Health Informatics: The Foundations of Public Health

Lee, Tian–Shyug; Lu, Chi-Jie

doi:10.3390/healthcare11060798

Cited by 3 publications

(3 citation statements)

References 22 publications

(29 reference statements)

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“…Conversely, other areas, such as predicting postoperative complications in perioperative medicine, have not yet produced the desired results. Although many predictive models have been published, most are still in the research stage, and a valid and universally applicable intelligent tool for clinical practice has yet to be developed [ 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ].…”

Section: Clinical Practice and Research Perspectivesmentioning

confidence: 99%

The Clinical Researcher Journey in the Artificial Intelligence Era: The PAC-MAN’s Challenge

et al. 2023

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Artificial intelligence (AI) is a powerful tool that can assist researchers and clinicians in various settings. However, like any technology, it must be used with caution and awareness as there are numerous potential pitfalls. To provide a creative analogy, we have likened research to the PAC-MAN classic arcade video game. Just as the protagonist of the game is constantly seeking data, researchers are constantly seeking information that must be acquired and managed within the constraints of the research rules. In our analogy, the obstacles that researchers face are represented by “ghosts”, which symbolize major ethical concerns, low-quality data, legal issues, and educational challenges. In short, clinical researchers need to meticulously collect and analyze data from various sources, often navigating through intricate and nuanced challenges to ensure that the data they obtain are both precise and pertinent to their research inquiry. Reflecting on this analogy can foster a deeper comprehension of the significance of employing AI and other powerful technologies with heightened awareness and attentiveness.

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Section: Clinical Practice and Research Perspectivesmentioning

confidence: 99%

The Clinical Researcher Journey in the Artificial Intelligence Era: The PAC-MAN’s Challenge

et al. 2023

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“…Ideally, rigorous upstream thinking to strengthen HIS governance should be undertaken by defining and proposing a coherent conceptual framework to analyze and guide the development and integration of digital applications into HIS over the long term. Health informatics is the field of study that deals with the application of information technology (IT) to healthcare (Lee and Lu, 2023). It encompasses a wide range of activities, including the development and implementation of electronic health records (EHRs), the use of data analytics to improve healthcare quality and outcomes, and the development of mobile health (mHealth) applications to reach underserved populations (Bellazzi et al, 2023).…”

Section: Current State Of Health Informatics In Africamentioning

confidence: 99%

Integrating Ai Into Health Informatics for Enhanced Public Health in Africa: A Comprehensive Review

Obe Destiny Balogun,

Oluwatoyin Ayo-Farai,

Oluwatosin Ogundairo

et al. 2023

imsrj

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This study delves into the integration of Artificial Intelligence (AI) within the field of health informatics and its transformative effect on public health outcomes in Africa. It will cover how AI-driven solutions are being implemented to overcome challenges in disease surveillance, healthcare delivery, and public health policy. The paper aims to provide an in-depth analysis of current innovations, the effectiveness of these technological interventions, and their broader implications for health policy and management across the African continent. The integration of Artificial Intelligence (AI) into health informatics holds transformative potential for enhancing public health in Africa. This comprehensive review explores the multifaceted applications, challenges, and opportunities associated with the convergence of AI and health informatics on the African continent. The review encompasses various domains, including disease surveillance, diagnostics, treatment optimization, and public health management. Key themes addressed in the review include the adoption of AI-driven technologies in healthcare, the impact on disease detection and monitoring, and the potential for improving healthcare accessibility in resource-constrained settings. Moreover, the ethical considerations, regulatory challenges, and disparities in technology adoption across diverse African regions are examined, providing insights into the complexities of implementing AI in the African public health landscape. Through an in-depth analysis of current initiatives, case studies, and emerging trends, this review aims to contribute a comprehensive understanding of the opportunities and challenges associated with integrating AI into health informatics for the advancement of public health in Africa. Ultimately, this exploration seeks to inform policymakers, healthcare professionals, and researchers on the critical role AI can play in addressing public health challenges on the continent and fostering sustainable healthcare solutions. Keywords: Artificial Intelligence, Health Informatics, Health Management, Africa, Review, Disease Surveillance.

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“…4,5 These techniques are widely applied in healthcare and medical informatics, demonstrating their potential for improving diagnostics, treatment, and patient outcomes. [6][7][8] Despite the extensive use of ML in various medical domains, there is a notable lack of research using ML methods to specifically explore sperm count-related problems. 9,10 In a previous study of sperm count, we used five predictive ML algorithms, namely random forest, stochastic gradient boosting, least absolute shrinkage and selection operator regression, ridge regression, and extreme gradient boosting.…”

Section: Introductionmentioning

confidence: 99%

Using a Decision Tree Algorithm Predictive Model for Sperm Count Assessment and Risk Factors in Health Screening Population

Huang,

Lu,

Jhou

et al. 2023

RMHP

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Approximately 20% of couples face infertility challenges and struggle to conceive naturally. Despite advances in artificial reproduction, its success hinges on sperm quality. Our previous study used five machine learning (ML) algorithms, random forest, stochastic gradient boosting, least absolute shrinkage and selection operator regression, ridge regression, and extreme gradient boosting, to model health data from 1375 Taiwanese males and identified ten risk factors affecting sperm count. Methods: We employed the CART algorithm to generate decision trees using identified risk factors to predict healthy sperm counts. Four error metrics, SMAPE, RAE, RRSE, and RMSE, were used to evaluate the decision trees. We identified the top five decision trees based on their low errors and discussed in detail the tree with the least error. Results:The decision tree featuring the least error, comprising BMI, UA, ST, T-Cho/HDL-C ratio, and BUN, corroborated the negative impacts of metabolic syndrome, particularly high BMI, on sperm count, while emphasizing the link between good sleep and male fertility. Our study also sheds light on the potentially significant influence of high BUN on spermatogenesis. Two novel risk factors, T-Cho/HDL-C and UA, warrant further investigation. Conclusion:The ML algorithm established a predictive model for healthcare personnel to assess low sperm counts. Refinement of the model using additional data is crucial for improved precision. The risk factors identified offer avenues for future investigations.

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Health Informatics: The Foundations of Public Health

Abstract: As technology continues to evolve, vast amounts of diverse digital data are becoming more easily generated and collected [...]

Cited by 3 publications

References 22 publications

The Clinical Researcher Journey in the Artificial Intelligence Era: The PAC-MAN’s Challenge

The Clinical Researcher Journey in the Artificial Intelligence Era: The PAC-MAN’s Challenge

Integrating Ai Into Health Informatics for Enhanced Public Health in Africa: A Comprehensive Review

Using a Decision Tree Algorithm Predictive Model for Sperm Count Assessment and Risk Factors in Health Screening Population

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