AI Medical Compendium Topic:
Models, Statistical

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Cell ontology in an age of data-driven cell classification.

BMC bioinformatics
BACKGROUND: Data-driven cell classification is becoming common and is now being implemented on a massive scale by projects such as the Human Cell Atlas. The scale of these efforts poses a challenge. How can the results be made searchable and accessib...

A functional supervised learning approach to the study of blood pressure data.

Statistics in medicine
In this work, a functional supervised learning scheme is proposed for the classification of subjects into normotensive and hypertensive groups, using solely the 24-hour blood pressure data, relying on the concepts of Fréchet mean and Fréchet variance...

Epileptic Seizures Prediction Using Machine Learning Methods.

Computational and mathematical methods in medicine
Epileptic seizures occur due to disorder in brain functionality which can affect patient's health. Prediction of epileptic seizures before the beginning of the onset is quite useful for preventing the seizure by medication. Machine learning technique...

Automatic hemolysis identification on aligned dual-lighting images of cultured blood agar plates.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The recent introduction of Full Laboratory Automation systems in clinical microbiology opens to the availability of streams of high definition images representing bacteria culturing plates. This creates new opportunities to ...

A Correction Method of a Binary Classifier Applied to Multi-Label Pairwise Models.

International journal of neural systems
In the paper, the problem of multi-label (ML) classification using the label-pairwise (LPW) scheme is addressed. For this approach, the method of correction of binary classifiers which constitute the LPW ensemble is proposed. The correction is based ...

The identification of high potential archers based on fitness and motor ability variables: A Support Vector Machine approach.

Human movement science
Support Vector Machine (SVM) has been shown to be an effective learning algorithm for classification and prediction. However, the application of SVM for prediction and classification in specific sport has rarely been used to quantify/discriminate low...

Estimating causal effects for survival (time-to-event) outcomes by combining classification tree analysis and propensity score weighting.

Journal of evaluation in clinical practice
RATIONALE, AIMS AND OBJECTIVES: A common approach to assessing treatment effects in nonrandomized studies with time-to-event outcomes is to estimate propensity scores and compute weights using logistic regression, test for covariate balance, and then...

Classification of cancer cells using computational analysis of dynamic morphology.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Detection of metastatic tumor cells is important for early diagnosis and staging of cancer. However, such cells are exceedingly difficult to detect from blood or biopsy samples at the disease onset. It is reported that cance...

A method to combine target volume data from 3D and 4D planned thoracic radiotherapy patient cohorts for machine learning applications.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: The gross tumour volume (GTV) is predictive of clinical outcome and consequently features in many machine-learned models. 4D-planning, however, has prompted substitution of the GTV with the internal gross target volume (iGTV)....

Utilizing knowledge base of amino acids structural neighborhoods to predict protein-protein interaction sites.

BMC bioinformatics
BACKGROUND: Protein-protein interactions (PPI) play a key role in an investigation of various biochemical processes, and their identification is thus of great importance. Although computational prediction of which amino acids take part in a PPI has b...