Health Informatics: The Foundations of Public Health

Lee, Tian–Shyug; Lu, Chi-Jie

doi:10.3390/healthcare11060798

Cited by 2 publications

(1 citation statement)

References 22 publications

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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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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 2 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

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

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