Predicting Age-related Macular Degeneration Progression with Longitudinal Fundus Images Using Deep Learning

Autor: Junghwan Lee, Tingyi Wanyan, Qingyu Chen, Tiarnan D. L. Keenan, Benjamin S. Glicksberg, Emily Y. Chew, Zhiyong Lu, Fei Wang, Yifan Peng

Publikováno v: Mach Learn Med Imaging
Machine Learning in Medical Imaging ISBN: 9783031210136

Accurately predicting a patient’s risk of progressing to late age-related macular degeneration (AMD) is difficult but crucial for personalized medicine. While existing risk prediction models for progression to late AMD are useful for triaging patie

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f161ab864668e43300f3bfdc32090b43
https://europepmc.org/articles/PMC9842432/

Deep Learning with Heterogeneous Graph Embeddings for Mortality Prediction from Electronic Health Records

Autor: Ying Ding, Benjamin S. Glicksberg, Ariful Azad, Hossein Honarvar, Tingyi Wanyan

Publikováno v: Data Intelligence. 3:329-339

Computational prediction of in-hospital mortality in the setting of an intensive care unit can help clinical practitioners to guide care and make early decisions for interventions. As clinical data are complex and varied in their structure and compon

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::455d11338f65a8a93f7fe8fcf73a10c0
https://doi.org/10.1162/dint_a_00097

Biomedical Knowledge Graph Refinement and Completion Using Graph Representation Learning and Top-K Similarity Measure

Autor: Abhik Seal, Ying Ding, Tingyi Wanyan, Jack Roper, Majdi Hassan, Islam Akef Ebeid

Publikováno v: Diversity, Divergence, Dialogue ISBN: 9783030712914
iConference (1)

Knowledge Graphs have been one of the fundamental methods for integrating heterogeneous data sources. Integrating heterogeneous data sources is crucial, especially in the biomedical domain, where central data-driven tasks such as drug discovery rely

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::79c36834b91dbff2fe6912c764b83f93
https://doi.org/10.1007/978-3-030-71292-1_10

Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach

Autor: Riccardo Miotto, Samuel J. Lee, Ishan Paranjpe, Fei Wang, Arvind Kumar, Zahi A. Fayad, Jessica K De Freitas, Jie Xu, Shelly Teng, Young Joon Kwon, Mesude Bicak, Tingyi Wanyan, Kipp W. Johnson, Benjamin S. Glicksberg, Shan Zhao, Girish N. Nadkarni, Eyal Klang, Akhil Vaid, Erwin P. Bottinger, Suraj K. Jaladanki, Sulaiman Somani, Anthony Costa, Alexander W. Charney

Publikováno v: medRxiv
article-version (status) pre
article-version (number) 1
JMIR Medical Informatics
JMIR Medical Informatics, Vol 9, Iss 1, p e24207 (2021)

Background Machine learning models require large datasets that may be siloed across different health care institutions. Machine learning studies that focus on COVID-19 have been limited to single-hospital data, which limits model generalizability. Ob

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c097e0c8a14aa3076b161a86f86c3e7
https://pubmed.ncbi.nlm.nih.gov/32817979

Heterogeneous Graph Embeddings of Electronic Health Records Improve Critical Care Disease Predictions

Autor: Ariful Azad, Riccardo Miotto, Martin Kang, Ying Ding, Akhil Vaid, Marcus A. Badgeley, Justin F. Rousseau, Kipp W. Johnson, Fei Wang, Shan Zhao, Benjamin S. Glicksberg, Girish N. Nadkarni, Jessica K De Freitas, Fayzan Chaudhry, Tingyi Wanyan

Publikováno v: Artificial Intelligence in Medicine ISBN: 9783030591366
AIME

Electronic Health Record (EHR) data is a rich source for powerful biomedical discovery but it consists of a wide variety of data types that are traditionally difficult to model. Furthermore, many machine learning frameworks that utilize these data fo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::923894311cf9c948116b116181be5cb9
https://doi.org/10.1007/978-3-030-59137-3_2