Predictive Patient Segmentation (PPS)

Utilizing machine learning algorithms and advanced analytics, Predictive Patient Segmentation identifies and categorizes patients based on their health risk profiles, potential future needs, and preferences. This strategy enables healthcare providers to deliver individualized, targeted care plans, resulting in enhanced health outcomes and more efficient resource allocation.

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"Robust adversarial uncertainty quantification for deep learning fine-tuning"

This research was conducted by Usman Ahmed and Jerry Chun-Wei Lin. It was published in The Journal of Supercomputing. The study proposes a deep learning model that is robust and capable of handling highly uncertain inputs, which can improve the performance of machine learning models, categorization of radiographic images, risk of misdiagnosis in medical imaging, and accuracy of medical diagnoses.

"GaNDLF: the generally nuanced deep learning framework for scalable end-to-end clinical workflows"

This research was conducted by a large team of researchers including Sarthak Pati, Siddhesh P. Thakur, İbrahim Ethem Hamamcı, Ujjwal Baid, Bhakti Baheti, Megh Bhalerao, Orhun Güley, Sofia Mouchtaris, David Lang, Spyridon Thermos, Karol Gotkowski, Camila González, Caleb Grenko, Alexander Getka, Brandon Edwards, Micah Sheller, Junwen Wu, Deepthi Karkada, Ravi Panchumarthy, Vinayak Ahluwalia, Chunrui Zou, Vishnu Bashyam, Yuemeng Li, Babak Haghighi, Rhea Chitalia, Shahira Abousamra, Tahsin M. Kurc, Aimilia Gastounioti, Sezgin Er, Mark Bergman, Joel H. Saltz, Yong Fan, Prashant Shah, Anirban Mukhopadhyay, Sotirios A. Tsaftaris, Bjoern Menze, Christos Davatzikos, Despina Kontos, Alexandros Karargyris, Renato Umeton, Peter Mattson, and Spyridon Bakas. It was published in the journal npj Digital Medicine. The study presents the GaNDLF, a deep learning framework aimed at making the mechanism of deep learning development, training, and inference more stable, reproducible, interpretable, and scalable in clinical workflows.

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