Building Trust: Lessons from the Technion-Rambam Machine Learning in Healthcare Datathon Event
원문 초록
A datathon is a time-constrained competition involving data science applied to a specific problem. In the past decade, datathons have been shown to be a valuable bridge between fields and expertise . Biomedical data analysis represents a challenging area requiring collaboration between engineers, biologists and physicians to gain a better understanding of patient physiology and of guide decision processes for diagnosis, prognosis and therapeutic interventions to improve care practice. Here, we reflect on the outcomes of an event that we organized in Israel at the end of March 2022 between the MIT Critical Data group, Rambam Health Care Campus (Rambam) and the Technion Israel Institute of Technology (Technion) in Haifa. Participants were asked to complete a survey about their skills and interests, which enabled us to identify current needs in machine learning training for medical problem applications. This work describes opportunities and limitations in medical data science in the Israeli context.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Learning Disentangled Stain and Structural Representations for Semi-Supervised Histopathology Segmentation
MiCo: Multiple Instance Learning with Context-Aware Clustering for Whole Slide Image Analysis
Revisiting End-to-End Learning with Slide-level Supervision in Computational Pathology
Cardiac Digital Twins at Scale from MRI: Open Tools and Representative Models from ~55000 UK Biobank Participants
Zero-Shot Multi-modal Large Language Model v.s. Supervised Deep Learning: A Comparative Study on CT-Based Intracranial Hemorrhage Subtyping
Equivariant Imaging Biomarkers for Robust Unsupervised Segmentation of Histopathology
토론
이 논문과 연결된 포럼 글이 아직 없습니다.