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Privacy-preserving distributed learning of radiomics to predict overall survival and HPV status in head and neck cancer.

Scientific reports Mar 11, 2020
A major challenge in radiomics is assembling data from multiple centers. Sharing data between hospitals is restricted by legal and ethical regulations. Distributed learning is a technique, enabling training models on multicenter data without data lea...
Head and Neck Neoplasms Privacy Data Accuracy Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Papillomaviridae Humans Survival Rate Prognosis Deep Learning Retrospective Studies ROC Curve Papillomavirus Infections
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Can a machine-learning model improve the prediction of nodal stage after a positive sentinel lymph node biopsy in breast cancer?

Acta oncologica (Stockholm, Sweden) Mar 9, 2020
The current standard for evaluating axillary nodal burden in clinically node negative breast cancer is sentinel lymph node biopsy (SLNB). However, the accuracy of SLNB to detect nodal stage N2-3 remains debatable. Nomograms can help the decision-mak...
Humans Machine Learning Retrospective Studies Female Aged Aged, 80 and over Axilla Lymph Nodes Adult Regression Analysis Nomograms Breast Neoplasms Sentinel Lymph Node Breast Lymphatic Metastasis Middle Aged Biopsy, Fine-Needle Neoplasm Invasiveness Sentinel Lymph Node Biopsy Tumor Burden Lymph Node Excision ROC Curve Carcinoma, Ductal, Breast Logistic Models
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Early prediction of circulatory failure in the intensive care unit using machine learning.

Nature medicine Mar 9, 2020
Intensive-care clinicians are presented with large quantities of measurements from multiple monitoring systems. The limited ability of humans to process complex information hinders early recognition of patient deterioration, and high numbers of monit...
ROC Curve Models, Theoretical Shock Databases as Topic Intensive Care Units Reproducibility of Results Risk Factors Cohort Studies Time Factors Humans Machine Learning Prognosis
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A Generic Design of Driver Drowsiness and Stress Recognition Using MOGA Optimized Deep MKL-SVM.

Sensors (Basel, Switzerland) Mar 7, 2020
Driver drowsiness and stress are major causes of traffic deaths and injuries, which ultimately wreak havoc on world economic loss. Researchers are in full swing to develop various algorithms for both drowsiness and stress recognition. In contrast to ...
Humans Algorithms Stress, Psychological Automobile Driving ROC Curve Sleepiness Support Vector Machine
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Recognition of Common Non-Normal Walking Actions Based on Relief-F Feature Selection and Relief-Bagging-SVM.

Sensors (Basel, Switzerland) Mar 6, 2020
Action recognition algorithms are widely used in the fields of medical health and pedestrian dead reckoning (PDR). The classification and recognition of non-normal walking actions and normal walking actions are very important for improving the accura...
ROC Curve Support Vector Machine Acceleration Decision Trees Pattern Recognition, Automated Humans Principal Component Analysis Reproducibility of Results Signal Processing, Computer-Assisted Time Factors Walking
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Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer.

Nature communications Mar 6, 2020
Accurate identification of axillary lymph node (ALN) involvement in patients with early-stage breast cancer is important for determining appropriate axillary treatment options and therefore avoiding unnecessary axillary surgery and complications. Her...
Breast Neoplasms Middle Aged Deep Learning Ultrasonography ROC Curve Prognosis Female Aged Humans Neoplasm Staging Aged, 80 and over Prospective Studies Breast Adult Mastectomy Reference Standards Image Processing, Computer-Assisted Preoperative Period Lymph Nodes Elasticity Imaging Techniques Lymphatic Metastasis Lymph Node Excision Axilla
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Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients.

Clinical cancer research : an official journal of the American Association for Cancer Research Mar 5, 2020
PURPOSE: Between 30%-40% of patients with prostate cancer experience disease recurrence following radical prostatectomy. Existing clinical models for recurrence risk prediction do not account for population-based variation in the tumor phenotype, des...
Biomarkers, Tumor Male Middle Aged Risk Assessment Disease Progression Humans Machine Learning Prognosis Survival Rate Prostate-Specific Antigen Neoplasm Recurrence, Local Stromal Cells Black or African American Prostatectomy ROC Curve Image Processing, Computer-Assisted Nomograms Predictive Value of Tests Prostatic Neoplasms
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External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules.

Thorax Mar 5, 2020
BACKGROUND: Estimation of the risk of malignancy in pulmonary nodules detected by CT is central in clinical management. The use of artificial intelligence (AI) offers an opportunity to improve risk prediction. Here we compare the performance of an AI...
Cell Transformation, Neoplastic Incidence Cohort Studies ROC Curve Lung Neoplasms Multiple Pulmonary Nodules Early Detection of Cancer Neoplasm Invasiveness Predictive Value of Tests Area Under Curve Aged Neoplasm Staging Humans Adult Neural Networks, Computer Male Artificial Intelligence Middle Aged Risk Assessment Algorithms Databases, Factual Prognosis Retrospective Studies Female
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A generalizable 29-mRNA neural-network classifier for acute bacterial and viral infections.

Nature communications Mar 4, 2020
Improved identification of bacterial and viral infections would reduce morbidity from sepsis, reduce antibiotic overuse, and lower healthcare costs. Here, we develop a generalizable host-gene-expression-based classifier for acute bacterial and viral ...
Humans Hospital Mortality ROC Curve RNA, Messenger Support Vector Machine Bacterial Infections Adult Virus Diseases Neural Networks, Computer Male Intensive Care Units Acute Disease Middle Aged Female Datasets as Topic Gene Expression Profiling Aged Host-Pathogen Interactions Sepsis Aged, 80 and over
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Predicting Wait Times in Pediatric Ophthalmology Outpatient Clinic Using Machine Learning.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
Patient perceptions of wait time during outpatient office visits can affect patient satisfaction. Providing accurate information about wait times could improve patients' satisfaction by reducing uncertainty. However, these are rarely known about effi...
Linear Models Time-to-Treatment Ophthalmology Humans Supervised Machine Learning Machine Learning Ambulatory Care Facilities Time Factors Pediatrics ROC Curve Patient Satisfaction Models, Statistical
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