of Graz, Graz, Austria,Center for Biomarker Research in Medicine (CBmed), Graz, Austria,*Correspondence: Vanessa Stadlbauer
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18
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Dron L, Kalatharan V, Gupta A, Haggstrom J, Zariffa N, Morris AD, Arora P, Park J. Data capture and sharing in the COVID-19 pandemic: a cause for concern. Lancet Digit Health 2022; 4:e748-e756. [PMID: 36150783 PMCID: PMC9489064 DOI: 10.1016/s2589-7500(22)00147-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 07/08/2022] [Accepted: 07/13/2022] [Indexed: 12/25/2022]
Abstract
Routine health care and research have been profoundly influenced by digital-health technologies. These technologies range from primary data collection in electronic health records (EHRs) and administrative claims to web-based artificial-intelligence-driven analyses. There has been increased use of such health technologies during the COVID-19 pandemic, driven in part by the availability of these data. In some cases, this has resulted in profound and potentially long-lasting positive effects on medical research and routine health-care delivery. In other cases, high profile shortcomings have been evident, potentially attenuating the effect of-or representing a decreased appetite for-digital-health transformation. In this Series paper, we provide an overview of how facets of health technologies in routinely collected medical data (including EHRs and digital data sharing) have been used for COVID-19 research and tracking, and how these technologies might influence future pandemics and health-care research. We explore the strengths and weaknesses of digital-health research during the COVID-19 pandemic and discuss how learnings from COVID-19 might translate into new approaches in a post-pandemic era.
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Affiliation(s)
- Louis Dron
- Real World & Advanced Analytics, Cytel Health, Vancouver, BC, Canada,Correspondence to: Mr Louis Dron, Real World & Advanced Analytics, Cytel Health, Vancouver, BC V5Z 4J7, Canada
| | - Vinusha Kalatharan
- Department of Epidemiology and Biostatistics, Western University, London, ON, Canada
| | - Alind Gupta
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Jonas Haggstrom
- Real World & Advanced Analytics, Cytel Health, Vancouver, BC, Canada,The International COVID-19 Data Alliance (ICODA), Health Data Research UK, London, UK
| | - Nevine Zariffa
- The International COVID-19 Data Alliance (ICODA), Health Data Research UK, London, UK,NMD Group, LLC, Bala Cynwyd, PA, USA
| | - Andrew D Morris
- The International COVID-19 Data Alliance (ICODA), Health Data Research UK, London, UK
| | - Paul Arora
- Real World & Advanced Analytics, Cytel Health, Vancouver, BC, Canada,Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Jay Park
- Department of Experimental Medicine, Department of Medicine, University of British Columbia, Vancouver, BC, Canada,Department of Health Research Methods, Evidence and Impact, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
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19
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Mattiuzzi C, Lippi G. The Global Impact of COVID-19 on Threat Appraisals. Healthcare (Basel) 2022; 10:healthcare10091718. [PMID: 36141329 PMCID: PMC9498705 DOI: 10.3390/healthcare10091718] [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: 08/17/2022] [Revised: 08/30/2022] [Accepted: 09/06/2022] [Indexed: 12/03/2022] Open
Abstract
We planned an infodemiological analysis to estimate the global impact of coronavirus disease 2019 (COVID-19) on threat appraisals. We accessed Google Trends using the search terms “Anxiety”, “Distress”, “Fear”, “Rumination”, “Stress” and “Worry” within the “topic” domain, setting the geographical location to “worldwide”, between July 2017 and July 2022. The weekly Google Trends score for the six search terms, thus, mirroring Web popularity and probable prevalence, was compared between the two search periods, “pre-COVID” (between July 2017 and February 2020) and COVID (between March 2020 and July 2022), thus, reflecting the volume of searches before and during the ongoing COVID-19 pandemic. The median weekly Google Trends score of all these search terms significantly increased during the COVID-19 pandemic, i.e., anxiety by 22%, distress by 13%, fear by 9%, rumination by 18%, stress by 13% and worry by 20%. With variable strength, the weekly Google Trends scores of each search term were found to be significantly associated (all p < 0.001). We can, hence, conclude that the enhanced burden of threat appraisals observed after SARS-CoV-2 spread leads the way to establish preventive, diagnostic and therapeutic measures in order to limit the unfavorable mental health consequences caused by the ongoing COVID-19 pandemic.
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Affiliation(s)
- Camilla Mattiuzzi
- Service of Clinical Governance, Provincial Agency for Social and Sanitary Services (APSS), 38123 Trento, Italy
| | - Giuseppe Lippi
- Section of Clinical Biochemistry and School of Medicine, University of Verona, 37126 Verona, Italy
- Correspondence: ; Tel.: +39-045-8124308
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He J, Yang T. In the era of long COVID, can we seek new techniques for better rehabilitation? Chronic Dis Transl Med 2022; 8:149-153. [PMID: 36161203 PMCID: PMC9481878 DOI: 10.1002/cdt3.42] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 07/24/2022] [Indexed: 11/08/2022] Open
Affiliation(s)
- Jiaze He
- Graduate School of Capital Medical University Beijing China
- Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China‐Japan Friendship Hospital, National Center for Respiratory Medicine, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, National Clinical Research Center for Respiratory Diseases WHO Collaborating Centre for Tobacco Cessation and Respiratory Diseases Prevention Beijing China
| | - Ting Yang
- Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China‐Japan Friendship Hospital, National Center for Respiratory Medicine, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, National Clinical Research Center for Respiratory Diseases WHO Collaborating Centre for Tobacco Cessation and Respiratory Diseases Prevention Beijing China
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21
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Martinez-Millana A, Saez-Saez A, Tornero-Costa R, Azzopardi-Muscat N, Traver V, Novillo-Ortiz D. Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews. Int J Med Inform 2022; 166:104855. [PMID: 35998421 PMCID: PMC9551134 DOI: 10.1016/j.ijmedinf.2022.104855] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 08/01/2022] [Accepted: 08/11/2022] [Indexed: 12/04/2022]
Abstract
An overview of systematic reviews on the application of AI including 129 studies. AI use is prominent in Universal Health Coverage, featuring image analysis in neoplasms. Half of the reviews did not evaluate validation procedures nor reporting guidelines. Risk of bias was only included un a third of the reviews. There is not sufficient evidence to transfer AI to actual healthcare delivery.
Background Artificial intelligence is fueling a new revolution in medicine and in the healthcare sector. Despite the growing evidence on the benefits of artificial intelligence there are several aspects that limit the measure of its impact in people’s health. It is necessary to assess the current status on the application of AI towards the improvement of people’s health in the domains defined by WHO’s Thirteenth General Programme of Work (GPW13) and the European Programme of Work (EPW), to inform about trends, gaps, opportunities, and challenges. Objective To perform a systematic overview of systematic reviews on the application of artificial intelligence in the people’s health domains as defined in the GPW13 and provide a comprehensive and updated map on the application specialties of artificial intelligence in terms of methodologies, algorithms, data sources, outcomes, predictors, performance, and methodological quality. Methods A systematic search in MEDLINE, EMBASE, Cochrane and IEEEXplore was conducted between January 2015 and June 2021 to collect systematic reviews using a combination of keywords related to the domains of universal health coverage, health emergencies protection, and better health and wellbeing as defined by the WHO’s PGW13 and EPW. Eligibility criteria was based on methodological quality and the inclusion of practical implementation of artificial intelligence. Records were classified and labeled using ICD-11 categories into the domains of the GPW13. Descriptors related to the area of implementation, type of modeling, data entities, outcomes and implementation on care delivery were extracted using a structured form and methodological aspects of the included reviews studies was assessed using the AMSTAR checklist. Results The search strategy resulted in the screening of 815 systematic reviews from which 203 were assessed for eligibility and 129 were included in the review. The most predominant domain for artificial intelligence applications was Universal Health Coverage (N = 98) followed by Health Emergencies (N = 16) and Better Health and Wellbeing (N = 15). Neoplasms area on Universal Health Coverage was the disease area featuring most of the applications (21.7 %, N = 28). The reviews featured analytics primarily over both public and private data sources (67.44 %, N = 87). The most used type of data was medical imaging (31.8 %, N = 41) and predictors based on regions of interest and clinical data. The most prominent subdomain of Artificial Intelligence was Machine Learning (43.4 %, N = 56), in which Support Vector Machine method was predominant (20.9 %, N = 27). Regarding the purpose, the application of Artificial Intelligence I is focused on the prediction of the diseases (36.4 %, N = 47). With respect to the validation, more than a half of the reviews (54.3 %, N = 70) did not report a validation procedure and, whenever available, the main performance indicator was the accuracy (28.7 %, N = 37). According to the methodological quality assessment, a third of the reviews (34.9 %, N = 45) implemented methods for analysis the risk of bias and the overall AMSTAR score below was 5 (4.01 ± 1.93) on all the included systematic reviews. Conclusion Artificial intelligence is being used for disease modelling, diagnose, classification and prediction in the three domains of GPW13. However, the evidence is often limited to laboratory and the level of adoption is largely unbalanced between ICD-11 categoriesand diseases. Data availability is a determinant factor on the developmental stage of artificial intelligence applications. Most of the reviewed studies show a poor methodological quality and are at high risk of bias, which limits the reproducibility of the results and the reliability of translating these applications to real clinical scenarios. The analyzed papers show results only in laboratory and testing scenarios and not in clinical trials nor case studies, limiting the supporting evidence to transfer artificial intelligence to actual care delivery.
