AIMC Topic: Time Factors

Clear Filters Showing 1821 to 1830 of 2001 articles

Utilizing Machine Learning Methods for Preoperative Prediction of Postsurgical Mortality and Intensive Care Unit Admission.

Annals of surgery Dec 1, 2020
OBJECTIVE: To compare the performance of machine learning models against the traditionally derived Combined Assessment of Risk Encountered in Surgery (CARES) model and the American Society of Anaesthesiologists-Physical Status (ASA-PS) in the predict...
Machine Learning Prognosis Retrospective Studies Intensive Care Units Female Humans Middle Aged Risk Assessment Postoperative Complications Time Factors Preoperative Period Hospitalization Aged Adult Male
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A Time-Phased Machine Learning Model for Real-Time Prediction of Sepsis in Critical Care.

Critical care medicine Oct 1, 2020
OBJECTIVES: As a life-threatening condition, sepsis is one of the major public health issues worldwide. Early prediction can improve sepsis outcomes with appropriate interventions. With the PhysioNet/Computing in Cardiology Challenge 2019, we aimed t...
Critical Care Early Diagnosis Vital Signs Humans Machine Learning Retrospective Studies Intensive Care Units Reproducibility of Results Severity of Illness Index Sepsis Time Factors
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New artificial intelligence prediction model using serial prothrombin time international normalized ratio measurements in atrial fibrillation patients on vitamin K antagonists: GARFIELD-AF.

European heart journal. Cardiovascular pharmacotherapy Sep 1, 2020
AIMS: Most clinical risk stratification models are based on measurement at a single time-point rather than serial measurements. Artificial intelligence (AI) is able to predict one-dimensional outcomes from multi-dimensional datasets. Using data from ...
Databases, Factual Prospective Studies Female Reproducibility of Results Risk Factors Humans Neural Networks, Computer Time Factors Treatment Outcome Predictive Value of Tests Anticoagulants Blood Coagulation Aged Aged, 80 and over Male Risk Assessment Drug Therapy, Computer-Assisted Administration, Oral Atrial Fibrillation Vitamin K Hemorrhage International Normalized Ratio Prothrombin Time Drug Monitoring Registries Stroke
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Comparison between atlas and convolutional neural network based automatic segmentation of multiple organs at risk in non-small cell lung cancer.

Medicine Aug 21, 2020
Delineation of organs at risk (OARs) is important but time consuming for radiotherapy planning. Automatic segmentation of OARs based on convolutional neural network (CNN) has been established for lung cancer patients at our institution. The aim of th...
Organs at Risk Radiotherapy Planning, Computer-Assisted Spinal Cord Lung Neoplasms Carcinoma, Non-Small-Cell Lung Liver Esophagus Heart Retrospective Studies Reproducibility of Results Lung Time Factors Humans Neural Networks, Computer
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Accurate deep-learning estimation of chlorophyll-a concentration from the spectral particulate beam-attenuation coefficient.

Optics express Aug 3, 2020
Different techniques exist for determining chlorophyll-a concentration as a proxy of phytoplankton abundance. In this study, a novel method based on the spectral particulate beam-attenuation coefficient (c) was developed to estimate chlorophyll-a con...
Databases as Topic Chlorophyll A Neural Networks, Computer Reproducibility of Results Deep Learning Time Factors Bias Spectrum Analysis
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Identifying tracé alternant activity in neonatal EEG using an inter-burst detection approach.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
Electroencephalography (EEG) is an important clinical tool for reviewing sleep-wake cycling in neonates in intensive care. Tracé alternant (TA)-a characteristic pattern of EEG activity during quiet sleep in term neonates-is defined by alternating per...
Mental Processes Sleep, Slow-Wave Humans Infant, Newborn Time Factors Support Vector Machine Electroencephalography
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Can Predictive Modeling Tools Identify Patients at High Risk of Prolonged Opioid Use After ACL Reconstruction?

Clinical orthopaedics and related research Jul 1, 2020
BACKGROUND: Machine-learning methods such as the Bayesian belief network, random forest, gradient boosting machine, and decision trees have been used to develop decision-support tools in other clinical settings. Opioid abuse is a problem among civili...
Humans Machine Learning Decision Support Techniques Databases, Factual Retrospective Studies Clinical Decision-Making Female Opioid-Related Disorders Drug Administration Schedule Arthroscopy Anterior Cruciate Ligament Injuries Male Reproducibility of Results Anterior Cruciate Ligament Reconstruction Risk Assessment Narcotic Antagonists Time Factors Risk Factors Military Medicine Treatment Outcome Young Adult Pain, Postoperative Adult
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Effect of a Machine Learning-Derived Early Warning System for Intraoperative Hypotension vs Standard Care on Depth and Duration of Intraoperative Hypotension During Elective Noncardiac Surgery: The HYPE Randomized Clinical Trial.

JAMA Mar 17, 2020
IMPORTANCE: Intraoperative hypotension is associated with increased morbidity and mortality. A machine learning-derived early warning system to predict hypotension shortly before it occurs has been developed and validated.
Hypotension Intraoperative Complications Operative Time Humans Anesthesia, General Aged Elective Surgical Procedures Machine Learning Female Male Tertiary Care Centers Middle Aged Time Factors Blood Pressure Determination
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Application of Convolutional Neural Networks for Detection of Superficial Nonampullary Duodenal Epithelial Tumors in Esophagogastroduodenoscopic Images.

Clinical and translational gastroenterology Mar 1, 2020
OBJECTIVES: A superficial nonampullary duodenal epithelial tumor (SNADET) is defined as a mucosal or submucosal sporadic tumor of the duodenum that does not arise from the papilla of Vater. SNADETs rarely metastasize to the lymph nodes, and most can ...
Humans Female Aged Datasets as Topic Deep Learning Neoplasms, Glandular and Epithelial Time Factors Aged, 80 and over Endoscopy, Digestive System Adult Male Duodenal Neoplasms Image Processing, Computer-Assisted Middle Aged Duodenum Tumor Burden
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Convolutional neural network-based approach to estimate bulk optical properties in diffuse optical tomography.

Applied optics Feb 10, 2020
Deep learning has been actively investigated for various applications such as image classification, computer vision, and regression tasks, and it has shown state-of-the-art performance. In diffuse optical tomography (DOT), the accurate estimation of ...
Tomography, Optical Image Processing, Computer-Assisted Humans Models, Theoretical Computer Simulation Breast Deep Learning Light Time Factors Scattering, Radiation
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