A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease
Identification of individuals at highest risk of coronary artery disease (CAD)—ideally before
onset—remains an important public health need. Prior studies have developed genome …
onset—remains an important public health need. Prior studies have developed genome …
Polygenic scoring accuracy varies across the genetic ancestry continuum
Polygenic scores (PGSs) have limited portability across different grou**s of individuals (for
example, by genetic ancestries and/or social determinants of health), preventing their …
example, by genetic ancestries and/or social determinants of health), preventing their …
[HTML][HTML] Machine learning use for prognostic purposes in multiple sclerosis
The course of multiple sclerosis begins with a relapsing-remitting phase, which evolves into
a secondarily progressive form over an extremely variable period, depending on many …
a secondarily progressive form over an extremely variable period, depending on many …
Attention-based multimodal fusion with contrast for robust clinical prediction in the face of missing modalities
Objective: With the increasing amount and growing variety of healthcare data, multimodal
machine learning supporting integrated modeling of structured and unstructured data is an …
machine learning supporting integrated modeling of structured and unstructured data is an …
A new approach for interpretability and reliability in clinical risk prediction: Acute coronary syndrome scenario
Introduction The risk prediction of the occurrence of a clinical event is often based on
conventional statistical procedures, through the implementation of risk score models …
conventional statistical procedures, through the implementation of risk score models …
The prognostic utility of galectin-3 in patients undergoing cardiac surgery: a sco** review
Objective To review the utility of galectin-3 (Gal-3) as a biomarker for postoperative adverse
outcomes in patients undergoing cardiac surgery. Method This review was conducted in …
outcomes in patients undergoing cardiac surgery. Method This review was conducted in …
Multiclass risk models for ovarian malignancy: an illustration of prediction uncertainty due to the choice of algorithm
A Ledger, J Ceusters, L Valentin, A Testa… - BMC Medical Research …, 2023 - Springer
Background Assessing malignancy risk is important to choose appropriate management of
ovarian tumors. We compared six algorithms to estimate the probabilities that an ovarian …
ovarian tumors. We compared six algorithms to estimate the probabilities that an ovarian …
Uncertainty estimation for classification and risk prediction on medical tabular data
In a data-scarce field such as healthcare, where models often deliver predictions on patients
with rare conditions, the ability to measure the uncertainty of a model's prediction could …
with rare conditions, the ability to measure the uncertainty of a model's prediction could …
Learning to predict with supporting evidence: Applications to clinical risk prediction
The impact of machine learning models on healthcare will depend on the degree of trust that
healthcare professionals place in the predictions made by these models. In this paper, we …
healthcare professionals place in the predictions made by these models. In this paper, we …
Uncertainty-Aware and Explainable Human Error Detection in the Operation of Nuclear Power Plants
B Reddy, E Gursel, K Daniels, A Khojandi… - Nuclear …, 2024 - Taylor & Francis
The timely and accurate identification of incidents, such as human factor error, is important to
restore nuclear power plants (NPPs) to a stable state. However, the identification of …
restore nuclear power plants (NPPs) to a stable state. However, the identification of …