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Himel GMS, Hasan MS, Salsabil US, Islam MM. MedLingua: A conceptual framework for a multilingual medical conversational agent. MethodsX 2024; 12:102614. [PMID: 38439929 PMCID: PMC10909737 DOI: 10.1016/j.mex.2024.102614] [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: 12/28/2023] [Accepted: 02/10/2024] [Indexed: 03/06/2024] Open
Abstract
This study introduces a hybrid model for an advanced medical chatbot addressing crucial healthcare communication challenges. Leveraging a hybrid ML model, the chatbot aims to provide accurate and prompt responses to users' health-related queries. The proposed model will overcome limitations observed in previous medical chatbots by integrating a dual-stemming approach, P-Stemmer and NLTK-Stemmer, accommodating both semitic and non-semitic languages. The system prioritizes the analysis of cognates, identification of symptoms, doctor recommendations, and prescription generation. It integrates an automatic translation module to facilitate a smooth multilingual diagnostic experience. Following the Scrum methodology for agile development, the framework ensures adaptability to evolving research needs and stays current with recent medical discoveries. This groundbreaking idea aims to improve the effectiveness and availability of healthcare services by introducing an intelligent, multilingual chatbot. This technology enables patients to communicate with doctors from diverse linguistic backgrounds through an automated language translation model, eliminating language barriers and extending healthcare access to rural regions worldwide.•A simple but efficient hybrid conceptual model for advancement in smart medical assistance.•This conceptual model can be applied to implement a medical chatbot that can understand multiple languages.•This method can be utilized to address medical chatbot limitations and enhance accuracy in response generation.
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Affiliation(s)
- Galib Muhammad Shahriar Himel
- Department of Computer Science, American International University-Bangladesh, Dhaka, Bangladesh
- School of Computer Sciences, Universiti Sains Malaysia, 11800 USM Penang, Malaysia
| | - Md. Shourov Hasan
- Department of Computer Science, American International University-Bangladesh, Dhaka, Bangladesh
| | - Umme Sadia Salsabil
- Department of Computer Science, American International University-Bangladesh, Dhaka, Bangladesh
| | - Md. Masudul Islam
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh
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Del Fiol G, Orleans B, Kuzmenko TV, Chipman J, Greene T, Martinez A, Wirth J, Meads R, Kaphingst KK, Gibson B, Kawamoto K, King AJ, Siaperas T, Hughes S, Pruhs A, Pariera Dinkins C, Lam CY, Pierce JH, Benson R, Borsato EP, Cornia R, Stevens L, Bradshaw RL, Schlechter CR, Wetter DW. SCALE-UP II: protocol for a pragmatic randomised trial examining population health management interventions to increase the uptake of at-home COVID-19 testing in community health centres. BMJ Open 2024; 14:e081455. [PMID: 38508633 PMCID: PMC10961568 DOI: 10.1136/bmjopen-2023-081455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 02/23/2024] [Indexed: 03/22/2024] Open
Abstract
INTRODUCTION SCALE-UP II aims to investigate the effectiveness of population health management interventions using text messaging (TM), chatbots and patient navigation (PN) in increasing the uptake of at-home COVID-19 testing among patients in historically marginalised communities, specifically, those receiving care at community health centres (CHCs). METHODS AND ANALYSIS The trial is a multisite, randomised pragmatic clinical trial. Eligible patients are >18 years old with a primary care visit in the last 3 years at one of the participating CHCs. Demographic data will be obtained from CHC electronic health records. Patients will be randomised to one of two factorial designs based on smartphone ownership. Patients who self-report replying to a text message that they have a smartphone will be randomised in a 2×2×2 factorial fashion to receive (1) chatbot or TM; (2) PN (yes or no); and (3) repeated offers to interact with the interventions every 10 or 30 days. Participants who do not self-report as having a smartphone will be randomised in a 2×2 factorial fashion to receive (1) TM with or without PN; and (2) repeated offers every 10 or 30 days. The interventions will be sent in English or Spanish, with an option to request at-home COVID-19 test kits. The primary outcome is the proportion of participants using at-home COVID-19 tests during a 90-day follow-up. The study will evaluate the main effects and interactions among interventions, implementation outcomes and predictors and moderators of study outcomes. Statistical analyses will include logistic regression, stratified subgroup analyses and adjustment for stratification factors. ETHICS AND DISSEMINATION The protocol was approved by the University of Utah Institutional Review Board. On completion, study data will be made available in compliance with National Institutes of Health data sharing policies. Results will be disseminated through study partners and peer-reviewed publications. TRIAL REGISTRATION NUMBER ClinicalTrials.gov: NCT05533918 and NCT05533359.
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Affiliation(s)
- Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Brian Orleans
- Center for Health Outcomes and Population Equity, University of Utah Health Huntsman Cancer Institute, Salt Lake City, Utah, USA
| | - Tatyana V Kuzmenko
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Jonathan Chipman
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Tom Greene
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Anna Martinez
- Center for Health Outcomes and Population Equity, University of Utah Health Huntsman Cancer Institute, Salt Lake City, Utah, USA
| | - Jennifer Wirth
- Center for Health Outcomes and Population Equity, University of Utah Health Huntsman Cancer Institute, Salt Lake City, Utah, USA
| | - Ray Meads
- Center for Health Outcomes and Population Equity, University of Utah Health Huntsman Cancer Institute, Salt Lake City, Utah, USA
| | | | - Bryan Gibson
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Andy J King
- Department of Communication, University of Utah, Salt Lake City, Utah, USA
| | - Tracey Siaperas
- Association for Utah Community Health, Salt Lake City, Utah, USA
| | - Shlisa Hughes
- Association for Utah Community Health, Salt Lake City, Utah, USA
| | - Alan Pruhs
- Association for Utah Community Health, Salt Lake City, Utah, USA
| | | | - Cho Y Lam
- Center for Health Outcomes and Population Equity, University of Utah Health Huntsman Cancer Institute, Salt Lake City, Utah, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Joni H Pierce
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Ryzen Benson
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Emerson P Borsato
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Ryan Cornia
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Leticia Stevens
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Richard L Bradshaw
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Chelsey R Schlechter
- Center for Health Outcomes and Population Equity, University of Utah Health Huntsman Cancer Institute, Salt Lake City, Utah, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah, USA
| | - David W Wetter
- Center for Health Outcomes and Population Equity, University of Utah Health Huntsman Cancer Institute, Salt Lake City, Utah, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah, USA
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Šafran V, Lin S, Nateqi J, Martin AG, Smrke U, Ariöz U, Plohl N, Rojc M, Bēma D, Chávez M, Horvat M, Mlakar I. Multilingual Framework for Risk Assessment and Symptom Tracking (MRAST). SENSORS (BASEL, SWITZERLAND) 2024; 24:1101. [PMID: 38400259 PMCID: PMC10892413 DOI: 10.3390/s24041101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 02/02/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024]
Abstract
The importance and value of real-world data in healthcare cannot be overstated because it offers a valuable source of insights into patient experiences. Traditional patient-reported experience and outcomes measures (PREMs/PROMs) often fall short in addressing the complexities of these experiences due to subjectivity and their inability to precisely target the questions asked. In contrast, diary recordings offer a promising solution. They can provide a comprehensive picture of psychological well-being, encompassing both psychological and physiological symptoms. This study explores how using advanced digital technologies, i.e., automatic speech recognition and natural language processing, can efficiently capture patient insights in oncology settings. We introduce the MRAST framework, a simplified way to collect, structure, and understand patient data using questionnaires and diary recordings. The framework was validated in a prospective study with 81 colorectal and 85 breast cancer survivors, of whom 37 were male and 129 were female. Overall, the patients evaluated the solution as well made; they found it easy to use and integrate into their daily routine. The majority (75.3%) of the cancer survivors participating in the study were willing to engage in health monitoring activities using digital wearable devices daily for an extended period. Throughout the study, there was a noticeable increase in the number of participants who perceived the system as having excellent usability. Despite some negative feedback, 44.44% of patients still rated the app's usability as above satisfactory (i.e., 7.9 on 1-10 scale) and the experience with diary recording as above satisfactory (i.e., 7.0 on 1-10 scale). Overall, these findings also underscore the significance of user testing and continuous improvement in enhancing the usability and user acceptance of solutions like the MRAST framework. Overall, the automated extraction of information from diaries represents a pivotal step toward a more patient-centered approach, where healthcare decisions are based on real-world experiences and tailored to individual needs. The potential usefulness of such data is enormous, as it enables better measurement of everyday experiences and opens new avenues for patient-centered care.
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Affiliation(s)
- Valentino Šafran
- Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia; (V.Š.); (U.S.); (U.A.); (M.R.)
| | - Simon Lin
- Science Department, Symptoma GmbH, 1030 Vienna, Austria (A.G.M.)
- Department of Internal Medicine, Paracelsus Medical University, 5020 Salzburg, Austria
| | - Jama Nateqi
- Science Department, Symptoma GmbH, 1030 Vienna, Austria (A.G.M.)
- Department of Internal Medicine, Paracelsus Medical University, 5020 Salzburg, Austria
| | | | - Urška Smrke
- Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia; (V.Š.); (U.S.); (U.A.); (M.R.)
| | - Umut Ariöz
- Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia; (V.Š.); (U.S.); (U.A.); (M.R.)
| | - Nejc Plohl
- Department of Psychology, Faculty of Arts, University of Maribor, 2000 Maribor, Slovenia;
| | - Matej Rojc
- Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia; (V.Š.); (U.S.); (U.A.); (M.R.)
| | - Dina Bēma
- Institute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia;
| | - Marcela Chávez
- Department of Information System Management, Centre Hospitalier Universitaire de Liège, 4000 Liège, Belgium;
| | - Matej Horvat
- Department of Oncology, University Medical Centre Maribor, 2000 Maribor, Slovenia;
| | - Izidor Mlakar
- Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia; (V.Š.); (U.S.); (U.A.); (M.R.)