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Affiliation(s)
- Antonio Martinez-Millana
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Aida Saez-Saez
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Roberto Tornero-Costa
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Natasha Azzopardi-Muscat
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
| | - Vicente Traver
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - David Novillo-Ortiz
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark.
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22
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Ramón A, Zaragozá M, Torres AM, Cascón J, Blasco P, Milara J, Mateo J. Application of Machine Learning in Hospitalized Patients with Severe COVID-19 Treated with Tocilizumab. J Clin Med 2022; 11:jcm11164729. [PMID: 36012968 PMCID: PMC9410189 DOI: 10.3390/jcm11164729] [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: 07/04/2022] [Revised: 08/05/2022] [Accepted: 08/07/2022] [Indexed: 11/16/2022] Open
Abstract
Among the IL-6 inhibitors, tocilizumab is the most widely used therapeutic option in patients with SARS-CoV-2-associated severe respiratory failure (SRF). The aim of our study was to provide evidence on predictors of poor outcome in patients with COVID-19 treated with tocilizumab, using machine learning (ML) techniques. We conducted a retrospective study, analyzing the clinical, laboratory and sociodemographic data of patients admitted for severe COVID-19 with SRF, treated with tocilizumab. The extreme gradient boost (XGB) method had the highest balanced accuracy (93.16%). The factors associated with a worse outcome of tocilizumab use in terms of mortality were: baseline situation at the start of tocilizumab treatment requiring invasive mechanical ventilation (IMV), elevated ferritin, lactate dehydrogenase (LDH) and glutamate-pyruvate transaminase (GPT), lymphopenia, and low PaFi [ratio between arterial oxygen pressure and inspired oxygen fraction (PaO2/FiO2)] values. The factors associated with a worse outcome of tocilizumab use in terms of hospital stay were: baseline situation at the start of tocilizumab treatment requiring IMV or supplemental oxygen, elevated levels of ferritin, glutamate-oxaloacetate transaminase (GOT), GPT, C-reactive protein (CRP), LDH, lymphopenia, and low PaFi values. In our study focused on patients with severe COVID-19 treated with tocilizumab, the factors that were weighted most strongly in predicting worse clinical outcome were baseline status at the start of tocilizumab treatment requiring IMV and hyperferritinemia.
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Affiliation(s)
- Antonio Ramón
- Department of Pharmacy, General University Hospital, 46014 Valencia, Spain
| | - Marta Zaragozá
- Department of Pharmacy, General University Hospital, 46014 Valencia, Spain
| | - Ana María Torres
- Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
| | - Joaquín Cascón
- Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
| | - Pilar Blasco
- Department of Pharmacy, General University Hospital, 46014 Valencia, Spain
| | - Javier Milara
- Department of Pharmacy, General University Hospital, 46014 Valencia, Spain
- Department of Pharmacology, Faculty of Medicine, University of Valencia, 46010 Valencia, Spain
- Centre for Biomedical Research Network on Respiratory Diseases (CIBERES), Health Institute Carlos III, 28029 Madrid, Spain
- Correspondence:
| | - Jorge Mateo
- Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
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23
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The performance of artificial intelligence-driven technologies in diagnosing mental disorders: an umbrella review. NPJ Digit Med 2022; 5:87. [PMID: 35798934 PMCID: PMC9262920 DOI: 10.1038/s41746-022-00631-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 06/08/2022] [Indexed: 11/08/2022] Open
Abstract
Artificial intelligence (AI) has been successfully exploited in diagnosing many mental disorders. Numerous systematic reviews summarize the evidence on the accuracy of AI models in diagnosing different mental disorders. This umbrella review aims to synthesize results of previous systematic reviews on the performance of AI models in diagnosing mental disorders. To identify relevant systematic reviews, we searched 11 electronic databases, checked the reference list of the included reviews, and checked the reviews that cited the included reviews. Two reviewers independently selected the relevant reviews, extracted the data from them, and appraised their quality. We synthesized the extracted data using the narrative approach. We included 15 systematic reviews of 852 citations identified. The included reviews assessed the performance of AI models in diagnosing Alzheimer's disease (n = 7), mild cognitive impairment (n = 6), schizophrenia (n = 3), bipolar disease (n = 2), autism spectrum disorder (n = 1), obsessive-compulsive disorder (n = 1), post-traumatic stress disorder (n = 1), and psychotic disorders (n = 1). The performance of the AI models in diagnosing these mental disorders ranged between 21% and 100%. AI technologies offer great promise in diagnosing mental health disorders. The reported performance metrics paint a vivid picture of a bright future for AI in this field. Healthcare professionals in the field should cautiously and consciously begin to explore the opportunities of AI-based tools for their daily routine. It would also be encouraging to see a greater number of meta-analyses and further systematic reviews on performance of AI models in diagnosing other common mental disorders such as depression and anxiety.
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24
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Ali H, Shah Z. Combating COVID-19 Using Generative Adversarial Networks and Artificial Intelligence for Medical Images: Scoping Review. JMIR Med Inform 2022; 10:e37365. [PMID: 35709336 PMCID: PMC9246088 DOI: 10.2196/37365] [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: 02/17/2022] [Revised: 03/06/2022] [Accepted: 03/11/2022] [Indexed: 12/02/2022] Open
Abstract
Background Research on the diagnosis of COVID-19 using lung images is limited by the scarcity of imaging data. Generative adversarial networks (GANs) are popular for synthesis and data augmentation. GANs have been explored for data augmentation to enhance the performance of artificial intelligence (AI) methods for the diagnosis of COVID-19 within lung computed tomography (CT) and X-ray images. However, the role of GANs in overcoming data scarcity for COVID-19 is not well understood. Objective This review presents a comprehensive study on the role of GANs in addressing the challenges related to COVID-19 data scarcity and diagnosis. It is the first review that summarizes different GAN methods and lung imaging data sets for COVID-19. It attempts to answer the questions related to applications of GANs, popular GAN architectures, frequently used image modalities, and the availability of source code. Methods A search was conducted on 5 databases, namely PubMed, IEEEXplore, Association for Computing Machinery (ACM) Digital Library, Scopus, and Google Scholar. The search was conducted from October 11-13, 2021. The search was conducted using intervention keywords, such as “generative adversarial networks” and “GANs,” and application keywords, such as “COVID-19” and “coronavirus.” The review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines for systematic and scoping reviews. Only those studies were included that reported GAN-based methods for analyzing chest X-ray images, chest CT images, and chest ultrasound images. Any studies that used deep learning methods but did not use GANs were excluded. No restrictions were imposed on the country of publication, study design, or outcomes. Only those studies that were in English and were published from 2020 to 2022 were included. No studies before 2020 were included. Results This review included 57 full-text studies that reported the use of GANs for different applications in COVID-19 lung imaging data. Most of the studies (n=42, 74%) used GANs for data augmentation to enhance the performance of AI techniques for COVID-19 diagnosis. Other popular applications of GANs were segmentation of lungs and superresolution of lung images. The cycleGAN and the conditional GAN were the most commonly used architectures, used in 9 studies each. In addition, 29 (51%) studies used chest X-ray images, while 21 (37%) studies used CT images for the training of GANs. For the majority of the studies (n=47, 82%), the experiments were conducted and results were reported using publicly available data. A secondary evaluation of the results by radiologists/clinicians was reported by only 2 (4%) studies. Conclusions Studies have shown that GANs have great potential to address the data scarcity challenge for lung images in COVID-19. Data synthesized with GANs have been helpful to improve the training of the convolutional neural network (CNN) models trained for the diagnosis of COVID-19. In addition, GANs have also contributed to enhancing the CNNs’ performance through the superresolution of the images and segmentation. This review also identified key limitations of the potential transformation of GAN-based methods in clinical applications.
