AIMC Topic: Clinical Decision Rules
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Combining machine learning models and rule engines in clinical decision systems: Exploring optimal aggregation methods for vaccine hesitancy prediction.
Computers in biology and medicine
Feb 20, 2025
BACKGROUND: With the increasing application of artificial intelligence (AI) technologies in the healthcare sector and the emergence of new solutions, such as large language models, there is a growing need to combine medical knowledge, often expressed...
Accommodating misclassification effects on optimizing dynamic treatment regimes with Q-learning.
Statistics in medicine
Dec 13, 2023
Research on dynamic treatment regimes has enticed extensive interest. Many methods have been proposed in the literature, which, however, are vulnerable to the presence of misclassification in covariates. In particular, although Q-learning has receive...
Estimation of a Machine Learning-Based Decision Rule to Reduce Hypoglycemia Among Older Adults With Type 1 Diabetes: A Post Hoc Analysis of Continuous Glucose Monitoring in the WISDM Study.
Journal of diabetes science and technology
Jan 11, 2023
BACKGROUND: The Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) study demonstrated continuous glucose monitoring (CGM) reduced hypoglycemia over 6 months among older adults with type 1 diabetes (T1D) compared with blood glucose monitor...
Development and External Validation of a Machine Learning Model for the Early Prediction of Doses of Harmful Intracranial Pressure in Patients with Severe Traumatic Brain Injury.
Journal of neurotrauma
Sep 13, 2022
Treatment and prevention of elevated intracranial pressure (ICP) is crucial in patients with severe traumatic brain injury (TBI). Elevated ICP is associated with secondary brain injury, and both intensity and duration of an episode of intracranial hy...
Clinical Prediction Rule for Identifying the Stroke Patients who will Obtain Clinically Important Improvement of Upper Limb Motor Function by Robot-Assisted Upper Limb.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Apr 29, 2022
BACKGROUND: The number of studies on the characteristics of patients with stroke who would benefit from robot-assisted upper limb rehabilitation is limited, and there are no clear criteria for determining which individuals should receive such treatme...
Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study.
BMJ (Clinical research ed.)
Feb 17, 2022
OBJECTIVE: To create and validate a simple and transferable machine learning model from electronic health record data to accurately predict clinical deterioration in patients with covid-19 across institutions, through use of a novel paradigm for mode...
Clinical Deterioration
Clinical Decision Rules
Predictive Value of Tests
Area Under Curve
Linear Models
Hospitals
Adolescent
ROC Curve
Aged
Aged, 80 and over
Humans
Young Adult
Machine Learning
Middle Aged
Risk Assessment
Adult
Male
Electronic Health Records
Female
COVID-19
SARS-CoV-2
Prognosis
Hospitalization
Retrospective Studies
Artificial intelligence predicts clinically relevant atrial high-rate episodes in patients with cardiac implantable electronic devices.
Scientific reports
Jan 7, 2022
To assess the utility of machine learning (ML) algorithms in predicting clinically relevant atrial high-rate episodes (AHREs), which can be recorded by a pacemaker. We aimed to develop ML-based models to predict clinically relevant AHREs based on the...
Explainable machine learning model for predicting the occurrence of postoperative malnutrition in children with congenital heart disease.
Clinical nutrition (Edinburgh, Scotland)
Nov 10, 2021
BACKGROUND & AIMS: Malnutrition is persistent in 50%-75% of children with congenital heart disease (CHD) after surgery, and early prediction is crucial for nutritional intervention. The aim of this study was to develop and validate machine learning (...
Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review.
BMJ (Clinical research ed.)
Oct 20, 2021
OBJECTIVE: To assess the methodological quality of studies on prediction models developed using machine learning techniques across all medical specialties.
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