Multiple Disease Hotspot Mining for Public Health Informatics in Resource Starved Settings: Study of Communicable Diseases in Punjab, Pakistan

Khalique, Fatima; Khan, Shoab Ahmed

doi:10.1109/access.2021.3091655

Cited by 3 publications

(2 citation statements)

References 19 publications

(18 reference statements)

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“…Several studies have examined Media coverage to support policies [76]. Hotspot Mining makes it possible to identify locations with higher needs, determine the optimal allocation of resources, and devise effective intervention programs [77], leading to improved agenda setting. Mining news media to categorize policy interference-related posts [78] and understand public health concerns makes new kinds of information available to decide.…”

Section: Agenda Settingmentioning

confidence: 99%

The Application of Artificial Intelligence in Health Policy: A Scoping Review

Ramezani

Takian

Bakhtiari

et al. 2023

Preprint

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Background Policymakers require precise, in-time information to make informed decisions in complex environments such as health systems. Artificial intelligence (AI) is a novel approach that makes collecting and analyzing data in complex systems more accessible. This study highlights recent research on AI's application and capabilities in health policymaking. Method PubMed, Scopus, and the Web of Science databases were investigated to find relevant studies from 2000 to 2023 using the keywords of "artificial intelligence" and "policymaking." Walt and Gilson's policy triangle framework was used for charting the data. Results The results revealed that using AI in health policy paved the way for novel analyses and innovative solutions for intelligent decision-making and data collection, potentially enhancing policymaking capacities, particularly in the evaluation phase. Moreover, it can be used to develop creative agendas with fewer political limitations and higher rationality, leading to better policies. Furthermore, AI provides the opportunity to make evidence-informed decisions by developing new platforms and toolkits. Most of the suggested AI solutions for health policy are not meant to replace experts but to make decision-making smarter. Conclusion Numerous approaches exist for AI to influence the health policymaking process. Leading health systems can benefit from AI's potential to expand the use of evidence-based policymaking in health systems.

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Section: Agenda Settingmentioning

confidence: 99%

The Application of Artificial Intelligence in Health Policy: A Scoping Review

Ramezani

Takian

Bakhtiari

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…For example, in the past, there was Smallpox, then AIDS, then SARS, followed by swine flu, Ebola, and finally Covid-19. The epidemiological sequence in previous centuries suggests there may be subsequent epidemics, which is one of the biggest challenges for the present and the future [ 1 – 4 ].…”

Section: Introductionmentioning

confidence: 99%

A Large-Scale IoT-Based Scheme for Real-Time Prediction of Infectious Disease Symptoms

Said

2023

Mobile Netw Appl

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In recent decades, many infectious diseases have appeared that have negatively affected life in general and people in particular, causing many economic and human losses. Recently, many attempts have emerged to confront these diseases using computer-based technology for diagnosis, prediction, and data analysis using various techniques, the most important of which is deep learning. Previous research relied primarily on a set of images taken from the patient’s body while he was in a healthcare facility, and this is the main weakness of these studies. Not all people go to a doctor or hospital when they feel the symptoms of a disease. Hence, people moving in crowded places without knowing their health status can contribute to spreading infectious diseases quickly, and this is the issue that should be confronted. Therefore, this paper presents a people-monitoring scheme, which is based on the internet of things (IoT) technology, to predict infectious disease symptoms through people’s behavior as well as through a wireless body area network (WBAN). This scheme can predict the spread of disease by tracking the movements of infected persons. Additionally, a simple methodology for processing the data extracted from the monitoring process across a range of different computing centers is introduced. Moreover, to ensure the monitoring scheme operates in real-time, it was necessary to provide a powerful coverage model for its objects. Also, a simple COVID-19 case study is presented. Finally, the performance of the prediction model is measured using images, sounds and videos files. Furthermore, the performance of the data computing and coverage methodologies is measured using an intensive simulation environment for the IoT that was constructed using NS3 package. The results showed that the proposed scheme is able to predict the symptoms of disease and its spread with accepted level of accuracy. In addition, using a mixture of coverage tools and computing techniques is recommended.

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The application of artificial intelligence in health policy: a scoping review

Ramezani,

Takian,

Bakhtiari

et al. 2023

BMC Health Serv Res

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Background Policymakers require precise and in-time information to make informed decisions in complex environments such as health systems. Artificial intelligence (AI) is a novel approach that makes collecting and analyzing data in complex systems more accessible. This study highlights recent research on AI’s application and capabilities in health policymaking. Methods We searched PubMed, Scopus, and the Web of Science databases to find relevant studies from 2000 to 2023, using the keywords “artificial intelligence” and “policymaking.” We used Walt and Gilson’s policy triangle framework for charting the data. Results The results revealed that using AI in health policy paved the way for novel analyses and innovative solutions for intelligent decision-making and data collection, potentially enhancing policymaking capacities, particularly in the evaluation phase. It can also be employed to create innovative agendas with fewer political constraints and greater rationality, resulting in evidence-based policies. By creating new platforms and toolkits, AI also offers the chance to make judgments based on solid facts. The majority of the proposed AI solutions for health policy aim to improve decision-making rather than replace experts. Conclusion Numerous approaches exist for AI to influence the health policymaking process. Health systems can benefit from AI’s potential to foster the meaningful use of evidence-based policymaking.

show abstract

Multiple Disease Hotspot Mining for Public Health Informatics in Resource Starved Settings: Study of Communicable Diseases in Punjab, Pakistan

Cited by 3 publications

References 19 publications

The Application of Artificial Intelligence in Health Policy: A Scoping Review

The Application of Artificial Intelligence in Health Policy: A Scoping Review

A Large-Scale IoT-Based Scheme for Real-Time Prediction of Infectious Disease Symptoms

The application of artificial intelligence in health policy: a scoping review

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