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Affiliation(s)
- Hazrat Ali
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Zubair Shah
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
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25
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El-Sherif DM, Abouzid M. Analysis of mHealth research: mapping the relationship between mobile apps technology and healthcare during COVID-19 outbreak. Global Health 2022; 18:67. [PMID: 35765078 PMCID: PMC9238163 DOI: 10.1186/s12992-022-00856-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 06/09/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Mobile health applications (mHealth apps) offer enormous promise for illness monitoring and treatment to improve the provided medical care and promote health and wellbeing. OBJECTIVE We applied bibliometric quantitative analysis and network visualization to highlight research trends and areas of particular interest. We expect by summarizing the trends in mHealth app research, our work will serve as a roadmap for future investigations. METHODS Relevant English publications were extracted from the Scopus database. VOSviewer (version 1.6.17) was used to build coauthorship networks of authors, countries, and the co-occurrence networks of author keywords. RESULTS We analyzed 550 published articles on mHealth apps from 2020 to February 1, 2021. The yearly publications increased from 130 to 390 in 2021. JMIR mHealth and uHealth (33/550, 6.0%), J. Med. Internet Res. (27/550, 4.9%), JMIR Res. Protoc. (22/550, 4.0%) were the widest journals for these publications. The United States has the largest number of publications (143/550, 26.0%), and England ranks second (96/550, 17.5%). The top three productive authors were: Giansanti D., Samuel G., Lucivero F., and Zhang L. Frequent authors' keywords have formed major 4 clusters representing the hot topics in the field: (1) artificial intelligence and telehealthcare; (2) digital contact tracing apps, privacy and security concerns; (3) mHealth apps and mental health; (4) mHealth apps in public health and health promotion. CONCLUSIONS mHealth apps undergo current developments, and they remain hot topics in COVID-19. These findings might be useful in determining future perspectives to improve infectious disease control and present innovative solutions for healthcare.
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Affiliation(s)
- Dina M El-Sherif
- National Institute of Oceanography and Fisheries (NIOF), Cairo, Egypt.
| | - Mohamed Abouzid
- Department of Physical Pharmacy and Pharmacokinetics, Poznan University of Medical Sciences, 60-781, Poznan, Poland.,Doctoral School, Poznan University of Medical Sciences, 60-781, Poznan, Poland
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26
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Comito C, Pizzuti C. Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review. Artif Intell Med 2022; 128:102286. [PMID: 35534142 PMCID: PMC8958821 DOI: 10.1016/j.artmed.2022.102286] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 03/16/2022] [Accepted: 03/21/2022] [Indexed: 02/05/2023]
Abstract
The outbreak of novel corona virus 2019 (COVID-19) has been treated as a public health crisis of global concern by the World Health Organization (WHO). COVID-19 pandemic hugely affected countries worldwide raising the need to exploit novel, alternative and emerging technologies to respond to the emergency created by the weak health-care systems. In this context, Artificial Intelligence (AI) techniques can give a valid support to public health authorities, complementing traditional approaches with advanced tools. This study provides a comprehensive review of methods, algorithms, applications, and emerging AI technologies that can be utilized for forecasting and diagnosing COVID-19. The main objectives of this review are summarized as follows. (i) Understanding the importance of AI approaches such as machine learning and deep learning for COVID-19 pandemic; (ii) discussing the efficiency and impact of these methods for COVID-19 forecasting and diagnosing; (iii) providing an extensive background description of AI techniques to help non-expert to better catch the underlying concepts; (iv) for each work surveyed, give a detailed analysis of the rationale behind the approach, highlighting the method used, the type and size of data analyzed, the validation method, the target application and the results achieved; (v) focusing on some future challenges in COVID-19 forecasting and diagnosing.
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27
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Abd-alrazaq A, Abuelezz I, Hassan A, AlSammarraie A, Alhuwail D, Irshaidat S, Abu Serhan H, Ahmed A, Alabed Alrazak S, Househ M. Artificial Intelligence-Driven Serious Games in Healthcare: A Scoping Review (Preprint). JMIR Serious Games 2022; 10:e39840. [DOI: 10.2196/39840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 09/11/2022] [Accepted: 10/11/2022] [Indexed: 11/07/2022] Open
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28
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Abd-alrazaq A, Abuelezz I, Hassan A, Alsammarraie A, Alhuwail D, Irshaidat S, Abu Serhan H, Ahmed A, Alabed Alrazak S, Househ M. Artificial Intelligence-Driven Serious Games in Healthcare: A Scoping Review (Preprint).. [DOI: 10.2196/preprints.39840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
BACKGROUND
Artificial Intelligence (AI)-driven serious games have been used in healthcare to offer a customizable and immersive experience. Summarizing the features of the current AI-driven serious games is very important to explore how they have been developed and used and their current state in order to plan on how to leverage them in the current and future healthcare needs.
OBJECTIVE
The current study aimed to explore the features of AI-driven serious games in healthcare as reported by previous research.
METHODS
We carried out a scoping review to achieve the above-mentioned objective. The most popular databases in information technology and health fields (e.g., MEDLINE and IEEE Xplore) were searched using keywords related to serious games and AI. These terms were selected based on the target intervention (i.e., AI) and the target disease (i.e., COVID-19). Two reviewers independently performed the study selection process. Three reviewers independently used Microsoft Excel to extract data from the included studies. A narrative approach was used for data synthesis.
RESULTS
The search process returned 1470 records. Of these records, 46 met all eligibility criteria. 60 different serious games were found in the included studies. Motor impairment was the most common health condition targeted by these serious games. Serious games in most of the studies were used for rehabilitation. The serious games in the majority of the included studies can be played by only single player. Most serious games were played on standalone devices (offline games). The most common genres of serious games were role-playing games, puzzle games, and platformer games. Unity was the most prominent game engine used to develop serious games. Personal computers (PCs) were the most common platforms used to play serious games. The most common algorithms used in the included studies were Support Vector Machine (SVM), Convolutional Neural Network (CNN), Artificial Neural Networks (ANN), and Random Forest (RF). The most common purposes of AI were the detection of disease and the evaluation of user's performance. The dataset size ranged from 36 to 795,600, with an average of about 52,124. The most common validation techniques used in the included studies were K-fold cross-validation and training test split validation. Accuracy was the most commonly used metric to evaluate the performance of AI models.
CONCLUSIONS
The last decade witnessed an increase in the development of AI-driven serious games for healthcare purposes and targeting various health conditions and leveraging multiple AI algorithms; this rising trend is expected to continue for years to come. While the evidence uncovered in this study shows promising applications of AI-driven serious games, larger and more rigorous, diverse, and robust studies may be needed to examine the efficacy and effectiveness of AI-driven serious games in different populations with different health conditions.
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29
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A causal learning framework for the analysis and interpretation of COVID-19 clinical data. PLoS One 2022; 17:e0268327. [PMID: 35588440 PMCID: PMC9119448 DOI: 10.1371/journal.pone.0268327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 04/27/2022] [Indexed: 11/29/2022] Open
Abstract
We present a workflow for clinical data analysis that relies on Bayesian Structure Learning (BSL), an unsupervised learning approach, robust to noise and biases, that allows to incorporate prior medical knowledge into the learning process and that provides explainable results in the form of a graph showing the causal connections among the analyzed features. The workflow consists in a multi-step approach that goes from identifying the main causes of patient’s outcome through BSL, to the realization of a tool suitable for clinical practice, based on a Binary Decision Tree (BDT), to recognize patients at high-risk with information available already at hospital admission time. We evaluate our approach on a feature-rich dataset of Coronavirus disease (COVID-19), showing that the proposed framework provides a schematic overview of the multi-factorial processes that jointly contribute to the outcome. We compare our findings with current literature on COVID-19, showing that this approach allows to re-discover established cause-effect relationships about the disease. Further, our approach yields to a highly interpretable tool correctly predicting the outcome of 85% of subjects based exclusively on 3 features: age, a previous history of chronic obstructive pulmonary disease and the PaO2/FiO2 ratio at the time of arrival to the hospital. The inclusion of additional information from 4 routine blood tests (Creatinine, Glucose, pO2 and Sodium) increases predictive accuracy to 94.5%.
