Statistical methods for constructing gestational age-related charts for fetal size and pregnancy dating using longitudinal data

Hynek, M.; Kalina, Jan; Zvárová, Jana; Long, Jeffrey D.

doi:10.1016/j.bbe.2018.09.001

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…A wide range of statistical methods has been implemented to construct GA-related charts for pregnancy dating, fetal size, and fetal weight [35], mainly using a parametric approach from cross-sectional to longitudinal data. The aim of this study, however, is to create a fetal growth chart for use in Indonesia to make up for the absence of fetal growth charts in settings with limited resources.…”

Section: The Development Of An Alternative Growth Chart For Estimatedmentioning

confidence: 99%

The development of an alternative growth chart for estimated fetal weight in the absence of ultrasound: Application in Indonesia

2020

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Section: The Development Of An Alternative Growth Chart For Estimatedmentioning

confidence: 99%

The development of an alternative growth chart for estimated fetal weight in the absence of ultrasound: Application in Indonesia

2020

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On Analyzing Complex Data Within Clinical Decision Support Systems

Kalina

2022

Advances in Medical Technologies and Clinical Practice

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Clinical decision support systems (CDSSs) represent digital health tools applicable to important tasks within the clinical decision-making process. Training data-driven CDSSs requires extracting medical knowledge from the available information by means of machine learning. The analysis of the complex (possibly big or high-dimensional) training data allows knowledge relevant to be obtained for clinical decisions related to the diagnosis, therapy, or prognosis. This chapter is devoted to training CDSSs by machine learning based on complex data. Remarkable recent examples of CDSSs including those based on deep learning are recalled here. Principles, challenges, or ethical aspects of machine learning are discussed here in the context of CDSSs. Attention is paid to dimensionality reduction, deep learning methods for big data, or explainability of the data analysis methods. Data analysis issues are discussed also for two particular CDSSs on which the author of this chapter participated.

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Ethical Aspects of Information-Based Medicine (With a Focus on Mental Health)

Kalina¹,

Tobišková²

2022

Ethical Implications of Reshaping Healthcare With Emerging Technologies

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Modern advanced tools of artificial intelligence assist healthcare professionals in a variety of complex tasks related to medical care. Digitalization and automation accelerate the development and deployment of innovative artificial intelligence tools tailor-made for clinical applications; decision support systems represent an important example of such tools. This chapter is devoted to discussing the role of artificial intelligence in current and prospective medical care and ethical aspects of their deployment. As developed countries currently go through remarkable transforms of medical care involving informatization and centering around medical information and knowledge, this chapter also discusses future ideals of medical care characterized by the concept of information-based medicine. Ethical and patient safety implications of using artificial intelligence within information-based medicine are overviewed here. Specific attention is paid to mental healthcare and ethical aspects of using artificial intelligence in its context.

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Statistical methods for constructing gestational age-related charts for fetal size and pregnancy dating using longitudinal data

Cited by 3 publications

References 32 publications

The development of an alternative growth chart for estimated fetal weight in the absence of ultrasound: Application in Indonesia

The development of an alternative growth chart for estimated fetal weight in the absence of ultrasound: Application in Indonesia

On Analyzing Complex Data Within Clinical Decision Support Systems

Ethical Aspects of Information-Based Medicine (With a Focus on Mental Health)

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