AIMC Topic: Risk Assessment

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Deep learning-based survival prediction for multiple cancer types using histopathology images.

PloS one Jun 17, 2020
Providing prognostic information at the time of cancer diagnosis has important implications for treatment and monitoring. Although cancer staging, histopathological assessment, molecular features, and clinical variables can provide useful prognostic ...
Survival Analysis Humans Male Datasets as Topic Prognosis Female Middle Aged Risk Factors Risk Assessment Adult Neoplasms Deep Learning Image Processing, Computer-Assisted Age Factors Neoplasm Staging Feasibility Studies Sex Factors
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Evaluation of the ACS NSQIP surgical risk calculator in patients undergoing pelvic organ prolapse surgery.

International urogynecology journal Jun 16, 2020
INTRODUCTION AND HYPOTHESIS: The purpose of this study was to evaluate the accuracy of the American College of Surgeons National Surgery Quality Improvement Program (ACS NSQIP) surgical risk calculator in predicting postoperative complications in pat...
Pelvic Organ Prolapse Risk Factors Risk Assessment Postoperative Complications Humans Robotics Retrospective Studies Quality Improvement
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Identification of Potential PBT/POP-Like Chemicals by a Deep Learning Approach Based on 2D Structural Features.

Environmental science & technology Jun 16, 2020
Identifying potential persistent organic pollutants (POPs) and persistent, bioaccumulative, and toxic (PBT) substances from industrial chemical inventories are essential for chemical risk assessment, management, and pollution control. Inspired by the...
Risk Assessment Deep Learning Environmental Monitoring Environmental Pollution Environmental Pollutants
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A Support Vector Machine Model Predicting the Risk of Duodenal Cancer in Patients with Familial Adenomatous Polyposis at the Transcript Levels.

BioMed research international Jun 16, 2020
OBJECTIVE: Familial adenomatous polyposis (FAP) is one major type of inherited duodenal cancer. The estimate of duodenal cancer risk in patients with FAP is critical for selecting the optimal treatment strategy.
Support Vector Machine Humans Databases, Genetic Risk Assessment Metabolic Networks and Pathways Transcriptome Adenomatous Polyposis Coli Computational Biology Duodenal Neoplasms
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Prediction of Soil Adsorption Coefficient in Pesticides Using Physicochemical Properties and Molecular Descriptors by Machine Learning Models.

Environmental toxicology and chemistry Jun 15, 2020
The soil adsorption coefficient (K ) plays an important role in environmental risk assessment of pesticide registration. Based on this risk assessment, applied and registered pesticides can be allowed in the European Union. Almost 1 yr is required to...
Soil Pollutants Pesticides Linear Models Chemical Phenomena Machine Learning Quantitative Structure-Activity Relationship Soil Risk Assessment Adsorption Decision Trees
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Machine Learning of 12-Lead QRS Waveforms to Identify Cardiac Resynchronization Therapy Patients With Differential Outcomes.

Circulation. Arrhythmia and electrophysiology Jun 14, 2020
BACKGROUND: Cardiac resynchronization therapy (CRT) improves heart failure outcomes but has significant nonresponse rates, highlighting limitations in ECG selection criteria: QRS duration (QRSd) ≥150 ms and subjective labeling of left bundle branch b...
Humans Retrospective Studies Female Risk Factors Aged Male Ventricular Function, Left Stroke Volume Middle Aged Heart Failure Cardiac Resynchronization Therapy Heart-Assist Devices Treatment Outcome Heart Transplantation Risk Assessment Unsupervised Machine Learning Bundle-Branch Block Disease Progression Recovery of Function Diagnosis, Computer-Assisted Predictive Value of Tests Time Factors Signal Processing, Computer-Assisted Electrocardiography
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Preprocedure Application of Machine Learning and Mechanistic Simulations Predicts Likelihood of Paroxysmal Atrial Fibrillation Recurrence Following Pulmonary Vein Isolation.

Circulation. Arrhythmia and electrophysiology Jun 14, 2020
BACKGROUND: Pulmonary vein isolation (PVI) is an effective treatment strategy for patients with atrial fibrillation (AF), but many experience AF recurrence and require repeat ablation procedures. The goal of this study was to develop and evaluate a m...
Meglumine Predictive Value of Tests Atrial Fibrillation Heart Rate Catheter Ablation Proof of Concept Study Humans Organometallic Compounds Recurrence Machine Learning Risk Factors Diagnosis, Computer-Assisted Retrospective Studies Models, Cardiovascular Aged Female Treatment Outcome Patient-Specific Modeling Male Reproducibility of Results Contrast Media Pulmonary Veins Middle Aged Magnetic Resonance Imaging Risk Assessment Action Potentials
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Predicting direct hepatocyte toxicity in humans by combining high-throughput imaging of HepaRG cells and machine learning-based phenotypic profiling.

Archives of toxicology Jun 12, 2020
Accurate prediction of drug- and chemical-induced hepatotoxicity remains to be a problem for pharmaceutical companies as well as other industries and regulators. The goal of the current study was to develop an in vitro/in silico method for the rapid ...
Image Processing, Computer-Assisted Hep G2 Cells Dose-Response Relationship, Drug DNA Damage Microscopy, Fluorescence Humans Machine Learning Phenotype Risk Assessment Transcription Factor RelA Chemical and Drug Induced Liver Injury Primary Cell Culture Actin Cytoskeleton Proto-Oncogene Mas Cellular Senescence Cell Death Hepatocytes High-Throughput Screening Assays Toxicity Tests
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Using Artificial Intelligence to Predict Surgical Shunts in Men with Ischemic Priapism.

The Journal of urology Jun 9, 2020
PURPOSE: Ischemic priapism is a urological emergency that requires prompt intervention to preserve erectile function. Characteristics that influence escalation to surgical intervention remain unclear. We identified factors and developed machine learn...
Likelihood Functions Penile Erection Emergency Treatment Priapism Adolescent Young Adult Anastomosis, Surgical Urologic Surgical Procedures, Male Time Factors Adult Phenylephrine Humans Treatment Outcome Male Paracentesis Models, Statistical Middle Aged Erectile Dysfunction Machine Learning Emergency Service, Hospital Retrospective Studies Aged Aged, 80 and over Penis Risk Assessment
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A novel model for predicting the outcome of intracerebral hemorrhage: Based on 1186 Patients.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association Jun 8, 2020
OBJECTIVE: To establish a model for predicting the outcome according to the clinical and computed tomography(CT) image data of patients with intracerebral hemorrhage(ICH).
Humans Decision Support Techniques Prognosis Middle Aged Risk Assessment China Time Factors Nomograms Support Vector Machine Retrospective Studies Tomography, X-Ray Computed Cerebral Hemorrhage Female Predictive Value of Tests Risk Factors Male
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