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30
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Semenova Y, Trenina V, Pivina L, Glushkova N, Zhunussov Y, Ospanov E, Bjørklund G. The lessons of COVID-19, SARS, and MERS: Implications for preventive strategies. INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT 2022. [DOI: 10.1080/20479700.2022.2051126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Yuliya Semenova
- Department of Neurology, Ophthalmology and Otolaryngology, Semey Medical University, Semey, Kazakhstan
- CONEM Kazakhstan Environmental Health and Safety Research Group, Semey Medical University, Semey, Kazakhstan
| | - Varvara Trenina
- Department of Neurology, Ophthalmology and Otolaryngology, Semey Medical University, Semey, Kazakhstan
| | - Lyudmila Pivina
- CONEM Kazakhstan Environmental Health and Safety Research Group, Semey Medical University, Semey, Kazakhstan
- Department of Emergency Medicine, Semey Medical University, Semey, Kazakhstan
| | - Natalya Glushkova
- Department of Epidemiology, Biostatistics & Evidence Based Medicine, Al-Farabi Kazakh National University, Almaty, Kazakhstan
| | | | - Erlan Ospanov
- Department of Neurology, Ophthalmology and Otolaryngology, Semey Medical University, Semey, Kazakhstan
| | - Geir Bjørklund
- Council for Nutritional and Environmental Medicine (CONEM), Mo i Rana, Norway
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31
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Woo JH, Kim EC, Kim SM. The Current Status of Breakthrough Devices Designation in the United States and Innovative Medical Devices Designation in Korea for Digital Health Software. Expert Rev Med Devices 2022; 19:213-228. [PMID: 35255755 DOI: 10.1080/17434440.2022.2051479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Artificial Intelligence (AI) is becoming increasingly utilized in the medical device industry as it can address unmet demands in clinical sites and provide more patient treatment options. This study aims to analyze the FDA's Breakthrough Device Program and MFDS' Innovative Medical Device Program, which support regulatory science for innovative medical devices today. Through this study, it is intended to enable prediction of current development trends of Software as a Medical Device (SaMD) and Digital Therapeutics (DTx), which combine AI and technologies to be used in the clinical field soon. AREAS COVERED A systematic search was conducted on the broad topics of "FDA and MFDS Program's SaMD, DTx". A parallel review and update of PubMed, and the official websites were conducted to investigate the regulator's databases, review official press releases of regulatory agencies, and provide detailed descriptions of researchers. EXPERT OPINION The efforts of related stakeholders are needed to expand AI technology to diagnosis, prevention, and treatment technologies for diseases that are difficult to diagnose early or are classified as clinical challenges. It is important to prepare regulatory policies suitable for the rapid pace of technological development and to create an environment where regulatory science can be realized by developers.
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Affiliation(s)
- Jae Hyun Woo
- Research Institute for Commercialization of Biomedical Convergence Technology, Seoul, Republic of Korea.,Medical Device Industry Program in Graduate School, Dongguk University, Seoul, Republic of Korea.,National Institute of Medical Device Safety Information, Seoul, Republic of Korea.,Department of Medical Biotechnology, Dongguk University-Seoul, Seoul, Korea
| | - Eun Cheol Kim
- Research Institute for Commercialization of Biomedical Convergence Technology, Seoul, Republic of Korea.,Medical Device Industry Program in Graduate School, Dongguk University, Seoul, Republic of Korea.,National Institute of Medical Device Safety Information, Seoul, Republic of Korea.,Department of Medical Biotechnology, Dongguk University-Seoul, Seoul, Korea
| | - Sung Min Kim
- Research Institute for Commercialization of Biomedical Convergence Technology, Seoul, Republic of Korea.,Medical Device Industry Program in Graduate School, Dongguk University, Seoul, Republic of Korea.,National Institute of Medical Device Safety Information, Seoul, Republic of Korea.,Department of Medical Biotechnology, Dongguk University-Seoul, Seoul, Korea
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32
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Uthman OA, Adetokunboh OO, Wiysonge CS, Al-Awlaqi S, Hanefeld J, El Bcheraoui C. Classification Schemes of COVID-19 High Risk Areas and Resulting Policies: A Rapid Review. Front Public Health 2022; 10:769174. [PMID: 35284361 PMCID: PMC8916531 DOI: 10.3389/fpubh.2022.769174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 01/20/2022] [Indexed: 11/13/2022] Open
Abstract
The COVID-19 pandemic has posed a significant global health threat since January 2020. Policies to reduce human mobility have been recognized to effectively control the spread of COVID-19; although the relationship between mobility, policy implementation, and virus spread remains contentious, with no clear pattern for how countries classify each other, and determine the destinations to- and from which to restrict travel. In this rapid review, we identified country classification schemes for high-risk COVID-19 areas and associated policies which mirrored the dynamic situation in 2020, with the aim of identifying any patterns that could indicate the effectiveness of such policies. We searched academic databases, including PubMed, Scopus, medRxiv, Google Scholar, and EMBASE. We also consulted web pages of the relevant government institutions in all countries. This rapid review's searches were conducted between October 2020 and December 2021. Web scraping of policy documents yielded additional 43 country reports on high-risk area classification schemes. In 43 countries from which relevant reports were identified, six issued domestic classification schemes. International classification schemes were issued by the remaining 38 countries, and these mainly used case incidence per 100,000 inhabitants as key indicator. The case incidence cut-off also varied across the countries, ranging from 20 cases per 100,000 inhabitants in the past 7 days to more than 100 cases per 100,000 inhabitants in the past 28 days. The criteria used for defining high-risk areas varied across countries, including case count, positivity rate, composite risk scores, community transmission and satisfactory laboratory testing. Countries either used case incidence in the past 7, 14 or 28 days. The resulting policies included restrictions on internal movement and international travel. The quarantine policies can be summarized into three categories: (1) 14 days self-isolation, (2) 10 days self-isolation and (3) 14 days compulsory isolation.
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Affiliation(s)
- Olalekan A. Uthman
- Warwick Centre for Global Health Research, The University of Warwick, Coventry, United Kingdom
| | - Olatunji O. Adetokunboh
- South African Centre for Epidemiological Modelling and Analysis, Stellenbosch University, Stellenbosch, South Africa
- Division of Epidemiology and Biostatistics, Department of Global Health, Stellenbosch University, South Africa
| | - Charles Shey Wiysonge
- Cochrane South Africa, South African Medical Research Council, Cape Town, South Africa
| | - Sameh Al-Awlaqi
- Evidence-Based Public Health, Centre for International Health Protection, Robert Koch Institute, Berlin, Germany
| | - Johanna Hanefeld
- Centre for International Health Protection, Robert Koch Institute, Berlin, Germany
| | - Charbel El Bcheraoui
- Evidence-Based Public Health, Centre for International Health Protection, Robert Koch Institute, Berlin, Germany
- *Correspondence: Charbel El Bcheraoui
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33
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Chen W, Yao M, Zhu Z, Sun Y, Han X. The application research of AI image recognition and processing technology in the early diagnosis of the COVID-19. BMC Med Imaging 2022; 22:29. [PMID: 35177020 PMCID: PMC8851724 DOI: 10.1186/s12880-022-00753-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 02/07/2022] [Indexed: 01/08/2023] Open
Abstract
Background This study intends to establish a combined prediction model that integrates the clinical symptoms,the lung lesion volume, and the radiomics features of patients with COVID-19, resulting in a new model to predict the severity of COVID-19. Methods The clinical data of 386 patients with COVID-19 at several hospitals, as well as images of certain patients during their hospitalization, were collected retrospectively to create a database of patients with COVID-19 pneumonia. The contour of lungs and lesion locations may be retrieved from CT scans using a CT-image-based quantitative discrimination and trend analysis method for COVID-19 and the Mask R-CNN deep neural network model to create 3D data of lung lesions. The quantitative COVID-19 factors were then determined, on which the diagnosis of the development of the patients' symptoms could be established. Then, using an artificial neural network, a prediction model of the severity of COVID-19 was constructed by combining characteristic imaging features on CT slices with clinical factors. ANN neural network was used for training, and tenfold cross-validation was used to verify the prediction model. The diagnostic performance of this model is verified by the receiver operating characteristic (ROC) curve. Results CT radiomics features extraction and analysis based on a deep neural network can detect COVID-19 patients with an 86% sensitivity and an 85% specificity. According to the ROC curve, the constructed severity prediction model indicates that the AUC of patients with severe COVID-19 is 0.761, with sensitivity and specificity of 79.1% and 73.1%, respectively. Conclusions The combined prediction model for severe COVID-19 pneumonia, which is based on deep learning and integrates clinical aspects, pulmonary lesion volume, and radiomics features of patients, has a remarkable differential ability for predicting the course of disease in COVID-19 patients. This may assist in the early prevention of severe COVID-19 symptoms.
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Affiliation(s)
- Wenyu Chen
- Department of Respiration, Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Ming Yao
- Department of Pain Medicine Center, Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Zhenyu Zhu
- Yangtze Delta Region Institute of Tsinghua University, Zhejiang, No. 705, Asia Pacific Road, Nanhu District, Jiaxing, 314006, Zhejiang, China
| | - Yanbao Sun
- Radiology Department, Affiliated Hospital of Jiaxing University, No. 1882 Zhonghuan South Road, Jiaxing, 314000, China.
| | - Xiuping Han
- Yangtze Delta Region Institute of Tsinghua University, Zhejiang, No. 705, Asia Pacific Road, Nanhu District, Jiaxing, 314006, Zhejiang, China.