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Yin R, Neyens DM. Examining how information presentation methods and a chatbot impact the use and effectiveness of electronic health record patient portals: An exploratory study. PATIENT EDUCATION AND COUNSELING 2024; 119:108055. [PMID: 37976665 DOI: 10.1016/j.pec.2023.108055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 11/02/2023] [Accepted: 11/03/2023] [Indexed: 11/19/2023]
Abstract
OBJECTIVES Examining information presentation strategies that may facilitate patient education through patient portals is important for effective health education. METHODS A randomized exploratory study evaluated information presentation (text or videos) and a chatbot in patient education and examined several performance and outcome variables (e.g., search duration, Decisional Conflict Scale, and eye-tracking measures), along with a simple descriptive qualitative content analysis of the transcript of chatbot. RESULTS Of the 92 participants, those within the text conditions (n = 46, p < 0.001), had chatbot experiences (B =-74.85, p = 0.046), knew someone with IBD (B =-98.66, p = 0.039), and preferred to engage in medical decision-making (B =102.32, p = 0.006) were more efficient in information-searching. Participants with videos spent longer in information-searching (mean=666.5 (SD=171.6) VS 480.3 (SD=159.5) seconds, p < 0.001) but felt more informed (mean score=18.8 (SD=17.6) VS 27.4 (SD=18.9), p = 0.027). The participants' average eye fixation duration with videos was significantly higher (mean= 473.8 ms, SD=52.9, p < 0.001). CONCLUSIONS Participants in video conditions were less efficient but more effective in information seeking. Exploring the trade-offs between efficiency and effectiveness for user interface designs is important to appropriately deliver education within patient portals. PRACTICE IMPLICATIONS This study suggests that user interface designs and chatbots impact health information's efficiency and effectiveness.
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Affiliation(s)
- Rong Yin
- Department of Industrial Engineering, Pittsburg Institute, Sichuan University, Chengdu, Sichuan, China.
| | - David M Neyens
- Department of Industrial Engineering, Department of Bioengineering, Clemson University, Clemson, SC, USA
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Clark EC, Neumann S, Hopkins S, Kostopoulos A, Hagerman L, Dobbins M. Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review. JMIR Public Health Surveill 2024; 10:e49185. [PMID: 38241067 PMCID: PMC10837764 DOI: 10.2196/49185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 09/06/2023] [Accepted: 12/07/2023] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND Public health surveillance plays a vital role in informing public health decision-making. The onset of the COVID-19 pandemic in early 2020 caused a widespread shift in public health priorities. Global efforts focused on COVID-19 monitoring and contact tracing. Existing public health programs were interrupted due to physical distancing measures and reallocation of resources. The onset of the COVID-19 pandemic intersected with advancements in technologies that have the potential to support public health surveillance efforts. OBJECTIVE This scoping review aims to explore emergent public health surveillance methods during the early COVID-19 pandemic to characterize the impact of the pandemic on surveillance methods. METHODS A scoping search was conducted in multiple databases and by scanning key government and public health organization websites from March 2020 to January 2022. Published papers and gray literature that described the application of new or revised approaches to public health surveillance were included. Papers that discussed the implications of novel public health surveillance approaches from ethical, legal, security, and equity perspectives were also included. The surveillance subject, method, location, and setting were extracted from each paper to identify trends in surveillance practices. Two public health epidemiologists were invited to provide their perspectives as peer reviewers. RESULTS Of the 14,238 unique papers, a total of 241 papers describing novel surveillance methods and changes to surveillance methods are included. Eighty papers were review papers and 161 were single studies. Overall, the literature heavily featured papers detailing surveillance of COVID-19 transmission (n=187). Surveillance of other infectious diseases was also described, including other pathogens (n=12). Other public health topics included vaccines (n=9), mental health (n=11), substance use (n=4), healthy nutrition (n=1), maternal and child health (n=3), antimicrobial resistance (n=2), and misinformation (n=6). The literature was dominated by applications of digital surveillance, for example, by using big data through mobility tracking and infodemiology (n=163). Wastewater surveillance was also heavily represented (n=48). Other papers described adaptations to programs or methods that existed prior to the COVID-19 pandemic (n=9). The scoping search also found 109 papers that discuss the ethical, legal, security, and equity implications of emerging surveillance methods. The peer reviewer public health epidemiologists noted that additional changes likely exist, beyond what has been reported and available for evidence syntheses. CONCLUSIONS The COVID-19 pandemic accelerated advancements in surveillance and the adoption of new technologies, especially for digital and wastewater surveillance methods. Given the investments in these systems, further applications for public health surveillance are likely. The literature for surveillance methods was dominated by surveillance of infectious diseases, particularly COVID-19. A substantial amount of literature on the ethical, legal, security, and equity implications of these emerging surveillance methods also points to a need for cautious consideration of potential harm.
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Affiliation(s)
- Emily C Clark
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
| | - Sophie Neumann
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
| | - Stephanie Hopkins
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
| | - Alyssa Kostopoulos
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
| | - Leah Hagerman
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
| | - Maureen Dobbins
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
- School of Nursing, McMaster University, Hamilton, ON, Canada
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Lim WA, Custodio R, Sunga M, Amoranto AJ, Sarmiento RF. General Characteristics and Design Taxonomy of Chatbots for COVID-19: Systematic Review. J Med Internet Res 2024; 26:e43112. [PMID: 38064638 PMCID: PMC10773556 DOI: 10.2196/43112] [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: 09/30/2022] [Revised: 02/28/2023] [Accepted: 07/11/2023] [Indexed: 01/06/2024] Open
Abstract
BACKGROUND A conversational agent powered by artificial intelligence, commonly known as a chatbot, is one of the most recent innovations used to provide information and services during the COVID-19 pandemic. However, the multitude of conversational agents explicitly designed during the COVID-19 pandemic calls for characterization and analysis using rigorous technological frameworks and extensive systematic reviews. OBJECTIVE This study aims to describe the general characteristics of COVID-19 chatbots and examine their system designs using a modified adapted design taxonomy framework. METHODS We conducted a systematic review of the general characteristics and design taxonomy of COVID-19 chatbots, with 56 studies included in the final analysis. This review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select papers published between March 2020 and April 2022 from various databases and search engines. RESULTS Results showed that most studies on COVID-19 chatbot design and development worldwide are implemented in Asia and Europe. Most chatbots are also accessible on websites, internet messaging apps, and Android devices. The COVID-19 chatbots are further classified according to their temporal profiles, appearance, intelligence, interaction, and context for system design trends. From the temporal profile perspective, almost half of the COVID-19 chatbots interact with users for several weeks for >1 time and can remember information from previous user interactions. From the appearance perspective, most COVID-19 chatbots assume the expert role, are task oriented, and have no visual or avatar representation. From the intelligence perspective, almost half of the COVID-19 chatbots are artificially intelligent and can respond to textual inputs and a set of rules. In addition, more than half of these chatbots operate on a structured flow and do not portray any socioemotional behavior. Most chatbots can also process external data and broadcast resources. Regarding their interaction with users, most COVID-19 chatbots are adaptive, can communicate through text, can react to user input, are not gamified, and do not require additional human support. From the context perspective, all COVID-19 chatbots are goal oriented, although most fall under the health care application domain and are designed to provide information to the user. CONCLUSIONS The conceptualization, development, implementation, and use of COVID-19 chatbots emerged to mitigate the effects of a global pandemic in societies worldwide. This study summarized the current system design trends of COVID-19 chatbots based on 5 design perspectives, which may help developers conveniently choose a future-proof chatbot archetype that will meet the needs of the public in the face of growing demand for a better pandemic response.
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Affiliation(s)
- Wendell Adrian Lim
- National Telehealth Center, National Institutes of Health, University of the Philippines Manila, Manila, Philippines
| | - Razel Custodio
- National Telehealth Center, National Institutes of Health, University of the Philippines Manila, Manila, Philippines
| | - Monica Sunga
- National Telehealth Center, National Institutes of Health, University of the Philippines Manila, Manila, Philippines
| | - Abegail Jayne Amoranto
- National Telehealth Center, National Institutes of Health, University of the Philippines Manila, Manila, Philippines
| | - Raymond Francis Sarmiento
- National Telehealth Center, National Institutes of Health, University of the Philippines Manila, Manila, Philippines
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Malinverno L, Barros V, Ghisoni F, Visonà G, Kern R, Nickel PJ, Ventura BE, Šimić I, Stryeck S, Manni F, Ferri C, Jean-Quartier C, Genga L, Schweikert G, Lovrić M, Rosen-Zvi M. A historical perspective of biomedical explainable AI research. PATTERNS (NEW YORK, N.Y.) 2023; 4:100830. [PMID: 37720333 PMCID: PMC10500028 DOI: 10.1016/j.patter.2023.100830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
The black-box nature of most artificial intelligence (AI) models encourages the development of explainability methods to engender trust into the AI decision-making process. Such methods can be broadly categorized into two main types: post hoc explanations and inherently interpretable algorithms. We aimed at analyzing the possible associations between COVID-19 and the push of explainable AI (XAI) to the forefront of biomedical research. We automatically extracted from the PubMed database biomedical XAI studies related to concepts of causality or explainability and manually labeled 1,603 papers with respect to XAI categories. To compare the trends pre- and post-COVID-19, we fit a change point detection model and evaluated significant changes in publication rates. We show that the advent of COVID-19 in the beginning of 2020 could be the driving factor behind an increased focus concerning XAI, playing a crucial role in accelerating an already evolving trend. Finally, we present a discussion with future societal use and impact of XAI technologies and potential future directions for those who pursue fostering clinical trust with interpretable machine learning models.
