AIMC Topic: Area Under Curve

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Predicting prostate tumour location from multiparametric MRI using Gaussian kernel support vector machines: a preliminary study.

Australasian physical & engineering sciences in medicine
The performance of a support vector machine (SVM) algorithm was investigated to predict prostate tumour location using multi-parametric MRI (mpMRI) data. The purpose was to obtain information of prostate tumour location for the implementation of bio-...

Neural network prediction of severe lower intestinal bleeding and the need for surgical intervention.

The Journal of surgical research
BACKGROUND: The prognosis for patients with severe acute lower intestinal bleeding (ALIB) may be assessed by complex artificial neural networks (ANNs) or user-friendly regression-based models. Comparisons between these modalities are limited, and pre...

Stable feature selection based on the ensemble L -norm support vector machine for biomarker discovery.

BMC genomics
BACKGROUND: Lately, biomarker discovery has become one of the most significant research issues in the biomedical field. Owing to the presence of high-throughput technologies, genomic data, such as microarray data and RNA-seq, have become widely avail...

Development of a Machine Learning Algorithm for the Surveillance of Autism Spectrum Disorder.

PloS one
The Autism and Developmental Disabilities Monitoring (ADDM) Network conducts population-based surveillance of autism spectrum disorder (ASD) among 8-year old children in multiple US sites. To classify ASD, trained clinicians review developmental eval...

ADAMTS-3, -13, -16, and -19 levels in patients with habitual abortion.

The Kaohsiung journal of medical sciences
A disintegrin-like and metalloproteinase domain with thrombospondin-type 1 motifs (ADAMTS) protein superfamily includes 19 secreted metalloproteases. Proteolytic substrates of ADAMTS enzymes have been linked to reproductive function. The aim of this ...

Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia.

Scientific reports
Neuroimaging-based models contribute to increasing our understanding of schizophrenia pathophysiology and can reveal the underlying characteristics of this and other clinical conditions. However, the considerable variability in reported neuroimaging ...

A Model-Based Machine Learning Approach to Probing Autonomic Regulation From Nonstationary Vital-Sign Time Series.

IEEE journal of biomedical and health informatics
Physiological variables, such as heart rate (HR), blood pressure (BP) and respiration (RESP), are tightly regulated and coupled under healthy conditions, and a break-down in the coupling has been associated with aging and disease. We present an appro...

Development of Type 2 Diabetes Mellitus Phenotyping Framework Using Expert Knowledge and Machine Learning Approach.

Journal of diabetes science and technology
BACKGROUND: Phenotyping is an automated technique that can be used to distinguish patients based on electronic health records. To improve the quality of medical care and advance type 2 diabetes mellitus (T2DM) research, the demand for T2DM phenotypin...

Large-scale identification of patients with cerebral aneurysms using natural language processing.

Neurology
OBJECTIVE: To use natural language processing (NLP) in conjunction with the electronic medical record (EMR) to accurately identify patients with cerebral aneurysms and their matched controls.

Early Detection of Heart Failure Using Electronic Health Records: Practical Implications for Time Before Diagnosis, Data Diversity, Data Quantity, and Data Density.

Circulation. Cardiovascular quality and outcomes
BACKGROUND: Using electronic health records data to predict events and onset of diseases is increasingly common. Relatively little is known, although, about the tradeoffs between data requirements and model utility.