[HTML][HTML] Role of artificial intelligence applications in real-life clinical practice: systematic review
Background Artificial intelligence (AI) applications are growing at an unprecedented pace in
health care, including disease diagnosis, triage or screening, risk analysis, surgical …
health care, including disease diagnosis, triage or screening, risk analysis, surgical …
[HTML][HTML] Deep learning and wearable sensors for the diagnosis and monitoring of Parkinson's disease: a systematic review
Parkinson's disease (PD) is a neurodegenerative disorder that produces both motor and non-
motor complications, degrading the quality of life of PD patients. Over the past two decades …
motor complications, degrading the quality of life of PD patients. Over the past two decades …
TRIPOD+ AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual
Prognosis Or Diagnosis) statement was published in 2015 to provide the minimum reporting …
Prognosis Or Diagnosis) statement was published in 2015 to provide the minimum reporting …
Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review
Background Describe and evaluate the methodological conduct of prognostic prediction
models developed using machine learning methods in oncology. Methods We conducted a …
models developed using machine learning methods in oncology. Methods We conducted a …
Transfer learning in magnetic resonance brain imaging: A systematic review
(1) Background: Transfer learning refers to machine learning techniques that focus on
acquiring knowledge from related tasks to improve generalization in the tasks of interest. In …
acquiring knowledge from related tasks to improve generalization in the tasks of interest. In …
Potential applications and performance of machine learning techniques and algorithms in clinical practice: a systematic review
EM Nwanosike, BR Conway, HA Merchant… - International journal of …, 2022 - Elsevier
Purpose The advent of clinically adapted machine learning algorithms can solve numerous
problems ranging from disease diagnosis and prognosis to therapy recommendations. This …
problems ranging from disease diagnosis and prognosis to therapy recommendations. This …
Machine learning methods applied to triage in emergency services: A systematic review
R Sánchez-Salmerón, JL Gómez-Urquiza… - International Emergency …, 2022 - Elsevier
Background In emergency services is important to accurately assess and classify symptoms,
which may be improved with the help of technology. One mechanism that could help and …
which may be improved with the help of technology. One mechanism that could help and …
[HTML][HTML] Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved
Objective Evaluate the completeness of reporting of prognostic prediction models developed
using machine learning methods in the field of oncology. Study design and setting We …
using machine learning methods in the field of oncology. Study design and setting We …
An explainable machine learning pipeline for stroke prediction on imbalanced data
Stroke is an acute neurological dysfunction attributed to a focal injury of the central nervous
system due to reduced blood flow to the brain. Nowadays, stroke is a global threat …
system due to reduced blood flow to the brain. Nowadays, stroke is a global threat …
Machine learning as a support for the diagnosis of type 2 diabetes
A Agliata, D Giordano, F Bardozzo, S Bottiglieri… - International Journal of …, 2023 - mdpi.com
Diabetes is a chronic, metabolic disease characterized by high blood sugar levels. Among
the main types of diabetes, type 2 is the most common. Early diagnosis and treatment can …
the main types of diabetes, type 2 is the most common. Early diagnosis and treatment can …