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Affiliation(s)
| | - Vesna Barros
- AI for Accelerated Healthcare & Life Sciences Discovery, IBM R&D Laboratories, University of Haifa Campus, Mount Carmel, Haifa 3498825, Israel
- The Hebrew University of Jerusalem, Ein Kerem Campus, 9112102, Jerusalem, Israel
| | | | - Giovanni Visonà
- Empirical Inference, Max-Planck Institute for Intelligent Systems, 72076 Tübingen, Germany
| | - Roman Kern
- Institute of Interactive Systems and Data Science, Graz University of Technology, Sandgasse 36/III, 8010 Graz, Austria
- Know-Center GmbH, Sandgasse 36/4A 8010, Graz, Austria
| | - Philip J. Nickel
- Eindhoven University of Technology, 5135600 MB Eindhoven, The Netherlands
| | | | - Ilija Šimić
- Know-Center GmbH, Sandgasse 36/4A 8010, Graz, Austria
| | - Sarah Stryeck
- Research Center Pharmaceutical Engineering GmbH, Inffeldgasse 138010 Graz, Austria
| | | | - Cesar Ferri
- VRAIN, Universitat Politècnica de València, Camino de Vera, s/n 46022 Valencia, Spain
| | - Claire Jean-Quartier
- Research Data Management, Graz University of Technology, Brockmanngasse 84, 8010 Graz, Austria
| | - Laura Genga
- Eindhoven University of Technology, 5135600 MB Eindhoven, The Netherlands
| | - Gabriele Schweikert
- School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, UK
| | - Mario Lovrić
- Know-Center GmbH, Sandgasse 36/4A 8010, Graz, Austria
- Centre for Applied Bioanthropology, Institute for Anthropological Research, 10000 Zagreb, Croatia
| | - Michal Rosen-Zvi
- AI for Accelerated Healthcare & Life Sciences Discovery, IBM R&D Laboratories, University of Haifa Campus, Mount Carmel, Haifa 3498825, Israel
- The Hebrew University of Jerusalem, Ein Kerem Campus, 9112102, Jerusalem, Israel
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Schooley BL, Ahmed A, Maxwell J, Feldman SS. Predictors of COVID-19 From a Statewide Digital Symptom and Risk Assessment Tool: Cross-Sectional Study. J Med Internet Res 2023; 25:e46026. [PMID: 37490320 PMCID: PMC10410382 DOI: 10.2196/46026] [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: 01/26/2023] [Revised: 04/25/2023] [Accepted: 06/26/2023] [Indexed: 07/26/2023] Open
Abstract
BACKGROUND Some of the most vexing issues with the COVID-19 pandemic were the inability of facilities and events, such as schools and work areas, to track symptoms to mitigate the spread of the disease. To combat these challenges, many turned to the implementation of technology. Technology solutions to mitigate repercussions of the COVID-19 pandemic include tools that provide guidelines and interfaces to influence behavior, reduce exposure to the disease, and enable policy-driven avenues to return to a sense of normalcy. This paper presents the implementation and early evaluation of a return-to-work COVID-19 symptom and risk assessment tool. The system was implemented across 34 institutions of health and education in Alabama, including more than 174,000 users with over 4 million total uses and more than 86,000 reports of exposure risk between July 2020 and April 2021. OBJECTIVE This study aimed to explore the usage of technology, specifically a COVID-19 symptom and risk assessment tool, to mitigate exposure to COVID-19 within public spaces. More specifically, the objective was to assess the relationship between user-reported symptoms and exposure via a mobile health app, with confirmed COVID-19 cases reported by the Alabama Department of Public Health (ADPH). METHODS This cross-sectional study evaluated the relationship between confirmed COVID-19 cases and user-reported COVID-19 symptoms and exposure reported through the Healthcheck web-based mobile application. A dependent variable for confirmed COVID-19 cases in Alabama was obtained from ADPH. Independent variables (ie, health symptoms and exposure) were collected through Healthcheck survey data and included measures assessing COVID-19-related risk levels and symptoms. Multiple linear regression was used to examine the relationship between ADPH-confirmed diagnosis of COVID-19 and self-reported health symptoms and exposure via Healthcheck that were analyzed across the state population but not connected at the individual patient level. RESULTS Regression analysis showed that the self-reported information collected by Healthcheck significantly affects the number of COVID-19-confirmed cases. The results demonstrate that the average number of confirmed COVID-19 cases increased by 5 (high risk: β=5.10; P=.001), decreased by 24 (sore throat: β=-24.03; P=.001), and increased by 21 (nausea or vomiting: β=21.67; P=.02) per day for every additional self-report of symptoms by Healthcheck survey respondents. Congestion or runny nose was the most frequently reported symptom. Sore throat, low risk, high risk, nausea, or vomiting were all statistically significant factors. CONCLUSIONS The use of technology allowed organizations to remotely track a population as it is related to COVID-19. Healthcheck was a platform that aided in symptom tracking, risk assessment, and evaluation of status for admitting individuals into public spaces for people in the Alabama area. The confirmed relationship between symptom and exposure self-reporting using an app and population-wide confirmed cases suggests that further investigation is needed to determine the opportunity for such apps to mitigate disease spread at a community and individual level.
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Affiliation(s)
| | - Abdulaziz Ahmed
- University of Alabama at Birmingham, Birmingham, AL, United States
| | - Justine Maxwell
- University of Alabama at Birmingham, Birmingham, AL, United States
| | - Sue S Feldman
- University of Alabama at Birmingham, Birmingham, AL, United States
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Hogeveen S, Donaghy-Hughes M, Nova A, Saari M, Sinn CLJ, Northwood M, Heckman G, Geffen L, Hirdes JP. The interRAI COVID-19 vulnerability screener: Results of a health surveillance initiative for vulnerable adults in the community during the COVID-19 pandemic. Arch Gerontol Geriatr 2023; 113:105056. [PMID: 37207541 PMCID: PMC10159666 DOI: 10.1016/j.archger.2023.105056] [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: 01/17/2023] [Revised: 04/26/2023] [Accepted: 05/03/2023] [Indexed: 05/21/2023]
Abstract
During the pandemic, the interRAI COVID-19 Vulnerability Screener (CVS) was used to identify community-dwelling older adults or adults with disabilities at risk of negative outcomes and facilitate triage for follow-up with health/social services. The interRAI CVS, a standardized self-report instrument administered virtually by a lay-person, includes COVID-19-related items and psychosocial and physical vulnerability. Our objective was to describe those assessed and identify sub-groups at highest risk of adverse outcomes. Seven community-based organizations in Ontario, Canada, implemented the interRAI CVS. We used descriptive statistics to report results and created a priority indicator for monitoring and/or intervention based on possible COVID-19 symptoms and psychosocial/physical vulnerabilities. We used logistic regression to examine the association between priority level and risk of poor outcomes using fair/poor self-rated health as a proxy measure. The sample included 942 adults assessed (April-November 2020; mean age=79). About 10% of individuals reported potential COVID-19 symptoms and <1% had a positive COVID-19 test/diagnosis. Of those with psychosocial/physical vulnerabilities (73.1%), most common were depressed mood (20.9%), loneliness (21.6%), and limited access to food/medications (7.5%). Overall, 45.7% had a recent doctor or nurse practitioner visit. Odds of fair/poor self-reported health were highest among those who reported both possible symptoms of COVID-19 and psychosocial/physical vulnerabilities (OR 10.9, 95% CI 5.96-20.12) compared to those with neither symptoms nor psychosocial/physical vulnerabilities. The sample represents a population largely unaffected by COVID-19 itself but with identified vulnerabilities. The interRAI CVS allows community providers to stay connected and obtain a better understanding of vulnerable individuals' needs during the pandemic.
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Affiliation(s)
- Sophie Hogeveen
- McMaster Institute for Research on Aging, McMaster University, MIP Suite 109A, 1280 Main Street West, Hamilton, ON L8S 4K1, Canada.
| | - Megan Donaghy-Hughes
- Canadian College of Naturopathic Medicine, 1255 Sheppard Ave East, North York, ON M2K1E2, Canada.
| | - Amanda Nova
- School of Public Health Sciences, University of Waterloo, 200 University Ave. W, Waterloo, ON N2L 3G1, Canada.
| | - Margaret Saari
- SE Research Centre, 90 Allstate Parkway, Suite 300, Markham, Ontario L3R 6H3, Canada.
| | - Chi-Ling Joanna Sinn
- McMaster Institute for Research on Aging, McMaster University, MIP Suite 109A, 1280 Main Street West, Hamilton, ON L8S 4K1, Canada.
| | - Melissa Northwood
- Faculty of Nursing, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4K1, Canada.
| | - George Heckman
- School of Public Health Sciences, University of Waterloo, 200 University Ave. W, Waterloo, ON N2L 3G1, Canada.
| | - Leon Geffen
- Samson Institute for Ageing Research, 9 Gorge Road, Vredehoek 8001, South Africa.
| | - John P Hirdes
- School of Public Health Sciences, University of Waterloo, 200 University Ave. W, Waterloo, ON N2L 3G1, Canada.
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10
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Zobel M, Knapp B, Nateqi J, Martin A. Correlating global trends in COVID-19 cases with online symptom checker self-assessments. PLoS One 2023; 18:e0281709. [PMID: 36763699 PMCID: PMC9917242 DOI: 10.1371/journal.pone.0281709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 01/19/2023] [Indexed: 02/12/2023] Open
Abstract
BACKGROUND Online symptom checkers are digital health solutions that provide a differential diagnosis based on a user's symptoms. During the coronavirus disease 2019 (COVID-19) pandemic, symptom checkers have become increasingly important due to physical distance constraints and reduced access to in-person medical consultations. Furthermore, various symptom checkers specialised in the assessment of COVID-19 infection have been produced. OBJECTIVES Assess the correlation between COVID-19 risk assessments from an online symptom checker and current trends in COVID-19 infections. Analyse whether those correlations are reflective of various country-wise quality of life measures. Lastly, determine whether the trends found in symptom checker assessments predict or lag relative to those of the COVID-19 infections. MATERIALS AND METHODS In this study, we compile the outcomes of COVID-19 risk assessments provided by the symptom checker Symptoma (www.symptoma.com) in 18 countries with suitably large user bases. We analyse this dataset's spatial and temporal features compared to the number of newly confirmed COVID-19 cases published by the respective countries. RESULTS We find an average correlation of 0.342 between the number of Symptoma users assessed to have a high risk of a COVID-19 infection and the official COVID-19 infection numbers. Further, we show a significant relationship between that correlation and the self-reported health of a country. Lastly, we find that the symptom checker is, on average, ahead (median +3 days) of the official infection numbers for most countries. CONCLUSION We show that online symptom checkers can capture the national-level trends in coronavirus infections. As such, they provide a valuable and unique information source in policymaking against pandemics, unrestricted by conventional resources.
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Affiliation(s)
- Marc Zobel
- Data Science Department, Symptoma, Vienna, Austria
- * E-mail:
| | - Bernhard Knapp
- Data Science Department, Symptoma, Vienna, Austria
- Faculty Computer Science, University of Applied Sciences Technikum, Vienna, Austria
| | - Jama Nateqi
- Medical Department, Symptoma, Attersee, Austria
- Department of Internal Medicine, Paracelsus Medical University, Salzburg, Austria
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11
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Gulbransen-Diaz N, Yoo S, Wang AP. Nurse, Give Me the News! Understanding Support for and Opposition to a COVID-19 Health Screening System. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1164. [PMID: 36673919 PMCID: PMC9859575 DOI: 10.3390/ijerph20021164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 12/20/2022] [Accepted: 12/22/2022] [Indexed: 06/17/2023]
Abstract
Helping the sick and protecting the vulnerable has long been the credo of the health profession. In response to the coronavirus-disease-2019 (COVID-19 pandemic), hospitals and healthcare institutions have rapidly employed public health measures to mitigate patient and staff infection. This paper investigates staff and visitor responses to the COVID-19 eGate health screening system; a self-service technology (SST) which aims to protect health care workers and facilities from COVID-19. Our study evaluates the in situ deployment of the eGate, and employs a System Usability Scale (SUS) and questionnaire (n = 220) to understand staff and visitor's acceptance of the eGate. In detailing the themes relevant to those who advocate for the system and those who oppose it, we contribute towards a more detailed understanding of the use and non-use of health-screening SSTs. We conclude with a series of considerations for the design of future interactive screening systems within hospitals.
