AIMC Topic: Predictive Value of Tests

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Data-efficient deep learning of radiological image data for outcome prediction after endovascular treatment of patients with acute ischemic stroke.

Computers in biology and medicine Oct 22, 2019
Treatment selection is becoming increasingly more important in acute ischemic stroke patient care. Clinical variables and radiological image biomarkers (old age, pre-stroke mRS, NIHSS, occlusion location, ASPECTS, among others) have an important role...
Computed Tomography Angiography Humans Cerebral Angiography Predictive Value of Tests Neural Networks, Computer Registries Prospective Studies Aged Stroke Female Aged, 80 and over Brain Ischemia Male Endovascular Procedures Middle Aged Postoperative Complications
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A speckle-tracking strain-based artificial neural network model to differentiate cardiomyopathy type.

Scandinavian cardiovascular journal : SCJ Oct 18, 2019
In heart failure, invasive angiography is often employed to differentiate ischaemic from non-ischaemic cardiomyopathy. We aim to examine the predictive value of echocardiographic strain features alone and in combination with other features to differ...
Stroke Volume Echocardiography Middle Aged Diagnosis, Computer-Assisted Diagnosis, Differential Female Reproducibility of Results Aged Male Ventricular Function, Left Predictive Value of Tests Heart Failure Image Interpretation, Computer-Assisted Cardiomyopathies Humans Neural Networks, Computer Prognosis Retrospective Studies
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Investigating Risk Factors and Predicting Complications in Deep Brain Stimulation Surgery with Machine Learning Algorithms.

World neurosurgery Oct 18, 2019
BACKGROUND: Deep brain stimulation (DBS) surgery is an option for patients experiencing medically resistant neurologic symptoms. DBS complications are rare; finding significant predictors requires a large number of surgeries. Machine learning algorit...
Humans Predictive Value of Tests Risk Factors Registries Machine Learning Aged Deep Brain Stimulation Algorithms Male Middle Aged Retrospective Studies Postoperative Complications Female
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Predicting diabetes second-line therapy initiation in the Australian population via time span-guided neural attention network.

PloS one Oct 18, 2019
INTRODUCTION: The first line of treatment for people with Diabetes mellitus is metformin. However, over the course of the disease metformin may fail to achieve appropriate glycemic control, and a second-line therapy may become necessary. In this pape...
Male Metformin Humans Neural Networks, Computer Diabetes Mellitus Machine Learning Australia Female Predictive Value of Tests Models, Biological
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A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preoperative diffusion-weighted imaging.

European radiology experimental Oct 17, 2019
BACKGROUND: To develop a supervised machine learning (ML) algorithm predicting above- versus below-median overall survival (OS) from diffusion-weighted imaging-derived radiomic features in patients with pancreatic ductal adenocarcinoma (PDAC).
Models, Theoretical Pancreatic Neoplasms Predictive Value of Tests Carcinoma, Pancreatic Ductal Preoperative Period Diffusion Magnetic Resonance Imaging Humans Machine Learning Retrospective Studies Survival Rate
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Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ.

Computers in biology and medicine Oct 16, 2019
PURPOSE: To determine whether deep learning-based algorithms applied to breast MR images can aid in the prediction of occult invasive disease following the diagnosis of ductal carcinoma in situ (DCIS) by core needle biopsy.
Humans Retrospective Studies Female Support Vector Machine Magnetic Resonance Imaging Predictive Value of Tests Aged Carcinoma, Ductal, Breast Adult Biopsy, Fine-Needle Breast Neoplasms Middle Aged Deep Learning
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Extreme Gradient Boosting Model Has a Better Performance in Predicting the Risk of 90-Day Readmissions in Patients with Ischaemic Stroke.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association Oct 16, 2019
OBJECT: Ischemic stroke readmission within 90 days of hospital discharge is an important quality of care metric. The readmission rates of ischemic stroke patients are usually higher than those of patients with other chronic diseases. Our aim was to i...
Recurrence Predictive Value of Tests Stroke Decision Support Techniques Patient Readmission Retrospective Studies Brain Ischemia Female Aged Reproducibility of Results Humans Male Risk Factors Machine Learning Middle Aged Risk Assessment Time Factors
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Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning.

Medical image analysis Oct 15, 2019
CT Perfusion (CTP) imaging has gained importance in the diagnosis of acute stroke. Conventional perfusion analysis performs a deconvolution of the measurements and thresholds the perfusion parameters to determine the tissue status. We pursue a data-d...
Predictive Value of Tests Radiographic Image Interpretation, Computer-Assisted Cerebral Infarction Datasets as Topic Cerebral Angiography Male Middle Aged Deep Learning Humans Multicenter Studies as Topic Tomography, X-Ray Computed Randomized Controlled Trials as Topic Female Aged
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Recurrent Neural Networks for Early Detection of Heart Failure From Longitudinal Electronic Health Record Data: Implications for Temporal Modeling With Respect to Time Before Diagnosis, Data Density, Data Quantity, and Data Type.

Circulation. Cardiovascular quality and outcomes Oct 15, 2019
BACKGROUND: We determined the impact of data volume and diversity and training conditions on recurrent neural network methods compared with traditional machine learning methods.
Neural Networks, Computer Machine Learning Electronic Health Records Female Reproducibility of Results Humans Early Diagnosis Smoking Alcohol Drinking California Vital Signs Time Factors Primary Health Care Risk Factors Longitudinal Studies Male Predictive Value of Tests Heart Failure Diagnosis, Computer-Assisted Incidence
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Atrial Fibrillation Burden Signature and Near-Term Prediction of Stroke: A Machine Learning Analysis.

Circulation. Cardiovascular quality and outcomes Oct 15, 2019
BACKGROUND: Atrial fibrillation (AF) increases the risk of stroke 5-fold and there is rising interest to determine if AF severity or burden can further risk stratify these patients, particularly for near-term events. Using continuous remote monitorin...
Male Middle Aged Risk Assessment Diagnosis, Computer-Assisted Time Factors Electronic Health Records Female Risk Factors Aged Aged, 80 and over Atrial Fibrillation Proof of Concept Study Humans Logistic Models Administrative Claims, Healthcare Neural Networks, Computer United States Telemetry Machine Learning Predictive Value of Tests Veterans Health Services Prognosis Signal Processing, Computer-Assisted Retrospective Studies Stroke
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