AI Medical Compendium Topic:
Decision Trees

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Automatic hip geometric feature extraction in DXA imaging using regional random forest.

Journal of X-ray science and technology
BACKGROUND: Hip fracture is considered one of the salient disability factors across the global population. People with hip fractures are prone to become permanently disabled or die from complications. Although currently the premier determiner, bone m...

Tree-Based Learning of Regulatory Network Topologies and Dynamics with Jump3.

Methods in molecular biology (Clifton, N.J.)
Inference of gene regulatory networks (GRNs) from time series data is a well-established field in computational systems biology. Most approaches can be broadly divided in two families: model-based and model-free methods. These two families are highly...

Unsupervised Gene Network Inference with Decision Trees and Random Forests.

Methods in molecular biology (Clifton, N.J.)
In this chapter, we introduce the reader to a popular family of machine learning algorithms, called decision trees. We then review several approaches based on decision trees that have been developed for the inference of gene regulatory networks (GRNs...

The Application of Machine Learning Techniques in Clinical Drug Therapy.

Current computer-aided drug design
INTRODUCTION: The development of a novel drug is an extremely complicated process that includes the target identification, design and manufacture, and proper therapy of the novel drug, as well as drug dose selection, drug efficacy evaluation, and adv...

Can Machine-learning Techniques Be Used for 5-year Survival Prediction of Patients With Chondrosarcoma?

Clinical orthopaedics and related research
BACKGROUND: Several studies have identified prognostic factors for patients with chondrosarcoma, but there are few studies investigating the accuracy of computationally intensive methods such as machine learning. Machine learning is a type of artific...

High Intraocular Pressure Detection from Frontal Eye Images: A Machine Learning Based Approach.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This paper presents a novel framework to detect the status of intraocular pressure (normal/high) using solely frontal eye image analysis. The framework is based on machine learning approaches to extract six features from frontal eye images. These fea...

A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of the signal of a sleep stage, based on the visual inspection of signa...

Femur segmentation in DXA imaging using a machine learning decision tree.

Journal of X-ray science and technology
BACKGROUND: Accurate measurement of bone mineral density (BMD) in dual-energy X-ray absorptiometry (DXA) is essential for proper diagnosis of osteoporosis. Calculation of BMD requires precise bone segmentation and subtraction of soft tissue absorptio...

Automatic health record review to help prioritize gravely ill Social Security disability applicants.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Every year, thousands of patients die waiting for disability benefits from the Social Security Administration. Some qualify for expedited service under the Compassionate Allowance (CAL) initiative, but CAL software focuses exclusively on i...

Extracting Predictive Indicator for Prognosis of Cerebral Infarction Using Machine Learning Techniques.

Studies in health technology and informatics
Identifying important predicative indicators for prognosis is useful since these factors help for understanding diseases and determining treatments for patients. We extracted important factors for prognosis of cerebral infarction from EHR. We analyze...