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Affiliation(s)
- Natalia Gulbransen-Diaz
- School of Architecture, Design and Planning, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Soojeong Yoo
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London W1W 7TY, UK
| | - Audrey P. Wang
- Biomedical Informatics and Digital Health, School of Medical Sciences, The University of Sydney, Camperdown, NSW 2006, Australia
- DHI Laboratory, Research Education Network, Western Sydney Local Health District, Westmead Health Precinct, Westmead, NSW 2145, Australia
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12
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Telehealth for COVID-19: A Conceptual Framework. JOURNAL OF HEALTHCARE ENGINEERING 2023; 2023:3679829. [PMID: 36818384 PMCID: PMC9929206 DOI: 10.1155/2023/3679829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 04/02/2022] [Accepted: 05/12/2022] [Indexed: 02/10/2023]
Abstract
The world has been going through the global crisis of the coronavirus (COVID-19). It is a challenging situation for every country to tackle its healthcare system. COVID-19 spreads through physical contact with COVID-positive patients and causes potential damage to the country's health and economy system. Therefore, to overcome the chance of spreading the disease, the only preventive measure is to maintain social distancing. In this vulnerable situation, virtual resources have been utilized in order to maintain social distance, i.e., the telehealth system has been proposed and developed to access healthcare services remotely and manage people's health conditions. The telehealth system could become a regular part of our healthcare system, and during any calamity or natural disaster, it could be used as an emergency response to deal with the catastrophe. For this purpose, we proposed a conceptual telehealth framework in response to COVID-19. We focused on identifying critical issues concerning the use of telehealth in healthcare setups. Furthermore, the factors influencing the implementation of the telehealth system have been explored in detail. The proposed telehealth system utilizes artificial intelligence and data science to regulate and maintain the system efficiently. Before implementing the telehealth system, it is required that prearrangements be made, such as appropriate funding measures, the skills to know technological usage, training sessions, and staff endorsement. The barriers and influencing factors provided in this article can be helpful for future developments in telehealth systems and for making fruitful progress in fighting pandemics like COVID-19. At the same time, the same approach can be used to save the lives of many frontline workers.
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Zhou S, Silvasstar J, Clark C, Salyers AJ, Chavez C, Bull SS. An artificially intelligent, natural language processing chatbot designed to promote COVID-19 vaccination: A proof-of-concept pilot study. Digit Health 2023; 9:20552076231155679. [PMID: 36896332 PMCID: PMC9989411 DOI: 10.1177/20552076231155679] [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: 05/24/2022] [Accepted: 01/20/2023] [Indexed: 03/08/2023] Open
Abstract
Objective Our goal is to establish the feasibility of using an artificially intelligent chatbot in diverse healthcare settings to promote COVID-19 vaccination. Methods We designed an artificially intelligent chatbot deployed via short message services and web-based platforms. Guided by communication theories, we developed persuasive messages to respond to users' COVID-19-related questions and encourage vaccination. We implemented the system in healthcare settings in the U.S. between April 2021 and March 2022 and logged the number of users, topics discussed, and information on system accuracy in matching responses to user intents. We regularly reviewed queries and reclassified responses to better match responses to query intents as COVID-19 events evolved. Results A total of 2479 users engaged with the system, exchanging 3994 COVID-19 relevant messages. The most popular queries to the system were about boosters and where to get a vaccine. The system's accuracy rate in matching responses to user queries ranged from 54% to 91.1%. Accuracy lagged when new information related to COVID emerged, such as that related to the Delta variant. Accuracy increased when we added new content to the system. Conclusions It is feasible and potentially valuable to create chatbot systems using AI to facilitate access to current, accurate, complete, and persuasive information on infectious diseases. Such a system can be adapted to use with patients and populations needing detailed information and motivation to act in support of their health.
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Affiliation(s)
- Shuo Zhou
- Department of Communication Studies, School of Communication and the System Health Lab, Hong Kong Baptist University, Hong Kong
| | - Joshva Silvasstar
- Department of Community and Behavioral Health and the mHealth Impact Lab, Colorado School of Public Health, Aurora, CO, USA
| | - Christopher Clark
- Department of Community and Behavioral Health and the mHealth Impact Lab, Colorado School of Public Health, Aurora, CO, USA.,Department of Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO, USA
| | - Adam J Salyers
- Department of Community and Behavioral Health and the mHealth Impact Lab, Colorado School of Public Health, Aurora, CO, USA
| | - Catia Chavez
- Department of Community and Behavioral Health and the mHealth Impact Lab, Colorado School of Public Health, Aurora, CO, USA
| | - Sheana S Bull
- Department of Community and Behavioral Health and the mHealth Impact Lab, Colorado School of Public Health, Aurora, CO, USA
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14
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Lin S, Nateqi J, Weingartner-Ortner R, Gruarin S, Marling H, Pilgram V, Lagler FB, Aigner E, Martin AG. An artificial intelligence-based approach for identifying rare disease patients using retrospective electronic health records applied for Pompe disease. Front Neurol 2023; 14:1108222. [PMID: 37153672 PMCID: PMC10160659 DOI: 10.3389/fneur.2023.1108222] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 04/03/2023] [Indexed: 05/10/2023] Open
Abstract
Objective We retrospectively screened 350,116 electronic health records (EHRs) to identify suspected patients for Pompe disease. Using these suspected patients, we then describe their phenotypical characteristics and estimate the prevalence in the respective population covered by the EHRs. Methods We applied Symptoma's Artificial Intelligence-based approach for identifying rare disease patients to retrospective anonymized EHRs provided by the "University Hospital Salzburg" clinic group. Within 1 month, the AI screened 350,116 EHRs reaching back 15 years from five hospitals, and 104 patients were flagged as probable for Pompe disease. Flagged patients were manually reviewed and assessed by generalist and specialist physicians for their likelihood for Pompe disease, from which the performance of the algorithms was evaluated. Results Of the 104 patients flagged by the algorithms, generalist physicians found five "diagnosed," 10 "suspected," and seven patients with "reduced suspicion." After feedback from Pompe disease specialist physicians, 19 patients remained clinically plausible for Pompe disease, resulting in a specificity of 18.27% for the AI. Estimating from the remaining plausible patients, the prevalence of Pompe disease for the greater Salzburg region [incl. Bavaria (Germany), Styria (Austria), and Upper Austria (Austria)] was one in every 18,427 people. Phenotypes for patient cohorts with an approximated onset of symptoms above or below 1 year of age were established, which correspond to infantile-onset Pompe disease (IOPD) and late-onset Pompe disease (LOPD), respectively. Conclusion Our study shows the feasibility of Symptoma's AI-based approach for identifying rare disease patients using retrospective EHRs. Via the algorithm's screening of an entire EHR population, a physician had only to manually review 5.47 patients on average to find one suspected candidate. This efficiency is crucial as Pompe disease, while rare, is a progressively debilitating but treatable neuromuscular disease. As such, we demonstrated both the efficiency of the approach and the potential of a scalable solution to the systematic identification of rare disease patients. Thus, similar implementation of this methodology should be encouraged to improve care for all rare disease patients.
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Affiliation(s)
- Simon Lin
- Science Department, Symptoma GmbH, Vienna, Austria
- Department of Internal Medicine, Paracelsus Medical University, Salzburg, Austria
| | - Jama Nateqi
- Science Department, Symptoma GmbH, Vienna, Austria
- Department of Internal Medicine, Paracelsus Medical University, Salzburg, Austria
| | | | | | | | - Vinzenz Pilgram
- Medical and Information Technology - MIT, University Hospital Salzburg (SALK), Salzburg, Austria
| | - Florian B. Lagler
- Medical and Information Technology - MIT, University Hospital Salzburg (SALK), Salzburg, Austria
- Department of Pediatrics and Institute for Inherited Metabolic Diseases, Paracelsus Medical University, Salzburg, Austria
| | - Elmar Aigner
- Department of Internal Medicine, Paracelsus Medical University, Salzburg, Austria
- Medical and Information Technology - MIT, University Hospital Salzburg (SALK), Salzburg, Austria
| | - Alistair G. Martin
- Science Department, Symptoma GmbH, Vienna, Austria
- *Correspondence: Alistair G. Martin
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Prasitpuriprecha C, Pitakaso R, Gonwirat S, Enkvetchakul P, Preeprem T, Jantama SS, Kaewta C, Weerayuth N, Srichok T, Khonjun S, Nanthasamroeng N. Embedded AMIS-Deep Learning with Dialog-Based Object Query System for Multi-Class Tuberculosis Drug Response Classification. Diagnostics (Basel) 2022; 12:diagnostics12122980. [PMID: 36552987 PMCID: PMC9777254 DOI: 10.3390/diagnostics12122980] [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: 10/25/2022] [Revised: 11/23/2022] [Accepted: 11/25/2022] [Indexed: 11/29/2022] Open
Abstract
A person infected with drug-resistant tuberculosis (DR-TB) is the one who does not respond to typical TB treatment. DR-TB necessitates a longer treatment period and a more difficult treatment protocol. In addition, it can spread and infect individuals in the same manner as regular TB, despite the fact that early detection of DR-TB could reduce the cost and length of TB treatment. This study provided a fast and effective classification scheme for the four subtypes of TB: Drug-sensitive tuberculosis (DS-TB), drug-resistant tuberculosis (DR-TB), multidrug-resistant tuberculosis (MDR-TB), and extensively drug-resistant tuberculosis (XDR-TB). The drug response classification system (DRCS) has been developed as a classification tool for DR-TB subtypes. As a classification method, ensemble deep learning (EDL) with two types of image preprocessing methods, four convolutional neural network (CNN) architectures, and three decision fusion methods have been created. Later, the model developed by EDL will be included in the dialog-based object query system (DBOQS), in order to enable the use of DRCS as the classification tool for DR-TB in assisting medical professionals with diagnosing DR-TB. EDL yields an improvement of 1.17-43.43% over the existing methods for classifying DR-TB, while compared with classic deep learning, it generates 31.25% more accuracy. DRCS was able to increase accuracy to 95.8% and user trust to 95.1%, and after the trial period, 99.70% of users were interested in continuing the utilization of the system as a supportive diagnostic tool.
