AIMC Topic: Time Factors

Clear Filters Showing 1841 to 1850 of 2001 articles

Network physiology in insomnia patients: Assessment of relevant changes in network topology with interpretable machine learning models.

Chaos (Woodbury, N.Y.)
Network physiology describes the human body as a complex network of interacting organ systems. It has been applied successfully to determine topological changes in different sleep stages. However, the number of network links can quickly grow above th...

The influence of place and time on lexical behavior: A distributional analysis.

Behavior research methods
We measured and documented the influence of corpus effects on lexical behavior. Specifically, we used a corpus of over 26,000 fiction books to show that computational models of language trained on samples of language (i.e., subcorpora) representative...

Optimization of treatment strategy by using a machine learning model to predict survival time of patients with malignant glioma after radiotherapy.

Journal of radiation research
The purpose of this study was to predict the survival time of patients with malignant glioma after radiotherapy with high accuracy by considering additional clinical factors and optimize the prescription dose and treatment duration for individual pat...

Deep convolutional neural network for reduction of contrast-enhanced region on CT images.

Journal of radiation research
This study aims to produce non-contrast computed tomography (CT) images using a deep convolutional neural network (CNN) for imaging. Twenty-nine patients were selected. CT images were acquired without and with a contrast enhancement medium. The trans...

[Automatic keyword retrieval from clinical texts: an application of natural language processing to massive data of Chilean suspected diagnosis].

Revista medica de Chile
BACKGROUND: Free-text imposes a challenge in health data analysis since the lack of structure makes the extraction and integration of information difficult, particularly in the case of massive data. An appropriate machine-interpretation of electronic...

Natural Language Processing Approaches to Detect the Timeline of Metastatic Recurrence of Breast Cancer.

JCO clinical cancer informatics
PURPOSE: Electronic medical records (EMRs) and population-based cancer registries contain information on cancer outcomes and treatment, yet rarely capture information on the timing of metastatic cancer recurrence, which is essential to understand can...

Fast and Precise Hippocampus Segmentation Through Deep Convolutional Neural Network Ensembles and Transfer Learning.

Neuroinformatics
Automatic segmentation of the hippocampus from 3D magnetic resonance imaging mostly relied on multi-atlas registration methods. In this work, we exploit recent advances in deep learning to design and implement a fully automatic segmentation method, o...

Predicting 90-Day and 1-Year Mortality in Spinal Metastatic Disease: Development and Internal Validation.

Neurosurgery
BACKGROUND: Increasing prevalence of metastatic disease has been accompanied by increasing rates of surgical intervention. Current tools have poor to fair predictive performance for intermediate (90-d) and long-term (1-yr) mortality.

Machine Learning in Modeling High School Sport Concussion Symptom Resolve.

Medicine and science in sports and exercise
INTRODUCTION: Concussion prevalence in sport is well recognized, so too is the challenge of clinical and return-to-play management for an injury with an inherent indeterminant time course of resolve. A clear, valid insight into the anticipated resolu...