Machine learning for precision medicine
SJ MacEachern, ND Forkert - Genome, 2021 - cdnsciencepub.com
Precision medicine is an emerging approach to clinical research and patient care that
focuses on understanding and treating disease by integrating multi-modal or multi-omics …
focuses on understanding and treating disease by integrating multi-modal or multi-omics …
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 …
Mind the gap: Performance metric evaluation in brain‐age prediction
Estimating age based on neuroimaging‐derived data has become a popular approach to
developing markers for brain integrity and health. While a variety of machine‐learning …
developing markers for brain integrity and health. While a variety of machine‐learning …
Developing clinical prediction models: a step-by-step guide
Predicting future outcomes of patients is essential to clinical practice, with many prediction
models published each year. Empirical evidence suggests that published studies often have …
models published each year. Empirical evidence suggests that published studies often have …
From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning
LS Li, L Yang, L Zhuang, ZY Ye, WG Zhao… - Military Medical …, 2023 - Springer
Latent tuberculosis infection (LTBI) has become a major source of active tuberculosis (ATB).
Although the tuberculin skin test and interferon-gamma release assay can be used to …
Although the tuberculin skin test and interferon-gamma release assay can be used to …
Artificial intelligence, machine learning, and deep learning for clinical outcome prediction
AI is a broad concept, grouping initiatives that use a computer to perform tasks that would
usually require a human to complete. AI methods are well suited to predict clinical outcomes …
usually require a human to complete. AI methods are well suited to predict clinical outcomes …
Use of machine learning in osteoarthritis research: a systematic literature review
Objective The aim of this systematic literature review was to provide a comprehensive and
exhaustive overview of the use of machine learning (ML) in the clinical care of osteoarthritis …
exhaustive overview of the use of machine learning (ML) in the clinical care of osteoarthritis …
[HTML][HTML] Explainable discovery of disease biomarkers: The case of ovarian cancer to illustrate the best practice in machine learning and Shapley analysis
Objective: Ovarian cancer is a significant health issue with lasting impacts on the community.
Despite recent advances in surgical, chemotherapeutic and radiotherapeutic interventions …
Despite recent advances in surgical, chemotherapeutic and radiotherapeutic interventions …
Predicting coronary heart disease using an improved LightGBM model: Performance analysis and comparison
H Yang, Z Chen, H Yang, M Tian - IEEE Access, 2023 - ieeexplore.ieee.org
Coronary heart disease (CHD) is a dangerous condition that cannot be completely cured.
Accurate detection of early coronary artery disease can assist physicians in treating patients …
Accurate detection of early coronary artery disease can assist physicians in treating patients …
Multimodal biological brain age prediction using magnetic resonance imaging and angiography with the identification of predictive regions
Biological brain age predicted using machine learning models based on high‐resolution
imaging data has been suggested as a potential biomarker for neurological and …
imaging data has been suggested as a potential biomarker for neurological and …