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Affiliation(s)
| | - Rapeepan Pitakaso
- Department of Industrial Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
| | - Sarayut Gonwirat
- Department of Computer Engineering and Automation, Kalasin University, Kalasin 46000, Thailand
| | - Prem Enkvetchakul
- Department of Information Technology, Buriram Rajabhat University, Buriram 31000, Thailand
- Correspondence:
| | - Thanawadee Preeprem
- Faculty of Pharmaceutical Sciences, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
| | | | - Chutchai Kaewta
- Department of Computer Science, Ubon Ratchathani Rajabhat University, Ubon Ratchathani 34000, Thailand
| | - Nantawatana Weerayuth
- Department of Mechanical Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
| | - Thanatkij Srichok
- Department of Industrial Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
| | - Surajet Khonjun
- Department of Industrial Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
| | - Natthapong Nanthasamroeng
- Department of Engineering Technology, Ubon Ratchathani Rajabhat University, Ubon Ratchathani 34000, Thailand
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16
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Chang IC, Shih YS, Kuo KM. Why would you use medical chatbots? interview and survey. Int J Med Inform 2022; 165:104827. [DOI: 10.1016/j.ijmedinf.2022.104827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 05/24/2022] [Accepted: 06/28/2022] [Indexed: 11/28/2022]
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Karpiel I, Starcevic A, Urzeniczok M. Database and AI Diagnostic Tools Improve Understanding of Lung Damage, Correlation of Pulmonary Disease and Brain Damage in COVID-19. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22166312. [PMID: 36016071 PMCID: PMC9414394 DOI: 10.3390/s22166312] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 08/17/2022] [Accepted: 08/18/2022] [Indexed: 05/02/2023]
Abstract
The COVID-19 pandemic caused a sharp increase in the interest in artificial intelligence (AI) as a tool supporting the work of doctors in difficult conditions and providing early detection of the implications of the disease. Recent studies have shown that AI has been successfully applied in the healthcare sector. The objective of this paper is to perform a systematic review to summarize the electroencephalogram (EEG) findings in patients with coronavirus disease (COVID-19) and databases and tools used in artificial intelligence algorithms, supporting the diagnosis and correlation between lung disease and brain damage, and lung damage. Available search tools containing scientific publications, such as PubMed and Google Scholar, were comprehensively evaluated and searched with open databases and tools used in AI algorithms. This work aimed to collect papers from the period of January 2019-May 2022 including in their resources the database from which data necessary for further development of algorithms supporting the diagnosis of the respiratory system can be downloaded and the correlation between lung disease and brain damage can be evaluated. The 10 articles which show the most interesting AI algorithms, trained by using open databases and associated with lung diseases, were included for review with 12 articles related to EEGs, which have/or may be related with lung diseases.
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Affiliation(s)
- Ilona Karpiel
- Łukasiewicz Research Network—Institute of Medical Technology and Equipment, 41-800 Zabrze, Poland
- Correspondence:
| | - Ana Starcevic
- Laboratory for Multimodal Neuroimaging, Institute of Anatomy, Medical Faculty, University of Belgrade, 11000 Belgrade, Serbia
| | - Mirella Urzeniczok
- Łukasiewicz Research Network—Institute of Medical Technology and Equipment, 41-800 Zabrze, Poland
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18
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Deng X, Li H, Liao X, Qin Z, Xu F, Friedman S, Ma G, Ye K, Lin S. Building a predictive model to identify clinical indicators for COVID-19 using machine learning method. Med Biol Eng Comput 2022; 60:1763-1774. [PMID: 35469375 PMCID: PMC9037972 DOI: 10.1007/s11517-022-02568-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 03/25/2022] [Indexed: 01/08/2023]
Abstract
Although some studies tried to identify risk factors for COVID-19, the evidence comparing COVID-19 and community-acquired pneumonia (CAP) is inconclusive, and CAP is the most common pneumonia with similar symptoms as COVID-19. We conducted a case–control study with 35 routine-collected clinical indicators and demographic factors to identify predictors for COVID-19 with CAP as controls. We randomly split the dataset into a training set (70%) and testing set (30%). We built Explainable Boosting Machine to select the important factors and built a decision tree on selected variables to interpret their relationships. The top five individual predictors of COVID-19 are albumin, total bilirubin, monocyte count, alanine aminotransferase, and percentage of monocyte with the importance scores ranging from 0.078 to 0.567. The top systematic predictors for COVID-19 are liver function, monocyte increasing, plasma protein, granulocyte, and renal function (importance scores ranging 0.009–0.096). We identified five combinations of important indicators to screen COVID-19 patients from CAP patients with differentiating abilities ranging 83.3–100%. An online predictive tool for our model was published. Certain clinical indicators collected routinely from most hospitals could help screen and distinguish COVID-19 from CAP. While further verification is needed, our findings and predictive tool could help screen suspected COVID-19 cases.
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Affiliation(s)
- Xinlei Deng
- Department of Environmental Health Sciences, School of Public Health, University at Albany, State University of New York, Rensselaer, NY, USA
| | - Han Li
- Department of Hematology, Guangxi Academy of Medical Sciences & The People's Hospital Of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Xin Liao
- Department of Scientific Research, Guangxi Academy of Medical Sciences & The People's Hospital Of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Zhiqiang Qin
- Department of Respiratory, Guangxi Academy of Medical Sciences & The People's Hospital Of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Fan Xu
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & The People's Hospital Of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Samantha Friedman
- Department of Sociology, University at Albany, State University of New York, Albany, NY, USA
| | - Gang Ma
- Department of Obstetrics and Gynecology, Guangxi Academy of Medical Sciences & The People's Hospital Of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Kun Ye
- Department of Nephrology, Guangxi Academy of Medical Sciences & The People's Hospital Of Guangxi Zhuang Autonomous Region, Nanning, China.
| | - Shao Lin
- Department of Environmental Health Sciences, School of Public Health, University at Albany, State University of New York, Rensselaer, NY, USA.
- Department of Epidemiology and Biostatistics, School of Public Health, University at Albany, State University of New York, Rensselaer, NY, USA.
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19
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Abstract
Complex phenomena have some common characteristics, such as nonlinearity, complexity, and uncertainty. In these phenomena, components typically interact with each other and a part of the system may affect other parts or vice versa. Accordingly, the human brain, the Earth’s global climate, the spreading of viruses, the economic organizations, and some engineering systems such as the transportation systems and power grids can be categorized into these phenomena. Since both analytical approaches and AI methods have some specific characteristics in solving complex problems, a combination of these techniques can lead to new hybrid methods with considerable performance. This is why several types of research have recently been conducted to benefit from these combinations to predict the spreading of COVID-19 and its dynamic behavior. In this review, 80 peer-reviewed articles, book chapters, conference proceedings, and preprints with a focus on employing hybrid methods for forecasting the spreading of COVID-19 published in 2020 have been aggregated and reviewed. These documents have been extracted from Google Scholar and many of them have been indexed on the Web of Science. Since there were many publications on this topic, the most relevant and effective techniques, including statistical models and deep learning (DL) or machine learning (ML) approach, have been surveyed in this research. The main aim of this research is to describe, summarize, and categorize these effective techniques considering their restrictions to be used as trustable references for scientists, researchers, and readers to make an intelligent choice to use the best possible method for their academic needs. Nevertheless, considering the fact that many of these techniques have been used for the first time and need more evaluations, we recommend none of them as an ideal way to be used in their project. Our study has shown that these methods can hold the robustness and reliability of statistical methods and the power of computation of DL ones.
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Symptoms associated with a COVID-19 infection among a non-hospitalized cohort in Vienna. Wien Klin Wochenschr 2022; 134:344-350. [PMID: 35416543 PMCID: PMC9007045 DOI: 10.1007/s00508-022-02028-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 03/13/2022] [Indexed: 11/16/2022]
Abstract
Background Most clinical studies report the symptoms experienced by those infected with coronavirus disease 2019 (COVID-19) via patients already hospitalized. Here we analyzed the symptoms experienced outside of a hospital setting. Methods The Vienna Social Fund (FSW; Vienna, Austria), the Public Health Services of the City of Vienna (MA15) and the private company Symptoma collaborated to implement Vienna’s official online COVID-19 symptom checker. Users answered 12 yes/no questions about symptoms to assess their risk for COVID-19. They could also specify their age and sex, and whether they had contact with someone who tested positive for COVID-19. Depending on the assessed risk of COVID-19 positivity, a SARS-CoV‑2 nucleic acid amplification test (NAAT) was performed. In this publication, we analyzed which factors (symptoms, sex or age) are associated with COVID-19 positivity. We also trained a classifier to correctly predict COVID-19 positivity from the collected data. Results Between 2 November 2020 and 18 November 2021, 9133 people experiencing COVID-19-like symptoms were assessed as high risk by the chatbot and were subsequently tested by a NAAT. Symptoms significantly associated with a positive COVID-19 test were malaise, fatigue, headache, cough, fever, dysgeusia and hyposmia. Our classifier could successfully predict COVID-19 positivity with an area under the curve (AUC) of 0.74. Conclusion This study provides reliable COVID-19 symptom statistics based on the general population verified by NAATs. Supplementary Information The online version of this article (10.1007/s00508-022-02028-9) contains supplementary material, which is available to authorized users.
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Alemi F, Vang J, Guralnik E, Roess A. Modeling the Probability of COVID-19 Based on Symptom Screening and Prevalence of Influenza and Influenza-Like Illnesses. Qual Manag Health Care 2022; 31:85-91. [PMID: 35195616 PMCID: PMC8963439 DOI: 10.1097/qmh.0000000000000339] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
BACKGROUND The importance of various patient-reported signs and symptoms to the diagnosis of coronavirus disease 2019 (COVID-19) changes during, and outside, of the flu season. None of the current published studies, which focus on diagnosis of COVID-19, have taken this seasonality into account. OBJECTIVE To develop predictive algorithm, which estimates the probability of having COVID-19 based on symptoms, and which incorporates the seasonality and prevalence of influenza and influenza-like illness data. METHODS Differential diagnosis of COVID-19 and influenza relies on demographic characteristics (age, race, and gender), and respiratory (eg, fever, cough, and runny nose), gastrointestinal (eg, diarrhea, nausea, and loss of appetite), and neurological (eg, anosmia and headache) signs and symptoms. The analysis was based on the symptoms reported by COVID-19 patients, 774 patients in China and 273 patients in the United States. The analysis also included 2885 influenza and 884 influenza-like illnesses in US patients. Accuracy of the predictions was calculated using the average area under the receiver operating characteristic (AROC) curves. RESULTS The likelihood ratio for symptoms, such as cough, depended on the flu season-sometimes indicating COVID-19 and other times indicating the reverse. In 30-fold cross-validated data, the symptoms accurately predicted COVID-19 (AROC of 0.79), showing that symptoms can be used to screen patients in the community and prior to testing. CONCLUSION Community-based health care providers should follow different signs and symptoms for diagnosing COVID-19 during, and outside of, influenza season.
