Artificial intelligence-assisted diagnostic cytology and genomic testing for hematologic disorders
Artificial intelligence (AI) is a rapidly evolving field of computer science that involves the
development of computational programs that can mimic human intelligence. In particular …
development of computational programs that can mimic human intelligence. In particular …
The Potential Promise of Machine Learning in Myelodysplastic Syndrome
The introduction of artificial intelligence (AI), and in particular machine learning (ML), has
revolutionized biomedical research at the clinical level, a trend that also includes …
revolutionized biomedical research at the clinical level, a trend that also includes …
[HTML][HTML] Predicting falls in long-term care facilities: machine learning study
Background Short-term fall prediction models that use electronic health records (EHRs) may
enable the implementation of dynamic care practices that specifically address changes in …
enable the implementation of dynamic care practices that specifically address changes in …
Machine Learning Approach for Improved Longitudinal Prediction of Progression from Mild Cognitive Impairment to Alzheimer's Disease
Mild cognitive impairment (MCI) is cognitive decline that can indicate future risk of
Alzheimer's disease (AD). We developed and validated a machine learning algorithm (MLA) …
Alzheimer's disease (AD). We developed and validated a machine learning algorithm (MLA) …
[HTML][HTML] Thrombosis prophylaxis in surgical patients using the Caprini Risk Score
S Wilson, X Chen, MA Cronin, N Dengler… - Current Problems in …, 2022 - Elsevier
Venous thromboembolism (VTE), which encompasses deep venous thrombosis (DVT) and
pulmonary embolism (PE), is associated with significant mortality and morbidity among …
pulmonary embolism (PE), is associated with significant mortality and morbidity among …
[HTML][HTML] Multitask learning with recurrent neural networks for acute respiratory distress syndrome prediction using only electronic health record data: model …
Background Acute respiratory distress syndrome (ARDS) is a condition that is often
considered to have broad and subjective diagnostic criteria and is associated with …
considered to have broad and subjective diagnostic criteria and is associated with …
Deep learning approaches to automatic chronic venous disease classification
M Barulina, A Sanbaev, S Okunkov, I Ulitin… - Mathematics, 2022 - mdpi.com
Chronic venous disease (CVD) occurs in a substantial proportion of the world's population. If
the onset of CVD looks like a cosmetic defect, over time, it might be transformed into serious …
the onset of CVD looks like a cosmetic defect, over time, it might be transformed into serious …
Evaluation of machine learning algorithms for early diagnosis of deep venous thrombosis
EE Contreras-Luján, EE García-Guerrero… - Mathematical and …, 2022 - mdpi.com
Deep venous thrombosis (DVT) is a disease that must be diagnosed quickly, as it can trigger
the death of patients. Nowadays, one can find different ways to determine it, including …
the death of patients. Nowadays, one can find different ways to determine it, including …
Prediction of Venous Thromboembolism in Diverse Populations Using Machine Learning and Structured Electronic Health Records
BACKGROUND: Venous thromboembolism (VTE) is a major cause of morbidity and
mortality worldwide. Current risk assessment tools, such as the Caprini and Padua scores …
mortality worldwide. Current risk assessment tools, such as the Caprini and Padua scores …
Development and validation of machine learning models for venous thromboembolism risk assessment at admission: a retrospective study
W Sheng, X Wang, W Xu, Z Hao, H Ma… - Frontiers in …, 2023 - frontiersin.org
Introduction Venous thromboembolism (VTE) risk assessment at admission is of great
importance for early screening and timely prophylaxis and management during …
importance for early screening and timely prophylaxis and management during …