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Gao Y, Xiong X, Jiao X, Yu Y, Chi J, Zhang W, Chen L, Li S, Gao Q. PRCTC: a machine learning model for prediction of response to corticosteroid therapy in COVID-19 patients. Aging (Albany NY) 2022; 14:54-72. [PMID: 35021153 PMCID: PMC8791209 DOI: 10.18632/aging.203819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 12/25/2021] [Indexed: 01/08/2023]
Abstract
Corticosteroid has been proved to be one of the few effective treatments for COVID-19 patients. However, not all the patients were suitable for corticosteroid therapy. In this study, we aimed to propose a machine learning model to forecast the response to corticosteroid therapy in COVID-19 patients. We retrospectively collected the clinical data about 666 COVID-19 patients receiving corticosteroid therapy between January 27, 2020, and March 30, 2020, from two hospitals in China. The response to corticosteroid therapy was evaluated by hospitalization time, oxygen supply duration, and the outcomes of patients. Least Absolute Shrinkage and Selection Operator (LASSO) was applied for feature selection. Five prediction models were applied in the training cohort and assessed in an internal and an external validation dataset, respectively. Finally, two (C reactive protein, lymphocyte percent) of 36 candidate immune/inflammatory features were finally used for model development. All five models displayed promising predictive performance. Notably, the ensemble model, PRCTC (prediction of response to corticosteroid therapy in COVID-19 patients), derived from three prediction models including Gradient Boosted Decision Tree (GBDT), Neural Network (NN), and logistic regression (LR), achieved the best performance with an area under the curve (AUC) of 0.810 (95% confidence interval [CI] 0.760-0.861) in internal validation cohort and 0.845 (95% CI 0.779-0.911) in external validation cohort to predict patients' response to corticosteroid therapy. In conclusion, PRCTC proposed with universality and scalability is hopeful to provide tangible and prompt clinical decision support in management of COVID-19 patients and potentially extends to other medication predictions.
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Affiliation(s)
- Yue Gao
- Cancer Biology Research Center, Key Laboratory of Chinese Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China
| | - Xiaoming Xiong
- Cancer Biology Research Center, Key Laboratory of Chinese Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China
| | - Xiaofei Jiao
- Cancer Biology Research Center, Key Laboratory of Chinese Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China
| | - Yang Yu
- Cancer Biology Research Center, Key Laboratory of Chinese Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China
| | - Jianhua Chi
- Cancer Biology Research Center, Key Laboratory of Chinese Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China
| | - Wei Zhang
- Cancer Biology Research Center, Key Laboratory of Chinese Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China
| | - Lingxi Chen
- Department of Computer Science, City University of Hong Kong, Kowloon Tong 999077, Hong Kong
| | - Shuaicheng Li
- Department of Computer Science, City University of Hong Kong, Kowloon Tong 999077, Hong Kong
| | - Qinglei Gao
- Cancer Biology Research Center, Key Laboratory of Chinese Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China
- Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China
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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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Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments. Symmetry (Basel) 2021. [DOI: 10.3390/sym14010016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
This paper presents the role of artificial intelligence (AI) and other latest technologies that were employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). These technologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablers of these technologies was data that was obtained from heterogeneous sources (i.e., social networks (SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiological investigations, and other digital/sensing platforms). To this end, we provide an insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss major services that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI role in seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions (NPIs)), (ii) AI role in data life cycle phases employed to control pandemic via digital solutions, (iii) AI role in performing analytics on heterogeneous types of data stemming from the COVID-19 pandemic, (iv) AI role in the healthcare sector in the context of COVID-19 pandemic, (v) general-purpose applications of AI in COVID-19 era, and (vi) AI role in drug design and repurposing (e.g., iteratively aligning protein spikes and applying three/four-fold symmetry to yield a low-resolution candidate template) against COVID-19. Further, we discuss the challenges involved in applying AI to the available data and privacy issues that can arise from personal data transitioning into cyberspace. We also provide a concise overview of other latest technologies that were increasingly applied to limit the spread of the ongoing pandemic. Finally, we discuss the avenues of future research in the respective area. This insightful review aims to highlight existing AI-based technological developments and future research dynamics in this area.
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Imami AS, McCullumsmith RE, O’Donovan SM. Strategies to identify candidate repurposable drugs: COVID-19 treatment as a case example. Transl Psychiatry 2021; 11:591. [PMID: 34785660 PMCID: PMC8594646 DOI: 10.1038/s41398-021-01724-w] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 10/26/2021] [Accepted: 11/02/2021] [Indexed: 02/07/2023] Open
Abstract
Drug repurposing is an invaluable strategy to identify new uses for existing drug therapies that overcome many of the time and financial costs associated with novel drug development. The COVID-19 pandemic has driven an unprecedented surge in the development and use of bioinformatic tools to identify candidate repurposable drugs. Using COVID-19 as a case study, we discuss examples of machine-learning and signature-based approaches that have been adapted to rapidly identify candidate drugs. The Library of Integrated Network-based Signatures (LINCS) and Connectivity Map (CMap) are commonly used repositories and have the advantage of being amenable to use by scientists with limited bioinformatic training. Next, we discuss how these recent advances in bioinformatic drug repurposing approaches might be adapted to identify repurposable drugs for CNS disorders. As the development of novel therapies that successfully target the cause of neuropsychiatric and neurological disorders has stalled, there is a pressing need for innovative strategies to treat these complex brain disorders. Bioinformatic approaches to identify repurposable drugs provide an exciting avenue of research that offer promise for improved treatments for CNS disorders.
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Affiliation(s)
- Ali S. Imami
- grid.267337.40000 0001 2184 944XDepartment of Neurosciences, University of Toledo, Toledo, OH USA
| | - Robert E. McCullumsmith
- grid.267337.40000 0001 2184 944XDepartment of Neurosciences, University of Toledo, Toledo, OH USA ,grid.422550.40000 0001 2353 4951Neurosciences Institute, Promedica, Toledo, OH USA
| | - Sinead M. O’Donovan
- grid.267337.40000 0001 2184 944XDepartment of Neurosciences, University of Toledo, Toledo, OH USA
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Loveys K, Sagar M, Pickering I, Broadbent E. A Digital Human for Delivering a Remote Loneliness and Stress Intervention to At-Risk Younger and Older Adults During the COVID-19 Pandemic: Randomized Pilot Trial. JMIR Ment Health 2021; 8:e31586. [PMID: 34596572 PMCID: PMC8577546 DOI: 10.2196/31586] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Revised: 09/22/2021] [Accepted: 09/24/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Loneliness is a growing public health issue that has been exacerbated in vulnerable groups during the COVID-19 pandemic. Computer agents are capable of delivering psychological therapies through the internet; however, there is limited research on their acceptability to date. OBJECTIVE The objectives of this study were to evaluate (1) the feasibility and acceptability of a remote loneliness and stress intervention with digital human delivery to at-risk adults and (2) the feasibility of the study methods in preparation for a randomized controlled trial. METHODS A parallel randomized pilot trial with a mixed design was conducted. Participants were adults aged 18 to 69 years with an underlying medical condition or aged 70 years or older with a Mini-Mental State Examination score of >24 (ie, at greater risk of developing severe COVID-19). Participants took part from their place of residence (independent living retirement village, 20; community dwelling, 7; nursing home, 3). Participants were randomly allocated to the intervention or waitlist control group that received the intervention 1 week later. The intervention involved completing cognitive behavioral and positive psychology exercises with a digital human facilitator on a website for at least 15 minutes per day over 1 week. The exercises targeted loneliness, stress, and psychological well-being. Feasibility was evaluated using dropout rates and behavioral observation data. Acceptability was evaluated from behavioral engagement data, the Friendship Questionnaire (adapted), self-report items, and qualitative questions. Psychological measures were administered to evaluate the feasibility of the trial methods and included the UCLA Loneliness Scale, the 4-item Perceived Stress Scale, a 1-item COVID-19 distress measure, the Flourishing Scale, and the Scale of Positive and Negative Experiences. RESULTS The study recruited 30 participants (15 per group). Participants were 22 older adults and 8 younger adults with a health condition. Six participants dropped out of the study. Thus, the data of 24 participants were analyzed (intervention group, 12; waitlist group, 12). The digital human intervention and trial methods were generally found to be feasible and acceptable in younger and older adults living independently, based on intervention completion, and behavioral, qualitative, and some self-report data. The intervention and trial methods were less feasible to nursing home residents who required caregiver assistance. Acceptability could be improved with additional content, tailoring to the population, and changes to the digital human's design. CONCLUSIONS Digital humans are a promising and novel technological solution for providing at-risk adults with access to remote psychological support during the COVID-19 pandemic. Research should further examine design techniques to improve their acceptability in this application and investigate intervention effectiveness in a randomized controlled trial. TRIAL REGISTRATION Australia New Zealand Clinical Trials Registry ACTRN12620000786998; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380113.
