Zobrazeno 1 - 10
of 99
pro vyhledávání: '"Jitendra Jonnagaddala"'
Autor:
Julia Höhn, Eva Krieghoff-Henning, Christoph Wies, Lennard Kiehl, Martin J. Hetz, Tabea-Clara Bucher, Jitendra Jonnagaddala, Kurt Zatloukal, Heimo Müller, Markus Plass, Emilian Jungwirth, Timo Gaiser, Matthias Steeg, Tim Holland-Letz, Hermann Brenner, Michael Hoffmeister, Titus J. Brinker
Publikováno v:
npj Precision Oncology, Vol 7, Iss 1, Pp 1-12 (2023)
Abstract Studies have shown that colorectal cancer prognosis can be predicted by deep learning-based analysis of histological tissue sections of the primary tumor. So far, this has been achieved using a binary prediction. Survival curves might contai
Externí odkaz:
https://doaj.org/article/e094c1a7b01c4b7bb8979efab082563b
Autor:
Jiaxing Liu, Shalini Gupta, Aipeng Chen, Chen-Kai Wang, Pratik Mishra, Hong-Jie Dai, Zoie Shui-Yee Wong, Jitendra Jonnagaddala
Publikováno v:
Journal of Medical Internet Research, Vol 25, p e48145 (2023)
BackgroundElectronic health records (EHRs) in unstructured formats are valuable sources of information for research in both the clinical and biomedical domains. However, before such records can be used for research purposes, sensitive health informat
Externí odkaz:
https://doaj.org/article/b5fb6bc037c045c28e4998640704641f
Publikováno v:
The Journal of Pathology: Clinical Research, Vol 9, Iss 3, Pp 223-235 (2023)
Abstract Many artificial intelligence models have been developed to predict clinically relevant biomarkers for colorectal cancer (CRC), including microsatellite instability (MSI). However, existing deep learning networks require large training datase
Externí odkaz:
https://doaj.org/article/8eafad7e89b0475797747539da604015
Autor:
Piumi Sandarenu, Ewan K. A. Millar, Yang Song, Lois Browne, Julia Beretov, Jodi Lynch, Peter H. Graham, Jitendra Jonnagaddala, Nicholas Hawkins, Junzhou Huang, Erik Meijering
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-12 (2022)
Abstract Computational pathology is a rapidly expanding area for research due to the current global transformation of histopathology through the adoption of digital workflows. Survival prediction of breast cancer patients is an important task that cu
Externí odkaz:
https://doaj.org/article/f4c314d9cc1a4265a0a2e2bc087eee51
Autor:
Ross D. Williams, Aniek F. Markus, Cynthia Yang, Talita Duarte-Salles, Scott L. DuVall, Thomas Falconer, Jitendra Jonnagaddala, Chungsoo Kim, Yeunsook Rho, Andrew E. Williams, Amanda Alberga Machado, Min Ho An, María Aragón, Carlos Areia, Edward Burn, Young Hwa Choi, Iannis Drakos, Maria Tereza Fernandes Abrahão, Sergio Fernández-Bertolín, George Hripcsak, Benjamin Skov Kaas-Hansen, Prasanna L. Kandukuri, Jan A. Kors, Kristin Kostka, Siaw-Teng Liaw, Kristine E. Lynch, Gerardo Machnicki, Michael E. Matheny, Daniel Morales, Fredrik Nyberg, Rae Woong Park, Albert Prats-Uribe, Nicole Pratt, Gowtham Rao, Christian G. Reich, Marcela Rivera, Tom Seinen, Azza Shoaibi, Matthew E. Spotnitz, Ewout W. Steyerberg, Marc A. Suchard, Seng Chan You, Lin Zhang, Lili Zhou, Patrick B. Ryan, Daniel Prieto-Alhambra, Jenna M. Reps, Peter R. Rijnbeek
Publikováno v:
BMC Medical Research Methodology, Vol 22, Iss 1, Pp 1-13 (2022)
Abstract Background We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Esti
Externí odkaz:
https://doaj.org/article/da152b2f9d8848c687fd69d67eb875e5
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
Abstract For research purposes, protected health information is often redacted from unstructured electronic health records to preserve patient privacy and confidentiality. The OpenDeID corpus is designed to assist development of automatic methods to
Externí odkaz:
https://doaj.org/article/bf43a13da7444d1aa4e7e7642ca37eee
Autor:
Solveig K. Sieberts, Jennifer Schaff, Marlena Duda, Bálint Ármin Pataki, Ming Sun, Phil Snyder, Jean-Francois Daneault, Federico Parisi, Gianluca Costante, Udi Rubin, Peter Banda, Yooree Chae, Elias Chaibub Neto, E. Ray Dorsey, Zafer Aydın, Aipeng Chen, Laura L. Elo, Carlos Espino, Enrico Glaab, Ethan Goan, Fatemeh Noushin Golabchi, Yasin Görmez, Maria K. Jaakkola, Jitendra Jonnagaddala, Riku Klén, Dongmei Li, Christian McDaniel, Dimitri Perrin, Thanneer M. Perumal, Nastaran Mohammadian Rad, Erin Rainaldi, Stefano Sapienza, Patrick Schwab, Nikolai Shokhirev, Mikko S. Venäläinen, Gloria Vergara-Diaz, Yuqian Zhang, the Parkinson’s Disease Digital Biomarker Challenge Consortium, Yuanjia Wang, Yuanfang Guan, Daniela Brunner, Paolo Bonato, Lara M. Mangravite, Larsson Omberg
Publikováno v:
npj Digital Medicine, Vol 4, Iss 1, Pp 1-12 (2021)
Abstract Consumer wearables and sensors are a rich source of data about patients’ daily disease and symptom burden, particularly in the case of movement disorders like Parkinson’s disease (PD). However, interpreting these complex data into so-cal
Externí odkaz:
https://doaj.org/article/45ee48713e9b4dd591d3f8cc81d31894
Autor:
Mifetika Lukitasari, Sony Apriliyawan, Halidah Manistamara, Yurike Olivia Sella, Mohammad Saifur Rohman, Jitendra Jonnagaddala
Publikováno v:
Global Heart, Vol 18, Iss 1, Pp 18-18 (2023)
Background: Chest pain misinterpretation is the leading cause of pre-hospital delay in acute coronary syndrome (ACS). This study aims to identify and differentiate the chest pain characteristics associated with ACS. Methods: A total of 164 patients w
Externí odkaz:
https://doaj.org/article/a58081f48c9c410195f73a06cb1395aa
Publikováno v:
Entropy, Vol 24, Iss 11, p 1669 (2022)
Most deep-learning algorithms that use Hematoxylin- and Eosin-stained whole slide images (WSIs) to predict cancer survival incorporate image patches either with the highest scores or a combination of both the highest and lowest scores. In this study,
Externí odkaz:
https://doaj.org/article/549d7108d6424b248af9d103daf32063
Autor:
Diana Barsasella, Karamo Bah, Pratik Mishra, Mohy Uddin, Eshita Dhar, Dewi Lena Suryani, Dedi Setiadi, Imas Masturoh, Ida Sugiarti, Jitendra Jonnagaddala, Shabbir Syed-Abdul
Publikováno v:
Medicina, Vol 58, Iss 11, p 1568 (2022)
Background and Objectives: Taiwan is among the nations with the highest rates of Type 2 Diabetes Mellitus (T2DM) and Hypertension (HTN). As more cases are reported each year, there is a rise in hospital admissions for people seeking medical attention
Externí odkaz:
https://doaj.org/article/dfde3b9a776e46b8ad05c7791e879ea7