AIMC Topic: Machine Learning

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MetaPheno: A critical evaluation of deep learning and machine learning in metagenome-based disease prediction.

Methods (San Diego, Calif.)
The human microbiome plays a number of critical roles, impacting almost every aspect of human health and well-being. Conditions in the microbiome have been linked to a number of significant diseases. Additionally, revolutions in sequencing technology...

Automatic Segmentation of the Prostate on CT Images Using Deep Neural Networks (DNN).

International journal of radiation oncology, biology, physics
PURPOSE: Recent advances in deep neural networks (DNNs) have unlocked opportunities for their application for automatic image segmentation. We have evaluated a DNN-based algorithm for automatic segmentation of the prostate gland on a large cohort of ...

Special issue on machine learning in medical imaging.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma.

Oral oncology
OBJECTIVES: To develop and validate an algorithm to predict occult nodal metastasis in clinically node negative oral cavity squamous cell carcinoma (OCSCC) using machine learning. To compare algorithm performance to a model based on tumor depth of in...

Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: Cross-validation of a machine learning method.

European journal of radiology
PURPOSE: To evaluate the performance of a multi-parametric MRI (mp-MRI)-based radiomics signature for discriminating between clinically significant prostate cancer (csPCa) and insignificant PCa (ciPCa).

Visual Exploration of Neural Document Embedding in Information Retrieval: Semantics and Feature Selection.

IEEE transactions on visualization and computer graphics
Neural embeddings are widely used in language modeling and feature generation with superior computational power. Particularly, neural document embedding - converting texts of variable-length to semantic vector representations - has shown to benefit w...

A machine learning based model for Out of Hospital cardiac arrest outcome classification and sensitivity analysis.

Resuscitation
BACKGROUND: Out-of-hospital cardiac arrest (OHCA) affects nearly 400,000 people each year in the United States of which only 10% survive. Using data from the Cardiac Arrest Registry to Enhance Survival (CARES), and machine learning (ML) techniques, w...

Bayesian Networks for Risk Prediction Using Real-World Data: A Tool for Precision Medicine.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
OBJECTIVE: The fields of medicine and public health are undergoing a data revolution. An increasing availability of data has brought about a growing interest in machine-learning algorithms. Our objective is to present the reader with an introduction ...

Generation of virtual lung single-photon emission computed tomography/CT fusion images for functional avoidance radiotherapy planning using machine learning algorithms.

Journal of medical imaging and radiation oncology
INTRODUCTION: Functional image-guided radiotherapy (RT) planning for normal lung avoidance has recently been introduced. Single-photon emission computed tomography (SPECT)/CT can help identify the functional areas of lungs, but it is associated with ...

Objective Relationship Between Sleep Apnea and Frequency of Snoring Assessed by Machine Learning.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
STUDY OBJECTIVES: Snoring is perceived to be directly proportional to sleep apnea severity, especially obstructive sleep apnea (OSA), but this notion has not been thoroughly and objectively evaluated, despite its popularity in clinical practice. This...