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Affiliation(s)
- Farrokh Alemi
- Departments of Health Administration and Policy (Dr Alemi and Ms Guralnik) and Global and Community Health (Dr Roess), George Mason University, Fairfax, Virginia; and Health Administration and Policy, George Mason University College of Health, Fairfax, Virginia (Dr Vang)
| | - Jee Vang
- Departments of Health Administration and Policy (Dr Alemi and Ms Guralnik) and Global and Community Health (Dr Roess), George Mason University, Fairfax, Virginia; and Health Administration and Policy, George Mason University College of Health, Fairfax, Virginia (Dr Vang)
| | - Elina Guralnik
- Departments of Health Administration and Policy (Dr Alemi and Ms Guralnik) and Global and Community Health (Dr Roess), George Mason University, Fairfax, Virginia; and Health Administration and Policy, George Mason University College of Health, Fairfax, Virginia (Dr Vang)
| | - Amira Roess
- Departments of Health Administration and Policy (Dr Alemi and Ms Guralnik) and Global and Community Health (Dr Roess), George Mason University, Fairfax, Virginia; and Health Administration and Policy, George Mason University College of Health, Fairfax, Virginia (Dr Vang)
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22
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A conversational agent for querying Italian Patient Information Leaflets and improving health literacy. Comput Biol Med 2021; 141:105004. [PMID: 34774337 DOI: 10.1016/j.compbiomed.2021.105004] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 10/29/2021] [Accepted: 10/29/2021] [Indexed: 11/21/2022]
Abstract
In the last years, the rise of digital technologies has enormously augmented the possibility for people to access health information and consult online versions of Patient Information Leaflets (PILs), enabling them to improve their knowledge about medication and adherence to therapies. However, health information may often be difficult to consult and comprehend due to an excessively lengthy and undersized text, coupled with the presence of many incomprehensible medical terms. To face these issues, this paper proposes a conversational agent as a valuable solution to simplify health information retrieval and improve health literacy in Italian by codifying PILs and making them query-able in natural language. In particular, the system has been devised to: i) comprehend natural language questions on medicines of interest; ii) proactively ask the user or automatically infer from the dialog state all the missing information necessary to generate an answer; iii) extract the answer from a structured knowledge base built from PILs of registered drugs. An experimental study has been carried out to evaluate both the performance and usability of the proposed system. Results showed an adequate ability of the system to handle most of the dialogues started by participants correctly, good users satisfaction, and, thus, proved its feasibility and usefulness.
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23
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Kim YJ, DeLisa JA, Chung YC, Shapiro NL, Kolar Rajanna SK, Barbour E, Loeb JA, Turner J, Daley S, Skowlund J, Krishnan JA. Recruitment in a research study via chatbot versus telephone outreach: a randomized trial at a minority-serving institution. J Am Med Inform Assoc 2021; 29:149-154. [PMID: 34741513 DOI: 10.1093/jamia/ocab240] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 09/25/2021] [Accepted: 10/23/2021] [Indexed: 11/13/2022] Open
Abstract
Chatbots are software applications to simulate a conversation with a person. The effectiveness of chatbots in facilitating the recruitment of study participants in research, specifically among racial and ethnic minorities, is unknown. The objective of this study is to compare a chatbot versus telephone-based recruitment in enrolling research participants from a predominantly minority patient population at an urban institution. We randomly allocated adults to receive either chatbot or telephone-based outreach regarding a study about vaccine hesitancy. The primary outcome was the proportion of participants who provided consent to participate in the study. In 935 participants, the proportion who answered contact attempts was significantly lower in the chatbot versus telephone group (absolute difference -21.8%; 95% confidence interval [CI] -27.0%, -16.5%; P < 0.001). The consent rate was also significantly lower in the chatbot group (absolute difference -3.4%; 95% CI -5.7%, -1.1%; P = 0.004). However, among participants who answered a contact attempt, the difference in consent rates was not significant. In conclusion, the consent rate was lower with chatbot compared to telephone-based outreach. The difference in consent rates was due to a lower proportion of participants in the chatbot group who answered a contact attempt.
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Affiliation(s)
- Yoo Jin Kim
- Breathe Chicago Center, Division of Pulmonary, Critical Care, Sleep, and Allergy, University of Illinois Chicago, Chicago, Illinois, USA
| | - Julie A DeLisa
- Breathe Chicago Center, Division of Pulmonary, Critical Care, Sleep, and Allergy, University of Illinois Chicago, Chicago, Illinois, USA
| | - Yu-Che Chung
- Breathe Chicago Center, Division of Pulmonary, Critical Care, Sleep, and Allergy, University of Illinois Chicago, Chicago, Illinois, USA
| | - Nancy L Shapiro
- Department of Pharmacy Practice, College of Pharmacy, University of Illinois Chicago, Chicago, Illinois, USA
| | - Subhash K Kolar Rajanna
- Center for Clinical and Translational Science, University of Illinois Chicago, Chicago, Illinois, USA
| | - Edward Barbour
- Center for Clinical and Translational Science, University of Illinois Chicago, Chicago, Illinois, USA
| | - Jeffrey A Loeb
- Center for Clinical and Translational Science, University of Illinois Chicago, Chicago, Illinois, USA.,Department of Neurology and Rehabilitation, University of Illinois Chicago, Chicago, Illinois, USA
| | | | | | | | - Jerry A Krishnan
- Breathe Chicago Center, Division of Pulmonary, Critical Care, Sleep, and Allergy, University of Illinois Chicago, Chicago, Illinois, USA
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24
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A Semantic Web Framework for Automated Smart Assistants: A Case Study for Public Health. BIG DATA AND COGNITIVE COMPUTING 2021. [DOI: 10.3390/bdcc5040057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The COVID-19 pandemic elucidated that knowledge systems will be instrumental in cases where accurate information needs to be communicated to a substantial group of people with different backgrounds and technological resources. However, several challenges and obstacles hold back the wide adoption of virtual assistants by public health departments and organizations. This paper presents the Instant Expert, an open-source semantic web framework to build and integrate voice-enabled smart assistants (i.e., chatbots) for any web platform regardless of the underlying domain and technology. The component allows non-technical domain experts to effortlessly incorporate an operational assistant with voice recognition capability into their websites. Instant Expert is capable of automatically parsing, processing, and modeling Frequently Asked Questions pages as an information resource as well as communicating with an external knowledge engine for ontology-powered inference and dynamic data use. The presented framework uses advanced web technologies to ensure reusability and reliability, and an inference engine for natural-language understanding powered by deep learning and heuristic algorithms. A use case for creating an informatory assistant for COVID-19 based on the Centers for Disease Control and Prevention (CDC) data is presented to demonstrate the framework’s usage and benefits.
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25
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Asada K, Komatsu M, Shimoyama R, Takasawa K, Shinkai N, Sakai A, Bolatkan A, Yamada M, Takahashi S, Machino H, Kobayashi K, Kaneko S, Hamamoto R. Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics. J Pers Med 2021; 11:886. [PMID: 34575663 PMCID: PMC8471764 DOI: 10.3390/jpm11090886] [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: 08/19/2021] [Revised: 09/01/2021] [Accepted: 09/02/2021] [Indexed: 12/12/2022] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic began at the end of December 2019, giving rise to a high rate of infections and causing COVID-19-associated deaths worldwide. It was first reported in Wuhan, China, and since then, not only global leaders, organizations, and pharmaceutical/biotech companies, but also researchers, have directed their efforts toward overcoming this threat. The use of artificial intelligence (AI) has recently surged internationally and has been applied to diverse aspects of many problems. The benefits of using AI are now widely accepted, and many studies have shown great success in medical research on tasks, such as the classification, detection, and prediction of disease, or even patient outcome. In fact, AI technology has been actively employed in various ways in COVID-19 research, and several clinical applications of AI-equipped medical devices for the diagnosis of COVID-19 have already been reported. Hence, in this review, we summarize the latest studies that focus on medical imaging analysis, drug discovery, and therapeutics such as vaccine development and public health decision-making using AI. This survey clarifies the advantages of using AI in the fight against COVID-19 and provides future directions for tackling the COVID-19 pandemic using AI techniques.
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Affiliation(s)
- Ken Asada
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Masaaki Komatsu
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Ryo Shimoyama
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Ken Takasawa
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Norio Shinkai
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Akira Sakai
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Amina Bolatkan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Masayoshi Yamada
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
- Department of Endoscopy, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
| | - Satoshi Takahashi
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Hidenori Machino
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Kazuma Kobayashi
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Syuzo Kaneko
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Ryuji Hamamoto
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
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26
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Tozzi AE, Gesualdo F, Urbani E, Sbenaglia A, Ascione R, Procopio N, Croci I, Rizzo C. Digital Surveillance Through an Online Decision Support Tool for COVID-19 Over One Year of the Pandemic in Italy: Observational Study. J Med Internet Res 2021; 23:e29556. [PMID: 34292866 PMCID: PMC8366755 DOI: 10.2196/29556] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 07/14/2021] [Accepted: 07/18/2021] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Italy has experienced severe consequences (ie, hospitalizations and deaths) during the COVID-19 pandemic. Online decision support systems (DSS) and self-triage applications have been used in several settings to supplement health authority recommendations to prevent and manage COVID-19. A digital Italian health tech startup, Paginemediche, developed a noncommercial, online DSS with a chat user interface to assist individuals in Italy manage their potential exposure to COVID-19 and interpret their symptoms since early in the pandemic. OBJECTIVE This study aimed to compare the trend in online DSS sessions with that of COVID-19 cases reported by the national health surveillance system in Italy, from February 2020 to March 2021. METHODS We compared the number of sessions by users with a COVID-19-positive contact and users with COVID-19-compatible symptoms with the number of cases reported by the national surveillance system. To calculate the distance between the time series, we used the dynamic time warping algorithm. We applied Symbolic Aggregate approXimation (SAX) encoding to the time series in 1-week periods. We calculated the Hamming distance between the SAX strings. We shifted time series of online DSS sessions 1 week ahead. We measured the improvement in Hamming distance to verify the hypothesis that online DSS sessions anticipate the trends in cases reported to the official surveillance system. RESULTS We analyzed 75,557 sessions in the online DSS; 65,207 were sessions by symptomatic users, while 19,062 were by contacts of individuals with COVID-19. The highest number of online DSS sessions was recorded early in the pandemic. Second and third peaks were observed in October 2020 and March 2021, respectively, preceding the surge in notified COVID-19 cases by approximately 1 week. The distance between sessions by users with COVID-19 contacts and reported cases calculated by dynamic time warping was 61.23; the distance between sessions by symptomatic users was 93.72. The time series of users with a COVID-19 contact was more consistent with the trend in confirmed cases. With the 1-week shift, the Hamming distance between the time series of sessions by users with a COVID-19 contact and reported cases improved from 0.49 to 0.46. We repeated the analysis, restricting the time window to between July 2020 and December 2020. The corresponding Hamming distance was 0.16 before and improved to 0.08 after the time shift. CONCLUSIONS Temporal trends in the number of online COVID-19 DSS sessions may precede the trend in reported COVID-19 cases through traditional surveillance. The trends in sessions by users with a contact with COVID-19 may better predict reported cases of COVID-19 than sessions by symptomatic users. Data from online DSS may represent a useful supplement to traditional surveillance and support the identification of early warning signals in the COVID-19 pandemic.
