Výsledky vyhledávání - "Erik, Drysdale"

Akademický článek

The Hydronephrosis Severity Index guides paediatric antenatal hydronephrosis management based on artificial intelligence applied to ultrasound images alone

Publikováno v: Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)

Abstract Antenatal hydronephrosis (HN) impacts up to 5% of pregnancies and requires close, frequent follow-up monitoring to determine who may benefit from surgical intervention. To create an automated HN Severity Index (HSI) that helps guide clinical

Externí odkaz: https://doaj.org/article/8aa35c14841242259eba98cfaf0f7df3

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Akademický článek

Therapeutic preference for Alzheimer’s disease treatments: a discrete choice experiment with caregivers and neurologists

Autor: George Dranitsaris, Quanwu Zhang, Lin Mu, Christopher Weyrer, Erik Drysdale, Peter Neumann, Alireza Atri, Amir Abbas Tahami Monfared

Publikováno v: Alzheimer’s Research & Therapy, Vol 15, Iss 1, Pp 1-11 (2023)

Abstract Background Alzheimer’s disease (AD) is a major global health crisis in need of more effective therapies. However, difficult choices to optimize value-based care will need to be made. While identifying preferred therapeutic attributes of ne

Externí odkaz: https://doaj.org/article/a1f208885ef84296aa525f8ff3128798

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Akademický článek

The telomere length landscape of prostate cancer

Autor: Julie Livingstone, Yu-Jia Shiah, Takafumi N. Yamaguchi, Lawrence E. Heisler, Vincent Huang, Robert Lesurf, Tsumugi Gebo, Benjamin Carlin, Stefan Eng, Erik Drysdale, Jeffrey Green, Theodorus van der Kwast, Robert G. Bristow, Michael Fraser, Paul C. Boutros

Publikováno v: Nature Communications, Vol 12, Iss 1, Pp 1-13 (2021)

Despite the known role of telomere length in cancer, its association with genomic features remains unclear. Here, the authors integrate telomere length, genomics, transcriptomics and proteomics in localized prostate cancer and reveal links between te

Externí odkaz: https://doaj.org/article/3a1b96dd7f314201be56b49c89bc022a

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Akademický článek

Automatically disambiguating medical acronyms with ontology-aware deep learning

Autor: Marta Skreta, Aryan Arbabi, Jixuan Wang, Erik Drysdale, Jacob Kelly, Devin Singh, Michael Brudno

Publikováno v: Nature Communications, Vol 12, Iss 1, Pp 1-10 (2021)

Disambiguating abbreviations is important for automated clinical note processing; however, deploying machine learning for this task is restricted by lack of good training data. Here, the authors show novel data augmentation methods that use biomedica

Externí odkaz: https://doaj.org/article/55b59136062549e789f34c5a3fb40092

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Predicting recurrence and recurrence‐free survival in high‐grade endometrial cancer using machine learning

Autor: Sabrina Piedimonte, Tomer Feigenberg, Erik Drysdale, Janice Kwon, Walter H. Gotlieb, Beatrice Cormier, Marie Plante, Susie Lau, Limor Helpman, Marie‐Claude Renaud, Taymaa May, Danielle Vicus

Publikováno v: Journal of Surgical Oncology. 126:1096-1103

To develop machine-learning models to predict recurrence and time-to-recurrence in high-grade endometrial cancer (HGEC) following surgery and tailored adjuvant treatment.Data were retrospectively collected across eight Canadian centers including 1237

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e10b028b2e02f8fa69ad28acb6e94f5d
https://doi.org/10.1002/jso.27008

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Personalized application of machine learning algorithms to identify pediatric patients at risk for recurrent ureteropelvic junction obstruction after dismembered pyeloplasty

Autor: Mandy Rickard, Marisol Lolas, Jethro C.C. Kwong, Joana Dos Santos, Jin K. Kim, Armando J. Lorenzo, Michael Chua, Lauren Erdman, Adree Khondker, Erik Drysdale, Daniel T. Keefe, Gregory E. Tasian

Publikováno v: World Journal of Urology. 40:593-599

To develop a model that predicts whether a child will develop a recurrent obstruction after pyeloplasty, determine their survival risk score, and expected time to re-intervention using machine learning (ML). We reviewed patients undergoing pyeloplast

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::7260933ce62c5f7e1fada5fb2106cd88
https://doi.org/10.1007/s00345-021-03879-z

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From Clinic to Computer and Back Again: Practical Considerations When Designing and Implementing Machine Learning Solutions for Pediatrics

Autor: Erik Drysdale, Michael Brudno, Felipe Morgado, Ian Stedman, Liam G. McCoy, Stephen Lu, Vinyas Harish, Sujay Nagaraj, Devin Singh

Publikováno v: Current Treatment Options in Pediatrics

Purpose of review Machine learning (ML), a branch of artificial intelligence, is influencing all fields in medicine, with an abundance of work describing its application to adult practice. ML in pediatrics is distinctly unique with clinical, technica

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6388adc9541250a02fc2dfbeb75eb871
http://europepmc.org/articles/PMC7490206

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