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Affiliation(s)
- Kate Loveys
- Department of Psychological Medicine, The University of Auckland, Auckland, New Zealand
| | - Mark Sagar
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.,Soul Machines Ltd, Auckland, New Zealand
| | - Isabella Pickering
- Department of Psychological Medicine, The University of Auckland, Auckland, New Zealand
| | - Elizabeth Broadbent
- Department of Psychological Medicine, The University of Auckland, Auckland, New Zealand
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Selvaraj C, Chandra I, Singh SK. Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries. Mol Divers 2021; 26:1893-1913. [PMID: 34686947 PMCID: PMC8536481 DOI: 10.1007/s11030-021-10326-z] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 09/24/2021] [Indexed: 12/27/2022]
Abstract
The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the available data resources and to find new and novel leads. Once the drug target is identified, several interdisciplinary areas work together with artificial intelligence (AI) and machine learning (ML) methods to get enriched drugs. These AI and ML methods are applied in every step of the computer-aided drug design, and integrating these AI and ML methods results in a high success rate of hit compounds. In addition, this AI and ML integration with high-dimension data and its powerful capacity have taken a step forward. Clinical trials output prediction through the AI/ML integrated models could further decrease the clinical trials cost by also improving the success rate. Through this review, we discuss the backend of AI and ML methods in supporting the computer-aided drug design, along with its challenge and opportunity for the pharmaceutical industry. From the available information or data, the AI and ML based prediction for the high throughput virtual screening. After this integration of AI and ML, the success rate of hit identification has gained a momentum with huge success by providing novel drugs.
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Affiliation(s)
- Chandrabose Selvaraj
- CADD and Molecular Modelling Lab, Department of Bioinformatics, Alagappa University, Science Block, Karaikudi, Tamil Nadu, 630004, India.
| | - Ishwar Chandra
- CADD and Molecular Modelling Lab, Department of Bioinformatics, Alagappa University, Science Block, Karaikudi, Tamil Nadu, 630004, India
| | - Sanjeev Kumar Singh
- CADD and Molecular Modelling Lab, Department of Bioinformatics, Alagappa University, Science Block, Karaikudi, Tamil Nadu, 630004, India.
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Bashar A, Latif G, Ben Brahim G, Mohammad N, Alghazo J. COVID-19 Pneumonia Detection Using Optimized Deep Learning Techniques. Diagnostics (Basel) 2021; 11:1972. [PMID: 34829319 PMCID: PMC8625739 DOI: 10.3390/diagnostics11111972] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 10/13/2021] [Accepted: 10/18/2021] [Indexed: 12/24/2022] Open
Abstract
It became apparent that mankind has to learn to live with and adapt to COVID-19, especially because the developed vaccines thus far do not prevent the infection but rather just reduce the severity of the symptoms. The manual classification and diagnosis of COVID-19 pneumonia requires specialized personnel and is time consuming and very costly. On the other hand, automatic diagnosis would allow for real-time diagnosis without human intervention resulting in reduced costs. Therefore, the objective of this research is to propose a novel optimized Deep Learning (DL) approach for the automatic classification and diagnosis of COVID-19 pneumonia using X-ray images. For this purpose, a publicly available dataset of chest X-rays on Kaggle was used in this study. The dataset was developed over three stages in a quest to have a unified COVID-19 entities dataset available for researchers. The dataset consists of 21,165 anterior-to-posterior and posterior-to-anterior chest X-ray images classified as: Normal (48%), COVID-19 (17%), Lung Opacity (28%) and Viral Pneumonia (6%). Data Augmentation was also applied to increase the dataset size to enhance the reliability of results by preventing overfitting. An optimized DL approach is implemented in which chest X-ray images go through a three-stage process. Image Enhancement is performed in the first stage, followed by Data Augmentation stage and in the final stage the results are fed to the Transfer Learning algorithms (AlexNet, GoogleNet, VGG16, VGG19, and DenseNet) where the images are classified and diagnosed. Extensive experiments were performed under various scenarios, which led to achieving the highest classification accuracy of 95.63% through the application of VGG16 transfer learning algorithm on the augmented enhanced dataset with freeze weights. This accuracy was found to be better as compared to the results reported by other methods in the recent literature. Thus, the proposed approach proved superior in performance as compared with that of other similar approaches in the extant literature, and it made a valuable contribution to the body of knowledge. Although the results achieved so far are promising, further work is planned to correlate the results of the proposed approach with clinical observations to further enhance the efficiency and accuracy of COVID-19 diagnosis.
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Affiliation(s)
- Abul Bashar
- Department of Computer Engineering, Prince Mohammad Bin Fahd University, Khobar 31952, Saudi Arabia
| | - Ghazanfar Latif
- Université du Québec à Chicoutimi, 555 Boulevard de l’Université, Chicoutimi, QC G7H2B1, Canada;
- Department of Computer Science, Prince Mohammad Bin Fahd University, Khobar 31952, Saudi Arabia;
| | - Ghassen Ben Brahim
- Department of Computer Science, Prince Mohammad Bin Fahd University, Khobar 31952, Saudi Arabia;
| | - Nazeeruddin Mohammad
- Cybersecurity Center, Prince Mohammad Bin Fahd University, Khobar 31952, Saudi Arabia;
| | - Jaafar Alghazo
- Department of Electrical and Computer Engineering, Virginia Military Institute, Lexington, VA 24450, USA;
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Toward the Integration of Technology-Based Interventions in the Care Pathway for People with Dementia: A Cross-National Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph181910405. [PMID: 34639704 PMCID: PMC8508540 DOI: 10.3390/ijerph181910405] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 09/30/2021] [Accepted: 09/30/2021] [Indexed: 11/16/2022]
Abstract
BACKGROUND The integration of technology-based interventions into health and care provision in our aging society is still a challenge especially in the care pathway for people with dementia. OBJECTIVE The study aims to: (1) identify which socio-demographic characteristics are independently associated with the use of the embodied conversational agent among subjects with dementia, (2) uncover patient cluster profiles based on these characteristics, and (3) discuss technology-based interventions challenges. METHODS A virtual agent was used for four weeks by 55 persons with dementia living in their home environment. RESULTS Participants evaluated the agent as easy-to-use and quickly learnable. They felt confident while using the system and expressed the willingness to use it frequently. Moreover, 21/55 of the patients perceived the virtual agent as a friend and assistant who they could feel close to and who would remind them of important things. CONCLUSIONS Technology-based interventions require a significant effort, such as personalized features and patient-centered care pathways, to be effective. Therefore, this study enriches the open discussion on how such virtual agents must be evidence-based related and designed by multidisciplinary teams, following patient-centered care as well as user-centered design approaches.
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Abd-Alrazaq A, Hassan A, Abuelezz I, Ahmed A, Alzubaidi MS, Shah U, Alhuwail D, Giannicchi A, Househ M. Overview of Technologies Implemented During the First Wave of the COVID-19 Pandemic: Scoping Review. J Med Internet Res 2021; 23:e29136. [PMID: 34406962 PMCID: PMC8767979 DOI: 10.2196/29136] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 04/28/2021] [Accepted: 06/20/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Technologies have been extensively implemented to provide health care services for all types of clinical conditions during the COVID-19 pandemic. While several reviews have been conducted regarding technologies used during the COVID-19 pandemic, they were limited by focusing either on a specific technology (or features) or proposed rather than implemented technologies. OBJECTIVE This review aims to provide an overview of technologies, as reported in the literature, implemented during the first wave of the COVID-19 pandemic. METHODS We conducted a scoping review using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) Extension for Scoping Reviews. Studies were retrieved by searching 8 electronic databases, checking the reference lists of included studies and relevant reviews (backward reference list checking), and checking studies that cited included studies (forward reference list checking). The search terms were chosen based on the target intervention (ie, technologies) and the target disease (ie, COVID-19). We included English publications that focused on technologies or digital tools implemented during the COVID-19 pandemic to provide health-related services regardless of target health condition, user, or setting. Two reviewers independently assessed the eligibility of studies and extracted data from eligible papers. We used a narrative approach to synthesize extracted data. RESULTS Of 7374 retrieved papers, 126 were deemed eligible. Telemedicine was the most common type of technology (107/126, 84.9%) implemented in the first wave of the COVID-19 pandemic, and the most common mode of telemedicine was synchronous (100/108, 92.6%). The most common purpose of the technologies was providing consultation (75/126, 59.5%), followed by following up with patients (45/126, 35.7%), and monitoring their health status (22/126, 17.4%). Zoom (22/126, 17.5%) and WhatsApp (12/126, 9.5%) were the most commonly used videoconferencing and social media platforms, respectively. Both health care professionals and health consumers were the most common target users (103/126, 81.7%). The health condition most frequently targeted was COVID-19 (38/126, 30.2%), followed by any physical health conditions (21/126, 16.7%), and mental health conditions (13/126, 10.3%). Technologies were web-based in 84.1% of the studies (106/126). Technologies could be used through 11 modes, and the most common were mobile apps (86/126, 68.3%), desktop apps (73/126, 57.9%), telephone calls (49/126, 38.9%), and websites (45/126, 35.7%). CONCLUSIONS Technologies played a crucial role in mitigating the challenges faced during the COVID-19 pandemic. We did not find papers describing the implementation of other technologies (eg, contact-tracing apps, drones, blockchain) during the first wave. Furthermore, technologies in this review were used for other purposes (eg, drugs and vaccines discovery, social distancing, and immunity passport). Future research on studies on these technologies and purposes is recommended, and further reviews are required to investigate technologies implemented in subsequent waves of the pandemic.