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Affiliation(s)
- Alberto Eugenio Tozzi
- Multifactorial and Complex Diseases Research Area, Bambino Gesù Children's Hospital IRCCS, Rome, Italy
| | - Francesco Gesualdo
- Multifactorial and Complex Diseases Research Area, Bambino Gesù Children's Hospital IRCCS, Rome, Italy
| | | | | | | | | | - Ileana Croci
- Multifactorial and Complex Diseases Research Area, Bambino Gesù Children's Hospital IRCCS, Rome, Italy
| | - Caterina Rizzo
- Clinical Pathways and Epidemiology Unit, Bambino Gesù Children's Hospital IRCCS, Rome, Italy
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27
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Borycki EM, Kushniruk AW, Kletke R, Vimarlund V, Senathirajah Y, Quintana Y. Enhancing Safety During a Pandemic Using Virtual Care Remote Monitoring Technologies and UML Modeling. Yearb Med Inform 2021; 30:264-271. [PMID: 33882599 PMCID: PMC8416194 DOI: 10.1055/s-0041-1726485] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVES This paper describes a methodology for gathering requirements and early design of remote monitoring technology (RMT) for enhancing patient safety during pandemics using virtual care technologies. As pandemics such as COrona VIrus Disease (COVID-19) progress there is an increasing need for effective virtual care and RMT to support patient care while they are at home. METHODS The authors describe their work in conducting literature reviews by searching PubMed.gov and the grey literature for articles, and government websites with guidelines describing the signs and symptoms of COVID-19, as well as the progression of the disease. The reviews focused on identifying gaps where RMT could be applied in novel ways and formed the basis for the subsequent modelling of use cases for applying RMT described in this paper. RESULTS The work was conducted in the context of a new Home of the Future laboratory which has been set up at the University of Victoria. The literature review led to the development of a number of object-oriented models for deploying RMT. This modeling is being used for a number of purposes, including for education of students in health infomatics as well as testing of new use cases for RMT with industrial collaborators and projects within the smart home of the future laboratory. CONCLUSIONS Object-oriented modeling, based on analysis of gaps in the literature, was found to be a useful approach for describing, communicating and teaching about potential new uses of RMT.
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Affiliation(s)
- Elizabeth M. Borycki
- School of Health Information Science, University of Victoria, Canada
- Michael Smith Foundation for Health Research, Vancouver, Canada
| | | | - Ryan Kletke
- School of Health Information Science, University of Victoria, Canada
| | - Vivian Vimarlund
- Department of Computer and Information Science, Linkoping University, Sweden
| | - Yalini Senathirajah
- Department of Biomedical Informatics, University of Pittsburgh, United States of America
| | - Yuri Quintana
- Division of Clinical Informatics, Harvard School of Medicine, Harvard University, United States of America
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28
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Guo Y, Zhang Y, Lyu T, Prosperi M, Wang F, Xu H, Bian J. The application of artificial intelligence and data integration in COVID-19 studies: a scoping review. J Am Med Inform Assoc 2021; 28:2050-2067. [PMID: 34151987 PMCID: PMC8344463 DOI: 10.1093/jamia/ocab098] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 05/03/2021] [Accepted: 05/06/2021] [Indexed: 12/23/2022] Open
Abstract
Objective To summarize how artificial intelligence (AI) is being applied in COVID-19 research and determine whether these AI applications integrated heterogenous data from different sources for modeling. Materials and Methods We searched 2 major COVID-19 literature databases, the National Institutes of Health’s LitCovid and the World Health Organization’s COVID-19 database on March 9, 2021. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline, 2 reviewers independently reviewed all the articles in 2 rounds of screening. Results In the 794 studies included in the final qualitative analysis, we identified 7 key COVID-19 research areas in which AI was applied, including disease forecasting, medical imaging-based diagnosis and prognosis, early detection and prognosis (non-imaging), drug repurposing and early drug discovery, social media data analysis, genomic, transcriptomic, and proteomic data analysis, and other COVID-19 research topics. We also found that there was a lack of heterogenous data integration in these AI applications. Discussion Risk factors relevant to COVID-19 outcomes exist in heterogeneous data sources, including electronic health records, surveillance systems, sociodemographic datasets, and many more. However, most AI applications in COVID-19 research adopted a single-sourced approach that could omit important risk factors and thus lead to biased algorithms. Integrating heterogeneous data for modeling will help realize the full potential of AI algorithms, improve precision, and reduce bias. Conclusion There is a lack of data integration in the AI applications in COVID-19 research and a need for a multilevel AI framework that supports the analysis of heterogeneous data from different sources.
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Affiliation(s)
- Yi Guo
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.,Cancer Informatics Shared Resource, University of Florida Health Cancer Center, Gainesville, Florida, USA
| | - Yahan Zhang
- Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, Florida, USA
| | - Tianchen Lyu
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.,Cancer Informatics Shared Resource, University of Florida Health Cancer Center, Gainesville, Florida, USA
| | - Mattia Prosperi
- Department of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA
| | - Hua Xu
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.,Cancer Informatics Shared Resource, University of Florida Health Cancer Center, Gainesville, Florida, USA
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29
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Syrowatka A, Kuznetsova M, Alsubai A, Beckman AL, Bain PA, Craig KJT, Hu J, Jackson GP, Rhee K, Bates DW. Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases. NPJ Digit Med 2021; 4:96. [PMID: 34112939 PMCID: PMC8192906 DOI: 10.1038/s41746-021-00459-8] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 04/26/2021] [Indexed: 02/05/2023] Open
Abstract
Artificial intelligence (AI) represents a valuable tool that could be widely used to inform clinical and public health decision-making to effectively manage the impacts of a pandemic. The objective of this scoping review was to identify the key use cases for involving AI for pandemic preparedness and response from the peer-reviewed, preprint, and grey literature. The data synthesis had two parts: an in-depth review of studies that leveraged machine learning (ML) techniques and a limited review of studies that applied traditional modeling approaches. ML applications from the in-depth review were categorized into use cases related to public health and clinical practice, and narratively synthesized. One hundred eighty-three articles met the inclusion criteria for the in-depth review. Six key use cases were identified: forecasting infectious disease dynamics and effects of interventions; surveillance and outbreak detection; real-time monitoring of adherence to public health recommendations; real-time detection of influenza-like illness; triage and timely diagnosis of infections; and prognosis of illness and response to treatment. Data sources and types of ML that were useful varied by use case. The search identified 1167 articles that reported on traditional modeling approaches, which highlighted additional areas where ML could be leveraged for improving the accuracy of estimations or projections. Important ML-based solutions have been developed in response to pandemics, and particularly for COVID-19 but few were optimized for practical application early in the pandemic. These findings can support policymakers, clinicians, and other stakeholders in prioritizing research and development to support operationalization of AI for future pandemics.
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Affiliation(s)
- Ania Syrowatka
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
| | | | - Ava Alsubai
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Adam L Beckman
- Harvard Medical School, Boston, MA, USA
- Harvard Business School, Boston, MA, USA
| | - Paul A Bain
- Countway Library of Medicine, Harvard Medical School, Boston, MA, USA
| | | | - Jianying Hu
- IBM Research, Center for Computational Health, Yorktown Heights, NY, USA
| | - Gretchen Purcell Jackson
- IBM Watson Health, Cambridge, MA, USA
- Department of Pediatric Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kyu Rhee
- IBM Watson Health, Cambridge, MA, USA
- CVS Health, Wellesley Hills, MA, USA
| | - David W Bates
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Harvard T. H. Chan School of Public Health, Boston, MA, USA
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Online Textual Symptomatic Assessment Chatbot Based on Q&A Weighted Scoring for Female Breast Cancer Prescreening. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11115079] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The increasing number of female breast cancer (FBC) incidences in the East predominated by Chinese language speakers has generated concerns over women’s medicare. To minimize the mortality rate associated with FBC in the region, governments and health experts are jointly encouraging women to undergo mammography screening at the earliest suspicion of FBC symptoms. However, studies show that a huge number of women affected by FBC tend to delay medical consultation at its early stage as a result of factors such as complacency due to unawareness of FBC symptoms, procrastination due to lifestyle, and the feeling of embarrassment in discussing private matters especially with medical personnel of the opposite gender. To address these issues, we propose a symptomatic assessment chatbot (SAC) based on artificial intelligence (AI) designed to prescreen women for FBC symptoms via a textual question-and-answer (Q&A) approach. The purpose of our chatbot is to assist women in engaging in communication regarding FBC symptoms, so as to subsequently initiate formal medical consultations for early FBC diagnosis and treatment. We implemented the SAC systematically with some of the latest natural language processing (NLP) techniques suitable for Chinese word segmentation (CWS) and trained the model with real-world FBC Q&A data obtained from a major hospital in Taiwan. The results from our experiments showed that the SAC achieved very high accuracy in FBC assessment scoring in comparison to FBC patients’ screening benchmark scores obtained from doctors.
