Exploring the Potential of Artificial Intelligence and Machine Learning to Combat COVID-19 and Existing Opportunities for LMIC: A Scoping Review

Naseem, Maleeha; Akhund, Ramsha; Arshad, Hajra; Ibrahim, Muhammad Talal

doi:10.1177/2150132720963634

Cited by 83 publications

(54 citation statements)

References 28 publications

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“…In this regard, multiple research projects have been published using machine/deep learning techniques for the early detection of the COVID-19 virus [ 22 , 23 , 24 , 25 ], models applied to patients admitted to ICUs, which have been the clinical units most affected by the virus [ 26 , 27 , 28 ], and machine/deep learning applied in “omic” technologies to predict complications of COVID-19 [ 29 , 30 ]. The number of published papers about COVID-19 is continuously growing [ 31 , 32 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Mortality Analysis in Patients with COVID-19

Sánchez-Montañés

Rodríguez-Belenguer

Serrano-López

et al. 2020

IJERPH

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This paper analyzes a sample of patients hospitalized with COVID-19 in the region of Madrid (Spain). Survival analysis, logistic regression, and machine learning techniques (both supervised and unsupervised) are applied to carry out the analysis where the endpoint variable is the reason for hospital discharge (home or deceased). The different methods applied show the importance of variables such as age, O2 saturation at Emergency Rooms (ER), and whether the patient comes from a nursing home. In addition, biclustering is used to globally analyze the patient-drug dataset, extracting segments of patients. We highlight the validity of the classifiers developed to predict the mortality, reaching an appreciable accuracy. Finally, interpretable decision rules for estimating the risk of mortality of patients can be obtained from the decision tree, which can be crucial in the prioritization of medical care and resources.

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Section: Introductionmentioning

confidence: 99%

Machine Learning for Mortality Analysis in Patients with COVID-19

Sánchez-Montañés

Rodríguez-Belenguer

Serrano-López

et al. 2020

IJERPH

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“…AI, and more specifically machine learning models, are increasingly used in disease surveillance and can maximise the impact of limited available resources. 14 Integration of such models with accurate digital epidemiological data was shown to provide reliable, near real-time estimations of influenza disease incidence. 15 The widespread distribution of smartphones coupled with the development of AI-enabled sound classification models now represent another new potential breakthrough in public health and epidemic preparedness.…”

Section: Discussionmentioning

confidence: 99%

Digital acoustic surveillance for early detection of respiratory disease outbreaks in Spain: a protocol for an observational study

Gabaldón-Figueira

Brew²,

Doré

et al. 2021

BMJ Open

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IntroductionCough is a common symptom of COVID-19 and other respiratory illnesses. However, objectively measuring its frequency and evolution is hindered by the lack of reliable and scalable monitoring systems. This can be overcome by newly developed artificial intelligence models that exploit the portability of smartphones. In the context of the ongoing COVID-19 pandemic, cough detection for respiratory disease syndromic surveillance represents a simple means for early outbreak detection and disease surveillance. In this protocol, we evaluate the ability of population-based digital cough surveillance to predict the incidence of respiratory diseases at population level in Navarra, Spain, while assessing individual determinants of uptake of these platforms.Methods and analysisParticipants in the Cendea de Cizur, Zizur Mayor or attending the local University of Navarra (Pamplona) will be invited to monitor their night-time cough using the smartphone app Hyfe Cough Tracker. Detected coughs will be aggregated in time and space. Incidence of COVID-19 and other diagnosed respiratory diseases within the participants cohort, and the study area and population will be collected from local health facilities and used to carry out an autoregressive moving average analysis on those independent time series. In a mixed-methods design, we will explore barriers and facilitators of continuous digital cough monitoring by evaluating participation patterns and sociodemographic characteristics. Participants will fill an acceptability questionnaire and a subgroup will participate in focus group discussions.Ethics and disseminationEthics approval was obtained from the ethics committee of the Centre Hospitalier de l’Université de Montréal, Canada and the Medical Research Ethics Committee of Navarre, Spain. Preliminary findings will be shared with civil and health authorities and reported to individual participants. Results will be submitted for publication in peer-reviewed scientific journals and international conferences.Trial registration numberNCT04762693.

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“…The latter also discusses how other technologies such as AI, IoT, and unmanned aerial vehicles (UAVs) can facilitate better management of the pandemic through improving, among other things, diagnosis, surveillance, and treatment capacities [2]. Focusing on the applications in the health sector, Naseem et al [13] also found that AI-enabled solutions are effective in timely and accurate identification and monitoring and tracing of COVID-19 cases. While these reviews have been successful in highlighting the utilities of smart solutions, they are mainly sector-based (i.e., the health sector).…”

Section: Introductionmentioning

confidence: 99%

Contributions of Smart City Solutions and Technologies to Resilience against the COVID-19 Pandemic: A Literature Review

2021

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Since its emergence in late 2019, the COVID-19 pandemic has swept through many cities around the world, claiming millions of lives and causing major socio-economic impacts. The pandemic occurred at an important historical juncture when smart solutions and technologies have become ubiquitous in many cities. Against this background, in this review, we examine how smart city solutions and technologies have contributed to resilience by enhancing planning, absorption, recovery, and adaptation abilities. For this purpose, we reviewed 147 studies that have discussed issues related to the use of smart solutions and technologies during the pandemic. The results were synthesized under four themes, namely, planning and preparation, absorption, recovery, and adaptation. This review shows that investment in smart city initiatives can enhance the planning and preparation ability. In addition, the adoption of smart solutions and technologies can, among other things, enhance the capacity of cities to predict pandemic patterns, facilitate an integrated and timely response, minimize or postpone transmission of the virus, provide support to overstretched sectors, minimize supply chain disruption, ensure continuity of basic services, and offer solutions for optimizing city operations. These are promising results that demonstrate the utility of smart solutions for enhancing resilience. However, it should be noted that realizing this potential hinges on careful attention to important issues and challenges related to privacy and security, access to open-source data, technological affordance, legal barriers, technological feasibility, and citizen engagement. Despite this, this review shows that further development of smart city initiatives can provide unprecedented opportunities for enhancing resilience to the pandemic and similar future events.

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Exploring the Potential of Artificial Intelligence and Machine Learning to Combat COVID-19 and Existing Opportunities for LMIC: A Scoping Review

Cited by 83 publications

References 28 publications

Machine Learning for Mortality Analysis in Patients with COVID-19

Machine Learning for Mortality Analysis in Patients with COVID-19

Digital acoustic surveillance for early detection of respiratory disease outbreaks in Spain: a protocol for an observational study

Contributions of Smart City Solutions and Technologies to Resilience against the COVID-19 Pandemic: A Literature Review

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