Advances in Disease Prediction

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 1769

Special Issue Editors


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Guest Editor
School of Medicine, Istanbul Medipol University, Kavacık Mah. Ekinciler Cad. No.19, Kavacık Kavşağı, Beykoz, 34810 Istanbul, Turkey
Interests: epidemiology; health care management; social medicine; public health; healthcare policies; social determinants of health

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Guest Editor
Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand
Interests: clinical epidemiology; chemotherapy and targeted therapy; surgery; gynecologic Oncology

Special Issue Information

Dear Colleagues,

In the era of digital health, complex and ubiquitous health data are collected from various sources, such as electronic health records, medical monitoring devices, wearable health systems, and mobile phone applications. Big data analytics techniques such as statistical analysis, machine learning, deep learning, generative intelligence, and digital twins can be applied to build innovative advances in disease prediction. Recently, the need for automated/intelligent laboratory recommendation systems has increased to provide more accurate and faster diagnoses. The fusion of various clinical data sources promoted by advances in laboratory approaches can significantly improve the diagnosis of diseases, illustrating how the digital revolution transforms clinical diagnostic practice. Based on concrete evidence, it is crucial and has a significant impact on the implementation of health care and programs. This fact highlights the important role of early diagnostics, including disease control, a range of treatment options, improved health services, improved health disparities and quality of life, etc. The aim of this Special Issue is to provide a comprehensive and current collection of state-of-the-art studies to advance disease prediction. Practical experience and experiments on the above-mentioned innovative analysis issues are also welcomed.

Prof. Dr. Chi-Chang Chang
Prof. Dr. Osman Hayran
Dr. Chalong Cheewakriangkrai
Guest Editors

Manuscript Submission Information

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Keywords

  • early diagnostics
  • medical big data analytics
  • intelligent diagnostic models in public health
  • survival analysis and health hazard evaluations
  • machine learning and generative intelligence
  • intelligent digital twins
  • automated/intelligent laboratory recommendation systems

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Published Papers (1 paper)

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16 pages, 1127 KiB  
Systematic Review
Prediction Models for Perioperative Blood Transfusion in Patients Undergoing Gynecologic Surgery: A Systematic Review
by Zhongmian Pan and Kittipat Charoenkwan
Diagnostics 2024, 14(18), 2018; https://doi.org/10.3390/diagnostics14182018 - 12 Sep 2024
Viewed by 549
Abstract
This systematic review aimed to evaluate prediction models for perioperative blood transfusion in patients undergoing gynecologic surgery. Given the inherent risks associated with blood transfusion and the critical need for accurate prediction, this study identified and assessed models based on their development, validation, [...] Read more.
This systematic review aimed to evaluate prediction models for perioperative blood transfusion in patients undergoing gynecologic surgery. Given the inherent risks associated with blood transfusion and the critical need for accurate prediction, this study identified and assessed models based on their development, validation, and predictive performance. The review included five studies encompassing various surgical procedures and approaches. Predicting factors commonly used across these models included preoperative hematocrit, race, surgical route, and uterine fibroid characteristics. However, the review highlighted significant variability in the definition of perioperative periods, a lack of standardization in transfusion criteria, and a high risk of bias in most models due to methodological issues, such as a low number of events per variable, inappropriate handling of continuous and categorical predictors, inappropriate handling of missing data, improper methods of predictor selection, inappropriate measurement methods for model performance, and inadequate evaluations of model overfitting and optimism in model performance. Despite some models demonstrating good discrimination and calibration, the overall quality and external validation of these models were limited. Consequently, there is a clear need for more robust and externally validated models to improve clinical decision-making and patient outcomes in gynecologic surgery. Future research should focus on refining these models, incorporating rigorous validation, and adhering to standardized reporting practices. Full article
(This article belongs to the Special Issue Advances in Disease Prediction)
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