AIMC Topic: Recurrence

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Recurrent disease progression networks for modelling risk trajectory of heart failure.

PloS one Jan 6, 2021
MOTIVATION: Recurrent neural networks (RNN) are powerful frameworks to model medical time series records. Recent studies showed improved accuracy of predicting future medical events (e.g., readmission, mortality) by leveraging large amount of high-di...
Risk Factors Disease Progression Heart Failure Heart Defects, Congenital Recurrence Humans Neural Networks, Computer Databases, Factual
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Structural Brain Network Abnormalities and the Probability of Seizure Recurrence After Epilepsy Surgery.

Neurology Dec 22, 2020
OBJECTIVE: We assessed preoperative structural brain networks and clinical characteristics of patients with drug-resistant temporal lobe epilepsy (TLE) to identify correlates of postsurgical seizure recurrences.
Epilepsy, Temporal Lobe Recurrence Drug Resistant Epilepsy Diffusion Magnetic Resonance Imaging Magnetic Resonance Imaging Postoperative Period Preoperative Period Neural Pathways Anterior Temporal Lobectomy Machine Learning Brain Female Adult Case-Control Studies Treatment Outcome Support Vector Machine Male Middle Aged Humans
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The prognostic role of end-of-treatment FDG-PET/CT in diffuse large B cell lymphoma: a pilot study application of neural networks to predict time-to-event.

Annals of nuclear medicine Oct 22, 2020
PURPOSE: To evaluate the prognostic role of end-of-treatment (EoT) FDG-PET/CT parameters in diffuse large B cell lymphoma (DLBCL), and then to explore a pilot application of Neural Networks (NN) in predicting time-to-relapse.
Humans Neural Networks, Computer Prognosis Retrospective Studies Aged, 80 and over Young Adult Adult Female Aged Time Factors Positron Emission Tomography Computed Tomography Fluorodeoxyglucose F18 Lymphoma, Large B-Cell, Diffuse Recurrence Male Middle Aged Pilot Projects
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Recurrent Hemoptysis After Bronchial Artery Embolization: Prediction Using a Nomogram and Artificial Neural Network Model.

AJR. American journal of roentgenology Oct 14, 2020
The purpose of this study was to develop an effective nomogram and artificial neural network (ANN) model for predicting recurrent hemoptysis after bronchial artery embolization (BAE). The institutional ethics review boards of the two participating ...
Hemoptysis Retrospective Studies Female Aged Male Embolization, Therapeutic Recurrence Bronchial Arteries Middle Aged Nomograms Humans Neural Networks, Computer
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Predicting the first smoking lapse during a quit attempt: A machine learning approach.

Drug and alcohol dependence Oct 11, 2020
BACKGROUND: Just-in-time adaptive interventions (JITAI) aim to prevent smoking lapse using tailored support delivered via mobile technology in the moments when it is most needed. Effective smoking cessation JITAI rely on the development of accurate d...
Ecological Momentary Assessment Tobacco Smoking Smoking Smokers Humans Recurrence Smoking Cessation Machine Learning Female Adult Smartphone Tobacco Products Motivation Male Middle Aged
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The Clinical Application of the Deep Learning Technique for Predicting Trigger Origins in Patients With Paroxysmal Atrial Fibrillation With Catheter Ablation.

Circulation. Arrhythmia and electrophysiology Oct 6, 2020
BACKGROUND: Non-pulmonary vein (NPV) trigger has been reported as an important predictor of recurrence post-atrial fibrillation ablation. Elimination of NPV triggers can reduce the recurrence of postablation atrial fibrillation. Deep learning was app...
Reproducibility of Results Risk Factors Aged Adult Humans Retrospective Studies Female Time Factors Treatment Outcome Action Potentials Predictive Value of Tests Radiographic Image Interpretation, Computer-Assisted Male Middle Aged Risk Assessment Deep Learning Computed Tomography Angiography Phlebography Pulmonary Veins Atrial Fibrillation Heart Rate Catheter Ablation Recurrence
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Predicting Early Warning Signs of Psychotic Relapse From Passive Sensing Data: An Approach Using Encoder-Decoder Neural Networks.

JMIR mHealth and uHealth Aug 31, 2020
BACKGROUND: Schizophrenia spectrum disorders (SSDs) are chronic conditions, but the severity of symptomatic experiences and functional impairments vacillate over the course of illness. Developing unobtrusive remote monitoring systems to detect early ...
Adult Male Middle Aged Humans Smartphone Neural Networks, Computer Female Young Adult Schizophrenia Recurrence Text Messaging
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Machine learning for RNA sequencing-based intrinsic subtyping of breast cancer.

Scientific reports Aug 21, 2020
Stratification of breast cancer (BC) into molecular subtypes by multigene expression assays is of demonstrated clinical utility. In principle, global RNA-sequencing (RNA-seq) should enable reconstructing existing transcriptional classifications of BC...
Machine Learning Prognosis Humans Female Breast Neoplasms Biomarkers, Tumor Logistic Models Sequence Analysis, RNA Estrogens Carcinoma Datasets as Topic Receptors, Estrogen Recurrence Neoplasms, Hormone-Dependent
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Robotic Versus Laparoscopic Approach to Hiatal Hernia Repair: Results After 7 Years of Robotic Experience.

The American surgeon Aug 18, 2020
INTRODUCTION: Robotic hiatal hernia repair offers potential advantages over traditional laparoscopy, most notably enhanced visualization, improved ergonomics, and articulating instruments. The clinical outcomes, however, have not been adequately eval...
Humans Retrospective Studies Length of Stay Time Factors Laparoscopy Recurrence Hernia, Hiatal Herniorrhaphy Treatment Outcome Follow-Up Studies Operative Time Robotics
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ZiMM: A deep learning model for long term and blurry relapses with non-clinical claims data.

Journal of biomedical informatics Aug 17, 2020
This paper considers the problems of modeling and predicting a long-term and "blurry" relapse that occurs after a medical act, such as a surgery. We do not consider a short-term complication related to the act itself, but a long-term relapse that cli...
Recurrence Humans Databases, Factual Electronic Health Records Deep Learning
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