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Affiliation(s)
- Alaa Abd-Alrazaq
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Asmaa Hassan
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Israa Abuelezz
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Arfan Ahmed
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Mahmood Saleh Alzubaidi
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Uzair Shah
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Dari Alhuwail
- Information Science Department, Kuwait University, Kuwait, Kuwait
- Health Informatics Unit, Dasman Diabetes Institute, Kuwait, Kuwait
| | - Anna Giannicchi
- School of Professional Studies, Berkeley College, New York, NY, United States
| | - Mowafa Househ
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
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Yu C, Helwig EJ. Role of rehabilitation amidst the COVID-19 pandemic: a review. J Transl Med 2021; 19:376. [PMID: 34481486 PMCID: PMC8417619 DOI: 10.1186/s12967-021-03048-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 08/19/2021] [Indexed: 12/24/2022] Open
Abstract
COVID-19 remains globally a highly infectious disease targeting multiple organs. Rehabilitation is increasingly valued among the supportive care fields to combat COVID-19 as currently definitive curative treatment remains largely absent. This narrative review is to address rehabilitation related topics associated with the treatment of COVID-19 patients. Nosocomial spread remains a high risk for healthcare workers, with comparable high ratios of exposed workers suffering from the disease with more severe clinical course. Primary principle of rehabilitation is to protect rehabilitation physicians and cover all person-to-person interactions. Translating perspectives are encouraged through each multidisciplinary approach. Rehabilitation for the outpatient remains a potential beneficial approach. Artificial intelligence can potentially provide aid and possible answers to important problems that may emerge involving COVID-19. The real value of rehabilitation in COVID-19 may be very impactful and beneficial for patient's physical and mental health.
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Affiliation(s)
- Chaoran Yu
- Department of General Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
| | - Ernest Johann Helwig
- Tongji Medical College of Huazhong University of Science and Technology, Wuhan, People's Republic of China.
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Hassoun S, Jefferson F, Shi X, Stucky B, Wang J, Rosa E. Artificial Intelligence for Biology. Integr Comp Biol 2021; 61:2267-2275. [PMID: 34448841 DOI: 10.1093/icb/icab188] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Revised: 07/14/2021] [Accepted: 08/23/2021] [Indexed: 01/18/2023] Open
Abstract
Despite efforts to integrate research across different subdisciplines of biology, the scale of integration remains limited. We hypothesize that future generations of Artificial Intelligence (AI) technologies specifically adapted for biological sciences will help enable the reintegration of biology. AI technologies will allow us not only to collect, connect and analyze data at unprecedented scales, but also to build comprehensive predictive models that span various subdisciplines. They will make possible both targeted (testing specific hypotheses) and untargeted discoveries. AI for biology will be the cross-cutting technology that will enhance our ability to do biological research at every scale. We expect AI to revolutionize biology in the 21st century much like statistics transformed biology in the 20th century. The difficulties, however, are many, including data curation and assembly, development of new science in the form of theories that connect the subdisciplines, and new predictive and interpretable AI models that are more suited to biology than existing machine learning and AI techniques. Development efforts will require strong collaborations between biological and computational scientists. This white paper provides a vision for AI for Biology and highlights some challenges.
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Affiliation(s)
- Soha Hassoun
- Department of Computer Science, Tufts University, Medford, MA 02155, USA
| | - Felicia Jefferson
- Biology Academic Department, Fort Valley State University, Fort Valley, GA 31030, USA
| | - Xinghua Shi
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA
| | - Brian Stucky
- Florida Museum of Natural History, University of Florida, Gainesville, FL 32611, USA
| | - Jin Wang
- Department of Mathematics, University of Tennessee at Chattanooga, Chattanooga, TN 37403, USA
| | - Epaminondas Rosa
- Department of Physics and School of Biological Sciences, Illinois State University, Normal, IL 61790, USA
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Alzubaidi M, Zubaydi HD, Bin-Salem AA, Abd-Alrazaq AA, Ahmed A, Househ M. Role of deep learning in early detection of COVID-19: Scoping review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE UPDATE 2021; 1:100025. [PMID: 34345877 PMCID: PMC8321699 DOI: 10.1016/j.cmpbup.2021.100025] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 07/21/2021] [Accepted: 07/21/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Since the onset of the COVID-19 pandemic, the world witnessed disruption on an unprecedented scale affecting our daily lives including but not limited to healthcare, business, education, and transportation. Deep Learning (DL) is a branch of Artificial intelligence (AI) applications, the recent growth of DL includes features that could be helpful in fighting the COVID-19 pandemic. Utilizing such features could support public health efforts. OBJECTIVE Investigate the literature available in the use of DL technology to support dealing with the COVID-19 crisis. We summarize the literature that uses DL features to analyze datasets for the purpose of a quick COVID-19 detection. METHODS This review follows PRISMA Extension for Scoping Reviews (PRISMA-ScR). We have scanned the most two commonly used databases (IEEE, ACM). Search terms were identified based on the target intervention (DL) and the target population (COVID-19). Two authors independently handled study selection and one author assigned for data extraction. A narrative approach is used to synthesize the extracted data. RESULTS We retrieved 53 studies and after passing through PRISMA excluding criteria, only 17 studies are considered in this review. All studies used deep learning for detection of COVID-19 cases in early stage based on different diagnostic modalities. Convolutional Neural Network (CNN) and Transfer Learning (TL) were the most commonly used techniques. CONCLUSION The included studies showed that DL techniques has significant impact on early detection of COVID-19 with high accuracy rate. However, most of the proposed methods are still in development and not tested in a clinical setting. Further investigation and collaboration are required from the research community and healthcare professionals in order to develop and standardize guidelines for use of DL in the healthcare domain.
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Key Words
- AI, Artificial intelligence
- CNN, Convolutional Neural Network
- COVID-19
- COVID-19, Corona Virus 2019
- CT, Computed Tomography
- CXR, Chest X-Ray Radiography
- Coronavirus
- DL, Deep Learning
- Deep learning
- Machine learning
- RNN, Recurrent Neural Network
- SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus 2
- ULS, Ultrasonography
- WHO, World Health Organization
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Affiliation(s)
- Mahmood Alzubaidi
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Haider Dhia Zubaydi
- National Advanced IPv6 Centre, Universiti Sains Malaysia, Gelugor 11800, Penang, Malaysia
| | | | - Alaa A Abd-Alrazaq
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Arfan Ahmed
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Mowafa Househ
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
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Managing SARS-CoV-2 Testing in Schools with an Artificial Intelligence Model and Application Developed by Simulation Data. ELECTRONICS 2021. [DOI: 10.3390/electronics10141626] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Research on SARS-CoV-2 and its social implications have become a major focus to interdisciplinary teams worldwide. As interest in more direct solutions, such as mass testing and vaccination grows, several studies appear to be dedicated to the operationalization of those solutions, leveraging both traditional and new methodologies, and, increasingly, the combination of both. This research examines the challenges anticipated for preventative testing of SARS-CoV-2 in schools and proposes an artificial intelligence (AI)-powered agent-based model crafted specifically for school scenarios. This research shows that in the absence of real data, simulation-based data can be used to develop an artificial intelligence model for the application of rapid assessment of school testing policies.
