Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Keyword
-
- Archives (1)
- Archivists (1)
- Bias (1)
- Bioinformatics (1)
- Critical Appraisal (1)
-
- Decision Making (1)
- Evidence Based Practice (1)
- Health Sciences Librarians (1)
- Health Sciences Libraries (1)
- Healthcare informatics (1)
- Healthcare. (1)
- Informaticists (1)
- Informatics (1)
- Machine learning (1)
- Medical Librarians (1)
- Noisy labels learning (1)
- PU learning (1)
- PULSNAR (1)
- Positive and unlabeled learning (1)
- Question Formulation (1)
- SCAR (1)
- SNAR (1)
- Soft Set (1)
- Soft Sets Extensions (1)
- Publication
- Publication Type
Articles 1 - 3 of 3
Full-Text Articles in Biomedical Informatics
Evidence Based Practice: A Decision-Making Guide For Health Information Professionals, Jonathan Eldredge
Evidence Based Practice: A Decision-Making Guide For Health Information Professionals, Jonathan Eldredge
Faculty Book Display Case
This Guide introduces Evidence Based Practice to newcomers as well as serves as a resource for experienced practitioners. It focuses on health information professionals (informaticists, health sciences librarians, informationists, information scientists, data managers, archivists, etc.) within the US context, although others outside of the US health context might find elements of it to be valuable.
Soft Sets Extensions Used In Bioinformatics, Florentin Smarandache, Daniela Gifu
Soft Sets Extensions Used In Bioinformatics, Florentin Smarandache, Daniela Gifu
Branch Mathematics and Statistics Faculty and Staff Publications
This comprehensive review delves into the intricate realm of Soft Sets and their extensions, including HyperSoft Set, IndetermSoft Set, IndetermHyperSoft Set, and TreeSoft Set, within the context of biomedical data analysis. Soft Sets serve as a foundational framework for managing the inherent uncertainty and imprecision inherent in biological data, thereby facilitating informed decision-making and knowledge discovery. The exploration of Soft Set Products, particularly in the context of multiple soft sets, underscores their pivotal role in advancing biomedical research. By extending these concepts to HyperSoft Sets, researchers can unlock deeper insights into complex biological phenomena, enabling more accurate predictions and classification.
Machine Learning Methods For Computational Phenotyping Using Patient Healthcare Data With Noisy Labels, Praveen Kumar
Machine Learning Methods For Computational Phenotyping Using Patient Healthcare Data With Noisy Labels, Praveen Kumar
Computer Science ETDs
Positive and Unlabeled (PU) learning problems abound in many real-world applications. In healthcare informatics, diagnosed patients are considered labeled positive for a specific disease, but being undiagnosed does not mean they can be labeled negative. PU learning can improve classification performance, and estimate the positive fraction, α, among unlabeled samples. However, algorithms based on the Selected Completely At Random (SCAR) assumption are inadequate when the SCAR assumption fails (e.g., severe cases overrepresented), and when class imbalance is substantial. This dissertation presents and evaluates new algorithms to overcome these limitations. The proposed methods outperform the state-of-art for α-estimation, enhance classification performance, …