1 code implementation • 25 Oct 2024 • Parthasarathy Suryanarayanan, Yunguang Qiu, Shreyans Sethi, Diwakar Mahajan, Hongyang Li, Yuxin Yang, Elif Eyigoz, Aldo Guzman Saenz, Daniel E. Platt, Timothy H. Rumbell, Kenney Ng, Sanjoy Dey, Myson Burch, Bum Chul Kwon, Pablo Meyer, Feixiong Cheng, Jianying Hu, Joseph A. Morrone
We show that the multi-view models perform robustly and are able to balance the strengths and weaknesses of specific views.
no code implementations • 6 Nov 2023 • Faris F. Gulamali, Ashwin S. Sawant, Lora Liharska, Carol R. Horowitz, Lili Chan, Patricia H. Kovatch, Ira Hofer, Karandeep Singh, Lynne D. Richardson, Emmanuel Mensah, Alexander W Charney, David L. Reich, Jianying Hu, Girish N. Nadkarni
The adoption of diagnosis and prognostic algorithms in healthcare has led to concerns about the perpetuation of bias against disadvantaged groups of individuals.
no code implementations • 24 Jun 2021 • Parthasarathy Suryanarayanan, Prithwish Chakraborty, Piyush Madan, Kibichii Bore, William Ogallo, Rachita Chandra, Mohamed Ghalwash, Italo Buleje, Sekou Remy, Shilpa Mahatma, Pablo Meyer, Jianying Hu
In this work we introduce Disease Progression Modeling workbench 360 (DPM360) opensource clinical informatics framework for collaborative research and delivery of healthcare AI.
no code implementations • 15 Nov 2019 • Prithwish Chakraborty, Fei Wang, Jianying Hu, Daby Sow
While networks with explicit memory have been proposed recently, the discontinuities imposed by the discrete operations make such networks harder to train and require more supervision.
no code implementations • 19 Feb 2018 • Bin Liu, Ying Li, Soumya Ghosh, Zhaonan Sun, Kenney Ng, Jianying Hu
The proposed method is favorable for healthcare applications because in additional to improved prediction performance, relationships among the different risks and risk factors are also identified.
no code implementations • NeurIPS 2008 • Vikas Sindhwani, Jianying Hu, Aleksandra Mojsilovic
By attempting to simultaneously partition both the rows (examples) and columns (features) of a data matrix, Co-clustering algorithms often demonstrate surpris- ingly impressive performance improvements over traditional one-sided (row) clustering techniques.