[HTML][HTML] Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and …
This paper discusses some overlooked challenges faced when working with machine
learning models for histopathology and presents a novel opportunity to support “Learning …
learning models for histopathology and presents a novel opportunity to support “Learning …
[HTML][HTML] Machine Learning Operations (MLOps) in health care: A scoping review
A Rajagopal, S Ayanian, AJ Ryu, R Qian… - Mayo Clinic …, 2024 - Elsevier
The use of machine learning tools in healthcare is rapidly expanding. However, the
processes that support these tools in deployment, ie, machine-learning operations (MLOps) …
processes that support these tools in deployment, ie, machine-learning operations (MLOps) …
Automatic correction of performance drift under acquisition shift in medical image classification
M Roschewitz, G Khara, J Yearsley, N Sharma… - Nature …, 2023 - nature.com
Image-based prediction models for disease detection are sensitive to changes in data
acquisition such as the replacement of scanner hardware or updates to the image …
acquisition such as the replacement of scanner hardware or updates to the image …
Longitudinal dynamic clinical phenotypes of in-hospital COVID-19 patients across three dominant virus variants in New York
Background COVID-19 is a challenging disease to characterize given its wide-ranging
heterogeneous symptomatology. Several studies have attempted to extract clinical …
heterogeneous symptomatology. Several studies have attempted to extract clinical …
Empirical data drift detection experiments on real-world medical imaging data
While it is common to monitor deployed clinical artificial intelligence (AI) models for
performance degradation, it is less common for the input data to be monitored for data drift …
performance degradation, it is less common for the input data to be monitored for data drift …
Monitoring performance of clinical artificial intelligence in health care: a scoping review
ES Andersen, JB Birk-Korch, RS Hansen… - JBI evidence …, 2024 - journals.lww.com
Objective: The objective of this review was to provide an overview of the diverse methods
described, tested, or implemented for monitoring performance of clinical artificial intelligence …
described, tested, or implemented for monitoring performance of clinical artificial intelligence …
Machine learning to predict completion of treatment for pancreatic cancer
SA Pasha, A Khalid, T Levy, L Demyan… - Journal of Surgical …, 2024 - Wiley Online Library
Background Chemotherapy enhances survival rates for pancreatic cancer (PC) patients
postsurgery, yet less than 60% complete adjuvant therapy, with a smaller fraction …
postsurgery, yet less than 60% complete adjuvant therapy, with a smaller fraction …
European and US Guideline‐Based Statin Eligibility, Genetically Predicted Coronary Artery Disease, and the Risk of Major Coronary Events
Background A study was designed to investigate whether the coronary artery disease
polygenic risk score (CAD‐PRS) may guide lipid‐lowering treatment initiation as well as …
polygenic risk score (CAD‐PRS) may guide lipid‐lowering treatment initiation as well as …
US and Dutch perspectives on the use of COVID-19 clinical prediction models: findings from a qualitative analysis
MJ Basile, IRAR Helmrich, JG Park… - Medical Decision …, 2023 - journals.sagepub.com
Introduction Clinical prediction models (CPMs) for coronavirus disease 2019 (COVID-19)
may support clinical decision making, treatment, and communication. However, attitudes …
may support clinical decision making, treatment, and communication. However, attitudes …
The association of clinically relevant variables with chest radiograph lung disease burden quantified in real-time by radiologists upon initial presentation in individuals …
Objectives We aimed to correlate lung disease burden on presentation chest radiographs
(CXR), quantified at the time of study interpretation, with clinical presentation in patients …
(CXR), quantified at the time of study interpretation, with clinical presentation in patients …