Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review
Objective To assess the methodological quality of studies on prediction models developed
using machine learning techniques across all medical specialties. Design Systematic …
using machine learning techniques across all medical specialties. Design Systematic …
Secure and robust machine learning for healthcare: A survey
Recent years have witnessed widespread adoption of machine learning (ML)/deep learning
(DL) techniques due to their superior performance for a variety of healthcare applications …
(DL) techniques due to their superior performance for a variety of healthcare applications …
Artificial intelligence system to determine risk of T1 colorectal cancer metastasis to lymph node
Background & Aims In accordance with guidelines, most patients with T1 colorectal cancers
(CRC) undergo surgical resection with lymph node dissection, despite the low incidence (∼ …
(CRC) undergo surgical resection with lymph node dissection, despite the low incidence (∼ …
[HTML][HTML] Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury
Objective We aimed to explore the added value of common machine learning (ML)
algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study …
algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study …
Machine learning-based dynamic mortality prediction after traumatic brain injury
R Raj, T Luostarinen, E Pursiainen, JP Posti… - Scientific reports, 2019 - nature.com
Our aim was to create simple and largely scalable machine learning-based algorithms that
could predict mortality in a real-time fashion during intensive care after traumatic brain injury …
could predict mortality in a real-time fashion during intensive care after traumatic brain injury …
XGBoost machine learning algorism performed better than regression models in predicting mortality of moderate-to-severe traumatic brain injury
R Wang, L Wang, J Zhang, M He, J Xu - World Neurosurgery, 2022 - Elsevier
Background Traumatic brain injury (TBI) brings severe mortality and morbidity risk to
patients. Predicting the outcome of these patients is necessary for physicians to make …
patients. Predicting the outcome of these patients is necessary for physicians to make …
Machine learning and surgical outcomes prediction: a systematic review
Background Machine learning (ML) has garnered increasing attention as a means to
quantitatively analyze the growing and complex medical data to improve individualized …
quantitatively analyze the growing and complex medical data to improve individualized …
Appositeness of optimized and reliable machine learning for healthcare: a survey
Abstract Machine Learning (ML) has been categorized as a branch of Artificial Intelligence
(AI) under the Computer Science domain wherein programmable machines imitate human …
(AI) under the Computer Science domain wherein programmable machines imitate human …
[HTML][HTML] Machine learning–based short-term mortality prediction models for patients with cancer using electronic health record data: systematic review and critical …
Background In the United States, national guidelines suggest that aggressive cancer care
should be avoided in the final months of life. However, guideline compliance currently …
should be avoided in the final months of life. However, guideline compliance currently …
A hierarchical expert-guided machine learning framework for clinical decision support systems: an application to traumatic brain injury prognostication
Prognosis of the long-term functional outcome of traumatic brain injury is essential for
personalized management of that injury. Nonetheless, accurate prediction remains …
personalized management of that injury. Nonetheless, accurate prediction remains …