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Artificial Intelligence and COVID-19: A Systematic umbrella review and roads ahead. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2021. [PMID: 37520766 PMCID: PMC8831917 DOI: 10.1016/j.jksuci.2021.07.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Artificial Intelligence (AI) has played a substantial role in the response to the challenges posed by the current pandemic. The growing interest in using AI to handle Covid-19 issues has accelerated the pace of AI research and resulted in an exponential increase in articles and review studies within a very short period of time. Hence, it is becoming challenging to explore the large corpus of academic publications dedicated to the global health crisis. Even with the presence of systematic review studies, given their number and diversity, identifying trends and research avenues beyond the pandemic should be an arduous task. We conclude therefore that after the one-year mark of the declaration of Covid-19 as a pandemic, the accumulated scientific contribution lacks two fundamental aspects: Knowledge synthesis and Future projections. In contribution to fill this void, this paper is a (i) synthesis study and (ii) foresight exercise. The synthesis study aims to provide the scholars a consolidation of findings and a knowledge synthesis through a systematic review of the reviews (umbrella review) studying AI applications against Covid-19. Following the PRISMA guidelines, we systematically searched PubMed, Scopus, and other preprint sources from 1st December 2019 to 1st June 2021 for eligible reviews. The literature search and screening process resulted in 45 included reviews. Our findings reveal patterns, relationships, and trends in the AI research community response to the pandemic. We found that in the space of few months, the research objectives of the literature have developed rapidly from identifying potential AI applications to evaluating current uses of intelligent systems. Only few reviews have adopted the meta-analysis as a study design. Moreover, a clear dominance of the medical theme and the DNN methods has been observed in the reported AI applications. Based on its constructive systematic umbrella review, this work conducts a foresight exercise that tries to envision the post-Covid-19 research landscape of the AI field. We see seven key themes of research that may be an outcome of the present crisis and which advocate a more sustainable and responsible form of intelligent systems. We set accordingly a post-pandemic research agenda articulated around these seven drivers. The results of this study can be useful for the AI research community to obtain a holistic view of the current literature and to help prioritize research needs as we are heading toward the new normal.
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Kumar V, Singh D, Kaur M, Damaševičius R. Overview of current state of research on the application of artificial intelligence techniques for COVID-19. PeerJ Comput Sci 2021; 7:e564. [PMID: 34141890 PMCID: PMC8176528 DOI: 10.7717/peerj-cs.564] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 05/05/2021] [Indexed: 05/09/2023]
Abstract
BACKGROUND Until now, there are still a limited number of resources available to predict and diagnose COVID-19 disease. The design of novel drug-drug interaction for COVID-19 patients is an open area of research. Also, the development of the COVID-19 rapid testing kits is still a challenging task. METHODOLOGY This review focuses on two prime challenges caused by urgent needs to effectively address the challenges of the COVID-19 pandemic, i.e., the development of COVID-19 classification tools and drug discovery models for COVID-19 infected patients with the help of artificial intelligence (AI) based techniques such as machine learning and deep learning models. RESULTS In this paper, various AI-based techniques are studied and evaluated by the means of applying these techniques for the prediction and diagnosis of COVID-19 disease. This study provides recommendations for future research and facilitates knowledge collection and formation on the application of the AI techniques for dealing with the COVID-19 epidemic and its consequences. CONCLUSIONS The AI techniques can be an effective tool to tackle the epidemic caused by COVID-19. These may be utilized in four main fields such as prediction, diagnosis, drug design, and analyzing social implications for COVID-19 infected patients.
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Affiliation(s)
- Vijay Kumar
- Computer Science and Engineering Department, National Institute of Technology, Hamirpur, Himachal Pradesh, India
| | - Dilbag Singh
- School of Engineering and Applied Sciences, Bennett University, Greater Noida, India
| | - Manjit Kaur
- School of Engineering and Applied Sciences, Bennett University, Greater Noida, India
| | - Robertas Damaševičius
- Faculty of Applied Mathematics, Silesian University of Technology, Gliwice, Poland
- Department of Applied Informatics, Vytautas Magnus University, Kaunas, Lithuania
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Abd-alrazaq A, Hassan A, Abuelezz I, Ahmed A, Alzubaidi MS, Shah U, Alhuwail D, Giannicchi A, Househ M. Overview of Technologies Implemented During the First Wave of the COVID-19 Pandemic: Scoping Review (Preprint).. [DOI: 10.2196/preprints.29136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
BACKGROUND
Technologies have been extensively implemented to provide health care services for all types of clinical conditions during the COVID-19 pandemic. While several reviews have been conducted regarding technologies used during the COVID-19 pandemic, they were limited by focusing either on a specific technology (or features) or proposed rather than implemented technologies.
OBJECTIVE
This review aims to provide an overview of technologies, as reported in the literature, implemented during the first wave of the COVID-19 pandemic.
METHODS
We conducted a scoping review using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) Extension for Scoping Reviews. Studies were retrieved by searching 8 electronic databases, checking the reference lists of included studies and relevant reviews (backward reference list checking), and checking studies that cited included studies (forward reference list checking). The search terms were chosen based on the target intervention (ie, technologies) and the target disease (ie, COVID-19). We included English publications that focused on technologies or digital tools implemented during the COVID-19 pandemic to provide health-related services regardless of target health condition, user, or setting. Two reviewers independently assessed the eligibility of studies and extracted data from eligible papers. We used a narrative approach to synthesize extracted data.
RESULTS
Of 7374 retrieved papers, 126 were deemed eligible. Telemedicine was the most common type of technology (107/126, 84.9%) implemented in the first wave of the COVID-19 pandemic, and the most common mode of telemedicine was synchronous (100/108, 92.6%). The most common purpose of the technologies was providing consultation (75/126, 59.5%), followed by following up with patients (45/126, 35.7%), and monitoring their health status (22/126, 17.4%). Zoom (22/126, 17.5%) and WhatsApp (12/126, 9.5%) were the most commonly used videoconferencing and social media platforms, respectively. Both health care professionals and health consumers were the most common target users (103/126, 81.7%). The health condition most frequently targeted was COVID-19 (38/126, 30.2%), followed by any physical health conditions (21/126, 16.7%), and mental health conditions (13/126, 10.3%). Technologies were web-based in 84.1% of the studies (106/126). Technologies could be used through 11 modes, and the most common were mobile apps (86/126, 68.3%), desktop apps (73/126, 57.9%), telephone calls (49/126, 38.9%), and websites (45/126, 35.7%).
CONCLUSIONS
Technologies played a crucial role in mitigating the challenges faced during the COVID-19 pandemic. We did not find papers describing the implementation of other technologies (eg, contact-tracing apps, drones, blockchain) during the first wave. Furthermore, technologies in this review were used for other purposes (eg, drugs and vaccines discovery, social distancing, and immunity passport). Future research on studies on these technologies and purposes is recommended, and further reviews are required to investigate technologies implemented in subsequent waves of the pandemic.
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Abd-alrazaq A, Schneider J, Alhuwail D, Toro CT, Ahmed A, Alajlani M, Househ M. The performance of artificial intelligence-driven technologies in diagnosing mental disorders: An umbrella review (Preprint).. [DOI: 10.2196/preprints.29235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
BACKGROUND
Diagnosing mental disorders is usually not an easy task and requires a large amount of time and effort given the complex nature of mental disorders. Artificial intelligence (AI) has been successfully exploited in diagnosing many mental disorders. Numerous systematic reviews summarize the evidence on the accuracy of AI models in diagnosing different mental disorders.
OBJECTIVE
This umbrella review aims to synthesize results of previous systematic reviews on the performance of AI models in diagnosing mental disorders.
METHODS
To identify relevant systematic reviews, we searched 11 electronic databases, checked the reference list of the included reviews, and checked the reviews that cited the included reviews. Two reviewers independently selected the relevant reviews, extracted the data from them, and appraised their quality. We synthesized the extracted data using the narrative approach. Specifically, results of the included reviews were grouped based on the target mental disorders that the AI classifiers distinguish.
RESULTS
We included 15 systematic reviews of 852 citations identified by searching all databases. The included reviews assessed the performance of AI models in diagnosing Alzheimer’s disease (n=7), mild cognitive impairment (n=6), schizophrenia (n=3), bipolar disease (n=2), autism spectrum disorder (n=1), obsessive-compulsive disorder (n=1), post-traumatic stress disorder (n=1), and psychotic disorders (n=1). The performance of the AI models in diagnosing these mental disorders ranged between 21% and 100%.
CONCLUSIONS
AI technologies offer great promise in diagnosing mental health disorders. The reported performance metrics paint a vivid picture of a bright future for AI in this field. To expedite progress towards these technologies being incorporated into routine practice, we recommend that healthcare professionals in the field cautiously and consciously begin to explore the opportunities of AI-based tools for their daily routine. It would also be encouraging to see a greater number of meta-analyses and further systematic reviews on performance of AI models in diagnosing other common mental disorders such as depression and anxiety.
CLINICALTRIAL
CRD42021231558
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