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Abdulkareem M, Petersen SE. The Promise of AI in Detection, Diagnosis, and Epidemiology for Combating COVID-19: Beyond the Hype. Front Artif Intell 2021; 4:652669. [PMID: 34056579 PMCID: PMC8160471 DOI: 10.3389/frai.2021.652669] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 04/13/2021] [Indexed: 12/24/2022] Open
Abstract
COVID-19 has created enormous suffering, affecting lives, and causing deaths. The ease with which this type of coronavirus can spread has exposed weaknesses of many healthcare systems around the world. Since its emergence, many governments, research communities, commercial enterprises, and other institutions and stakeholders around the world have been fighting in various ways to curb the spread of the disease. Science and technology have helped in the implementation of policies of many governments that are directed toward mitigating the impacts of the pandemic and in diagnosing and providing care for the disease. Recent technological tools, artificial intelligence (AI) tools in particular, have also been explored to track the spread of the coronavirus, identify patients with high mortality risk and diagnose patients for the disease. In this paper, areas where AI techniques are being used in the detection, diagnosis and epidemiological predictions, forecasting and social control for combating COVID-19 are discussed, highlighting areas of successful applications and underscoring issues that need to be addressed to achieve significant progress in battling COVID-19 and future pandemics. Several AI systems have been developed for diagnosing COVID-19 using medical imaging modalities such as chest CT and X-ray images. These AI systems mainly differ in their choices of the algorithms for image segmentation, classification and disease diagnosis. Other AI-based systems have focused on predicting mortality rate, long-term patient hospitalization and patient outcomes for COVID-19. AI has huge potential in the battle against the COVID-19 pandemic but successful practical deployments of these AI-based tools have so far been limited due to challenges such as limited data accessibility, the need for external evaluation of AI models, the lack of awareness of AI experts of the regulatory landscape governing the deployment of AI tools in healthcare, the need for clinicians and other experts to work with AI experts in a multidisciplinary context and the need to address public concerns over data collection, privacy, and protection. Having a dedicated team with expertise in medical data collection, privacy, access and sharing, using federated learning whereby AI scientists hand over training algorithms to the healthcare institutions to train models locally, and taking full advantage of biomedical data stored in biobanks can alleviate some of problems posed by these challenges. Addressing these challenges will ultimately accelerate the translation of AI research into practical and useful solutions for combating pandemics.
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Affiliation(s)
- Musa Abdulkareem
- Barts Heart Centre, Barts Health National Health Service (NHS) Trust, London, United Kingdom
- National Institute for Health Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- Health Data Research UK, London, United Kingdom
| | - Steffen E. Petersen
- Barts Heart Centre, Barts Health National Health Service (NHS) Trust, London, United Kingdom
- National Institute for Health Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- Health Data Research UK, London, United Kingdom
- The Alan Turing Institute, London, United Kingdom
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Banerjee I, Robinson J, Kashyap A, Mohabeer P, Sathian B. COVID-19 and Artificial Intelligence: the pandemic pacifier. Nepal J Epidemiol 2020; 10:919-922. [PMID: 33495709 PMCID: PMC7812328 DOI: 10.3126/nje.v10i4.33334] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 12/15/2020] [Accepted: 12/22/2020] [Indexed: 12/13/2022] Open
Abstract
COVID-19 remains a threat to the entire world. In an attempt to curb its spread and facilitate its treatment, the technological tool that is Artificial Intelligence (AI) is being researched as a potential alternative to conventional methods. Industrial Revolution 4.0 marks the dawn to the combination of digital, physical and biological systems, by application of digital skills such as Blockchain, Internet of things, Artificial Intelligence and Big data. AI tools in SARS-COV-2 pandemic are highly competitive to human performance, such as rapid screening and diagnosis of the disease, surveilling the efficacy of the treatment, keeping record and depicting active cases and mortality, inventions of medications and vaccines, relieving the workload of healthcare workers and extinguishing the spread of the disease. Contact tracing platforms like Aarogya Setu App, implemented by the Government of India, Australian Government's COVID Safe app, Trace Together- a Bluetooth-based contact tracing app developed in Singapore; based on syndromic mapping/surveillance technology. Artificial intelligence will become a mainstay in both the diagnosis and treatment of COVID-19 as well as similar pandemics in future. The application and system development will be challenging; the accuracy and rapidity of its use far outweigh this drawback. The current global technological leaders have proven that the retro modification of current data systems and applications have been indispensable in the war on COVID-19, thus permanently securing their development and application in future.
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Affiliation(s)
| | - Jared Robinson
- Sir Seewoosagur Ramgoolam Medical College, Belle Rive, Mauritius
| | - Abhishek Kashyap
- Sir Seewoosagur Ramgoolam Medical College, Belle Rive, Mauritius
| | | | - Brijesh Sathian
- Geriatric and long term care Department, Rumailah Hospital, Hamad Medical Corporation, Doha, Qatar
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What Can COVID-19 Teach Us about Using AI in Pandemics? Healthcare (Basel) 2020; 8:healthcare8040527. [PMID: 33271960 PMCID: PMC7711608 DOI: 10.3390/healthcare8040527] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 11/23/2020] [Accepted: 11/24/2020] [Indexed: 12/17/2022] Open
Abstract
The COVID-19 pandemic put significant strain on societies and their resources, with the healthcare system and workers being particularly affected. Artificial Intelligence (AI) offers the unique possibility of improving the response to a pandemic as it emerges and evolves. Here, we utilize the WHO framework of a pandemic evolution to analyze the various AI applications. Specifically, we analyzed AI from the perspective of all five domains of the WHO pandemic response. To effectively review the current scattered literature, we organized a sample of relevant literature from various professional and popular resources. The article concludes with a consideration of AI’s weaknesses as key factors affecting AI in future pandemic preparedness and response.
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Golinelli D, Boetto E, Carullo G, Nuzzolese AG, Landini MP, Fantini MP. Adoption of Digital Technologies in Health Care During the COVID-19 Pandemic: Systematic Review of Early Scientific Literature. J Med Internet Res 2020; 22:e22280. [PMID: 33079693 PMCID: PMC7652596 DOI: 10.2196/22280] [Citation(s) in RCA: 193] [Impact Index Per Article: 48.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 07/25/2020] [Accepted: 09/15/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND The COVID-19 pandemic is favoring digital transitions in many industries and in society as a whole. Health care organizations have responded to the first phase of the pandemic by rapidly adopting digital solutions and advanced technology tools. OBJECTIVE The aim of this review is to describe the digital solutions that have been reported in the early scientific literature to mitigate the impact of COVID-19 on individuals and health systems. METHODS We conducted a systematic review of early COVID-19-related literature (from January 1 to April 30, 2020) by searching MEDLINE and medRxiv with appropriate terms to find relevant literature on the use of digital technologies in response to the pandemic. We extracted study characteristics such as the paper title, journal, and publication date, and we categorized the retrieved papers by the type of technology and patient needs addressed. We built a scoring rubric by cross-classifying the patient needs with the type of technology. We also extracted information and classified each technology reported by the selected articles according to health care system target, grade of innovation, and scalability to other geographical areas. RESULTS The search identified 269 articles, of which 124 full-text articles were assessed and included in the review after screening. Most of the selected articles addressed the use of digital technologies for diagnosis, surveillance, and prevention. We report that most of these digital solutions and innovative technologies have been proposed for the diagnosis of COVID-19. In particular, within the reviewed articles, we identified numerous suggestions on the use of artificial intelligence (AI)-powered tools for the diagnosis and screening of COVID-19. Digital technologies are also useful for prevention and surveillance measures, such as contact-tracing apps and monitoring of internet searches and social media usage. Fewer scientific contributions address the use of digital technologies for lifestyle empowerment or patient engagement. CONCLUSIONS In the field of diagnosis, digital solutions that integrate with traditional methods, such as AI-based diagnostic algorithms based both on imaging and clinical data, appear to be promising. For surveillance, digital apps have already proven their effectiveness; however, problems related to privacy and usability remain. For other patient needs, several solutions have been proposed, such as telemedicine or telehealth tools. These tools have long been available, but this historical moment may actually be favoring their definitive large-scale adoption. It is worth taking advantage of the impetus provided by the crisis; it is also important to keep track of the digital solutions currently being proposed to implement best practices and models of care in future and to adopt at least some of the solutions proposed in the scientific literature, especially in national health systems, which have proved to be particularly resistant to the digital transition in recent years.
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Affiliation(s)
- Davide Golinelli
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Erik Boetto
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Gherardo Carullo
- Department of Italian and Supranational Public Law, University of Milan, Milan, Italy
| | | | | | - Maria Pia Fantini
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
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Zheng Y, Zhu Y, Ji M, Wang R, Liu X, Zhang M, Liu J, Zhang X, Qin CH, Fang L, Ma S. A Learning-Based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics. PATTERNS (NEW YORK, N.Y.) 2020; 1:100092. [PMID: 32838344 PMCID: PMC7396968 DOI: 10.1016/j.patter.2020.100092] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 06/30/2020] [Accepted: 07/29/2020] [Indexed: 01/08/2023]
Abstract
The emergence of the novel coronavirus disease 2019 (COVID-19) is placing an increasing burden on healthcare systems. Although the majority of infected patients experience non-severe symptoms and can be managed at home, some individuals develop severe symptoms and require hospital admission. Therefore, it is critical to efficiently assess the severity of COVID-19 and identify hospitalization priority with precision. In this respect, a four-variable assessment model, including lymphocyte, lactate dehydrogenase, C-reactive protein, and neutrophil, is established and validated using the XGBoost algorithm. This model is found to be effective in identifying severe COVID-19 cases on admission, with a sensitivity of 84.6%, a specificity of 84.6%, and an accuracy of 100% to predict the disease progression toward rapid deterioration. It also suggests that a computation-derived formula of clinical measures is practically applicable for healthcare administrators to distribute hospitalization resources to the most needed in epidemics and pandemics.
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Affiliation(s)
- Yichao Zheng
- Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University, Shenzhen 518055, China
- Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen 518055, China
| | - Yinheng Zhu
- Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University, Shenzhen 518055, China
- Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen 518055, China
| | - Mengqi Ji
- Department of Automation, Tsinghua University, Beijing 100084, China
- Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen 518055, China
| | - Rongpin Wang
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang 550002, China
| | - Xinfeng Liu
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang 550002, China
| | - Mudan Zhang
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang 550002, China
| | - Jun Liu
- Department of Radiology, the Second Xiangya Hospital, Central South University, Changsha 410011, China
- Department of Radiology Quality Control Center, Changsha 410011, China
| | - Xiaochun Zhang
- Department of Radiology, Zhongnan Hospital, Wuhan University, Wuhan 43000, China
| | - Choo Hui Qin
- Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University, Shenzhen 518055, China
- Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen 518055, China
| | - Lu Fang
- Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University, Shenzhen 518055, China
- Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen 518055, China
| | - Shaohua Ma
- Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University, Shenzhen 518055, China
- Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen 518055, China
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