Use of Artificial Intelligence for Predicting COVID-19 Outcomes: A Scoping Review

Lyu, Jinyan; Cui, Wanting; Finkelstein, Joseph

doi:10.3233/shti210923

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Considering that none of the available reviews on the use of AI in the predictive modeling of COVID-19-related outcomes have performed a bias/applicability assessment of AI models used to predict such outcomes (7)(8)(9)(10)(11)(12)(13)(14) (Table 1), we conducted a systematic screening aiming at filling this knowledge gap. According to the definitions of the tool used for assessing the risk of bias in this systematic review (i.e., Bias analysis using the Prediction model Risk Of Bias ASsessment Tool, PROBAST), bias was defined to occur when shortcomings in the study design, conduct, or analysis lead to systematically distorted estimates of model predictive performance.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review

Shakibfar

Nyberg

et al. 2023

Front. Public Health

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AimTo perform a systematic review on the use of Artificial Intelligence (AI) techniques for predicting COVID-19 hospitalization and mortality using primary and secondary data sources.Study eligibility criteriaCohort, clinical trials, meta-analyses, and observational studies investigating COVID-19 hospitalization or mortality using artificial intelligence techniques were eligible. Articles without a full text available in the English language were excluded.Data sourcesArticles recorded in Ovid MEDLINE from 01/01/2019 to 22/08/2022 were screened.Data extractionWe extracted information on data sources, AI models, and epidemiological aspects of retrieved studies.Bias assessmentA bias assessment of AI models was done using PROBAST.ParticipantsPatients tested positive for COVID-19.ResultsWe included 39 studies related to AI-based prediction of hospitalization and death related to COVID-19. The articles were published in the period 2019-2022, and mostly used Random Forest as the model with the best performance. AI models were trained using cohorts of individuals sampled from populations of European and non-European countries, mostly with cohort sample size <5,000. Data collection generally included information on demographics, clinical records, laboratory results, and pharmacological treatments (i.e., high-dimensional datasets). In most studies, the models were internally validated with cross-validation, but the majority of studies lacked external validation and calibration. Covariates were not prioritized using ensemble approaches in most of the studies, however, models still showed moderately good performances with Area under the Receiver operating characteristic Curve (AUC) values >0.7. According to the assessment with PROBAST, all models had a high risk of bias and/or concern regarding applicability.ConclusionsA broad range of AI techniques have been used to predict COVID-19 hospitalization and mortality. The studies reported good prediction performance of AI models, however, high risk of bias and/or concern regarding applicability were detected.

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Section: Introductionmentioning

confidence: 99%

Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review

Shakibfar

Nyberg

et al. 2023

Front. Public Health

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“…Advances in telerehabilitation technology has brought about a possibility of a remotely supervised rehabilitation program through real-time communication, tracking of physical activities of a patient, monitoring vital body functions, and rehab physical therapy [1][2][3]. Telemedicine use has grown significantly during the COVID-19 pandemic [4] when the World Health Organization encouraged physical distancing [5]. Telehealth approaches may be instrumental for supporting home-based cycling exercises in people with chronic health conditions especially when safety and adherence with exercise prescription can be monitored in real time [6].…”

Section: Introductionmentioning

confidence: 99%

Telemonitoring of Home-Based Biking Exercise: Assessment of Wireless Interfaces

Smiley¹,

Tsai²,

Cui³

et al. 2022

JMIR Biomed Eng

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Background Telerehabiliation has been shown to have great potential in expanding access to rehabilitation services, enhancing patients’ quality of life, and improving clinical outcomes. Stationary biking exercise can serve as an effective aerobic component of home-based physical rehabilitation programs. Remote monitoring of biking exercise provides necessary safeguards to ensure exercise adherence and safety in patients' homes. The scalability of the current remote monitoring of biking exercise solutions is impeded by the high cost that limits patient access to these services, especially among older adults with chronic health conditions. Objective The aim of this project was to design and test two low-cost wireless interfaces for the telemonitoring of home-based biking exercise. Methods We designed an interactive biking system (iBikE) that comprises a tablet PC and a low-cost bike. Two wireless interfaces to monitor the revolutions per minute (RPM) were built and tested. The first version of the iBikE system uses Bluetooth Low Energy (BLE) to send information from the iBikE to the PC tablet, and the second version uses a Wi-Fi network for communication. Both systems provide patients and their clinical teams the capability to monitor exercise progress in real time using a simple graphical representation. The bike can be used for upper or lower limb rehabilitation. We developed two tablet applications with the same graphical user interfaces between the application and the bike sensors but with different communication protocols (BLE and Wi-Fi). For testing purposes, healthy adults were asked to use an arm bike for three separate subsessions (1 minute each at a slow, medium, and fast pace) with a 1-minute resting gap. While collecting speed values from the iBikE application, we used a tachometer to continuously measure the speed of the bikes during each subsession. Collected data were later used to assess the accuracy of the measured data from the iBikE system. Results Collected RPM data in each subsession (slow, medium, and fast) from the iBikE and tachometer were further divided into 4 categories, including RPM in every 10-second bin (6 bins), RPM in every 20-second bin (3 bins), RPM in every 30-second bin (2 bins), and RPM in each 1-minute subsession (60 seconds, 1 bin). For each bin, the mean difference (iBikE and tachometer) was then calculated and averaged for all bins in each subsession. We saw a decreasing trend in the mean RPM difference from the 10-second to the 1-minute measurement. For the 10-second measurements during the slow and fast cycling, the mean discrepancy between the wireless interface and tachometer was 0.67 (SD 0.24) and 1.22 (SD 0.67) for the BLE iBike, and 0.66 (SD 0.48) and 0.87 (SD 0.91) for the Wi-Fi iBike system, respectively. For the 1-minute measurements during the slow and fast cycling, the mean discrepancy between the wireless interface and tachometer was 0.32 (SD 0.26) and 0.66 (SD 0.83) for the BLE iBike, and 0.21 (SD 0.21) and 0.47 (SD 0.52) for the Wi-Fi iBike system, respectively. Conclusions We concluded that a low-cost wireless interface provides the necessary accuracy for the telemonitoring of home-based biking exercise.

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Artificial Intelligence in Medicine and Dentistry

Vodanović¹,

Subašić²,

Milošević³

et al. 2023

Acta Stomatol Croat

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Introduction Artificial intelligence has been applied in various fields throughout history, but its integration into daily life is more recent. The first applications of AI were primarily in academia and government research institutions, but as technology has advanced, AI has also been applied in industry, commerce, medicine and dentistry. Objective Considering that the possibilities of applying artificial intelligence are developing rapidly and that this field is one of the areas with the greatest increase in the number of newly published articles, the aim of this paper was to provide an overview of the literature and to give an insight into the possibilities of applying artificial intelligence in medicine and dentistry. In addition, the aim was to discuss its advantages and disadvantages. Conclusion The possibilities of applying artificial intelligence to medicine and dentistry are just being discovered. Artificial intelligence will greatly contribute to developments in medicine and dentistry, as it is a tool that enables development and progress, especially in terms of personalized healthcare that will lead to much better treatment outcomes.

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Use of Artificial Intelligence for Predicting COVID-19 Outcomes: A Scoping Review

Cited by 3 publications

References 17 publications

Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review

Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review

Telemonitoring of Home-Based Biking Exercise: Assessment of Wireless Interfaces

Artificial Intelligence in Medicine and Dentistry

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