Machine learning generalizability across healthcare settings: insights from multi-site COVID-19 screening
As patient health information is highly regulated due to privacy concerns, most machine
learning (ML)-based healthcare studies are unable to test on external patient cohorts …
learning (ML)-based healthcare studies are unable to test on external patient cohorts …
Perspectives on validation of clinical predictive algorithms
The generalizability of predictive algorithms is of key relevance to application in clinical
practice. We provide an overview of three types of generalizability, based on existing …
practice. We provide an overview of three types of generalizability, based on existing …
Diachronic and synchronic variation in the performance of adaptive machine learning systems: the ethical challenges
J Hatherley, R Sparrow - Journal of the American Medical …, 2023 - academic.oup.com
Objectives Machine learning (ML) has the potential to facilitate “continual learning” in
medicine, in which an ML system continues to evolve in response to exposure to new data …
medicine, in which an ML system continues to evolve in response to exposure to new data …
Applications of machine learning on electronic health record data to combat antibiotic resistance
SE Blechman, ES Wright - The Journal of Infectious Diseases, 2024 - academic.oup.com
There is growing excitement about the clinical use of artificial intelligence and machine
learning technologies. Advancements in computing and the accessibility of machine …
learning technologies. Advancements in computing and the accessibility of machine …
Heterogeneity in Antidepressant Treatment and Major Depressive Disorder Outcomes Among Clinicians
Importance While abundant work has examined patient-level differences in antidepressant
treatment outcomes, little is known about the extent of clinician-level differences …
treatment outcomes, little is known about the extent of clinician-level differences …
A simulation-based evaluation of machine learning models for clinical decision support: application and analysis using hospital readmission
The interest in applying machine learning in healthcare has grown rapidly in recent years.
Most predictive algorithms requiring pathway implementations are evaluated using metrics …
Most predictive algorithms requiring pathway implementations are evaluated using metrics …
Machine learning based early mortality prediction in the emergency department
Background It is a great challenge for emergency physicians to early detect the patient's
deterioration and prevent unexpected death through a large amount of clinical data, which …
deterioration and prevent unexpected death through a large amount of clinical data, which …
COVID-19–associated coagulopathy: less fibrinolysis can be more harmful!
K Görlinger, JH Levy - Anesthesiology, 2021 - pubs.asahq.org
With more than 300,000 deaths, the United States is the country with the highest coronavirus
disease 2019 (COVID-19) death toll globally, and the hospitalization attributable to COVID …
disease 2019 (COVID-19) death toll globally, and the hospitalization attributable to COVID …
Assessing optimal methods for transferring machine learning models to low-volume and imbalanced clinical datasets: experiences from predicting outcomes of Danish …
Introduction Accurately predicting patient outcomes is crucial for improving healthcare
delivery, but large-scale risk prediction models are often developed and tested on specific …
delivery, but large-scale risk prediction models are often developed and tested on specific …
PetBERT: automated ICD-11 syndromic disease coding for outbreak detection in first opinion veterinary electronic health records
Effective public health surveillance requires consistent monitoring of disease signals such
that researchers and decision-makers can react dynamically to changes in disease …
that researchers and decision-makers can react dynamically to changes in disease …