AIMC Topic: Bayes Theorem

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Adapting machine learning techniques to censored time-to-event health record data: A general-purpose approach using inverse probability of censoring weighting.

Journal of biomedical informatics
Models for predicting the probability of experiencing various health outcomes or adverse events over a certain time frame (e.g., having a heart attack in the next 5years) based on individual patient characteristics are important tools for managing pa...

PDP-CON: prediction of domain/linker residues in protein sequences using a consensus approach.

Journal of molecular modeling
The prediction of domain/linker residues in protein sequences is a crucial task in the functional classification of proteins, homology-based protein structure prediction, and high-throughput structural genomics. In this work, a novel consensus-based ...

Diagnosis of Acute Coronary Syndrome with a Support Vector Machine.

Journal of medical systems
Acute coronary syndrome (ACS) is a serious condition arising from an imbalance of supply and demand to meet myocardium's metabolic needs. Patients typically present with retrosternal chest pain radiating to neck and left arm. Electrocardiography (ECG...

Classification of clinically useful sentences in clinical evidence resources.

Journal of biomedical informatics
UNLABELLED: Most patient care questions raised by clinicians can be answered by online clinical knowledge resources. However, important barriers still challenge the use of these resources at the point of care.

A class of joint models for multivariate longitudinal measurements and a binary event.

Biometrics
Predicting binary events such as newborns with large birthweight is important for obstetricians in their attempt to reduce both maternal and fetal morbidity and mortality. Such predictions have been a challenge in obstetric practice, where longitudin...

Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network.

Sensors (Basel, Switzerland)
A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based ...

Bayesian network modeling: A case study of an epidemiologic system analysis of cardiovascular risk.

Computer methods and programs in biomedicine
An extensive, in-depth study of cardiovascular risk factors (CVRF) seems to be of crucial importance in the research of cardiovascular disease (CVD) in order to prevent (or reduce) the chance of developing or dying from CVD. The main focus of data an...

Discovery of Influenza A virus neuraminidase inhibitors using support vector machine and Naïve Bayesian models.

Molecular diversity
Neuraminidase (NA) is a critical enzyme in the life cycle of influenza virus, which is known as a successful paradigm in the design of anti-influenza agents. However, to date there are no classification models for the virtual screening of NA inhibito...

Fuzzy State Transition and Kalman Filter Applied in Short-Term Traffic Flow Forecasting.

Computational intelligence and neuroscience
Traffic flow is widely recognized as an important parameter for road traffic state forecasting. Fuzzy state transform and Kalman filter (KF) have been applied in this field separately. But the studies show that the former method has good performance ...