Zobrazeno 1 - 10
of 329
pro vyhledávání: '"May D. Wang"'
Autor:
Monica Isgut, Felipe Giuste, Logan Gloster, Aniketh Swain, Katherine Choi, Andrew Hornback, Shriprasad R. Deshpande, May D. Wang
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract Polygenic risk scores (PRSs) hold promise in their potential translation into clinical settings to improve disease risk prediction. An important consideration in integrating PRSs into clinical settings is to gain an understanding of how to i
Externí odkaz:
https://doaj.org/article/f4bfbc2d69ef4750b425fbca41c7a9eb
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 24, Iss 1, Pp 1-15 (2024)
Abstract Background Learning policies for decision-making, such as recommending treatments in clinical settings, is important for enhancing clinical decision-support systems. However, the challenge lies in accurately evaluating and optimizing these p
Externí odkaz:
https://doaj.org/article/6817526e924b44cea5f537f7c1e1ab2f
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 24, Iss 1, Pp 1-15 (2024)
Abstract Background Modeling causality through graphs, referred to as causal graph learning, offers an appropriate description of the dynamics of causality. The majority of current machine learning models in clinical decision support systems only pre
Externí odkaz:
https://doaj.org/article/f90c78138b4e4ccb83187efd81455c80
Publikováno v:
IEEE Open Journal of Engineering in Medicine and Biology, Vol 5, Pp 816-827 (2024)
Objective: To develop a clinical decision support tool that can predict cardiovascular disease (CVD) risk with high accuracy while requiring minimal clinical feature input, thus reducing the time and effort required by clinicians to manually enter da
Externí odkaz:
https://doaj.org/article/8f91f42a2e5643b6aeb77437105a8956
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-18 (2023)
Abstract Recent advances in artificial intelligence (AI) have sparked interest in developing explainable AI (XAI) methods for clinical decision support systems, especially in translational research. Although using XAI methods may enhance trust in bla
Externí odkaz:
https://doaj.org/article/a7742a04c76e4766aa2c523f17b56815
Autor:
Felipe O. Giuste, Lawrence He, Peter Lais, Wenqi Shi, Yuanda Zhu, Andrew Hornback, Chiche Tsai, Monica Isgut, Blake Anderson, May D. Wang
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-13 (2023)
Abstract Personalized medicine plays an important role in treatment optimization for COVID-19 patient management. Early treatment in patients at high risk of severe complications is vital to prevent death and ventilator use. Predicting COVID-19 clini
Externí odkaz:
https://doaj.org/article/5c53904e65b24691966af229d078525b
Autor:
Charles A. Ellis, Mohammad S. E. Sendi, Rongen Zhang, Darwin A. Carbajal, May D. Wang, Robyn L. Miller, Vince D. Calhoun
Publikováno v:
Frontiers in Neuroinformatics, Vol 17 (2023)
IntroductionMultimodal classification is increasingly common in electrophysiology studies. Many studies use deep learning classifiers with raw time-series data, which makes explainability difficult, and has resulted in relatively few studies applying
Externí odkaz:
https://doaj.org/article/bb154bf172e24d809c656847873c5452
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
Abstract Most current Alzheimer’s disease (AD) and mild cognitive disorders (MCI) studies use single data modality to make predictions such as AD stages. The fusion of multiple data modalities can provide a holistic view of AD staging analysis. Thu
Externí odkaz:
https://doaj.org/article/0a0b6b02dd8e4f618f39fd0e5c768139
Publikováno v:
Frontiers in Artificial Intelligence, Vol 5 (2022)
More than 5 million patients have admitted annually to intensive care units (ICUs) in the United States. The leading causes of mortality are cardiovascular failures, multi-organ failures, and sepsis. Data-driven techniques have been used in the analy
Externí odkaz:
https://doaj.org/article/bbe1c19df2f14225a668c1246a596f8f
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 20, Iss 1, Pp 1-12 (2020)
Abstract Background Breast cancer is the most prevalent and among the most deadly cancers in females. Patients with breast cancer have highly variable survival lengths, indicating a need to identify prognostic biomarkers for personalized diagnosis an
Externí odkaz:
https://doaj.org/article/727c8ed9652748ba84a2dbe71d071329