AIMC Topic: Patient Selection

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Selecting Children with Vesicoureteral Reflux Who are Most Likely to Benefit from Antibiotic Prophylaxis: Application of Machine Learning to RIVUR.

The Journal of urology Dec 8, 2020
PURPOSE: Continuous antibiotic prophylaxis reduces the risk of recurrent urinary tract infection by 50% in children with vesicoureteral reflux. However, there may be subgroups in whom continuous antibiotic prophylaxis could be used more selectively. ...
Humans Machine Learning Urinary Tract Infections Patient Selection Vesico-Ureteral Reflux Antibiotic Prophylaxis Female Male Infant Predictive Value of Tests
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Machine Learning-Guided Adjuvant Treatment of Head and Neck Cancer.

JAMA network open Nov 2, 2020
IMPORTANCE: Postoperative chemoradiation is the standard of care for cancers with positive margins or extracapsular extension, but the benefit of chemotherapy is unclear for patients with other intermediate risk features.
Humans Neural Networks, Computer Oropharyngeal Neoplasms Hypopharyngeal Neoplasms Proportional Hazards Models Lymph Nodes Radiotherapy, Adjuvant Male Squamous Cell Carcinoma of Head and Neck Mouth Neoplasms Deep Learning Neoplasm Staging Patient Selection Machine Learning Cohort Studies Neoplasm Grading Tumor Burden Retrospective Studies Logistic Models Laryngeal Neoplasms Chemoradiotherapy, Adjuvant Female Otorhinolaryngologic Surgical Procedures Aged
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Deep Learning and Multivariable Models Select EVAR Patients for Short-Stay Discharge.

Vascular and endovascular surgery Sep 10, 2020
OBJECTIVES: We sought to develop a prediction score with data from the Vascular Quality Initiative (VQI) EVAR in efforts to assist endovascular specialists in deciding whether or not a patient is appropriate for short-stay discharge.
Smartphone Mobile Applications Treatment Outcome Patient Discharge Risk Assessment Deep Learning Length of Stay Time Factors Health Services Research Aortic Aneurysm Patient Selection Multivariate Analysis Endovascular Procedures Enhanced Recovery After Surgery Decision Support Techniques Databases, Factual Clinical Decision-Making Female Risk Factors Aged Aged, 80 and over Male Humans
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Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data.

Scientific reports May 21, 2020
Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a...
Humans Machine Learning Disease Progression Models, Theoretical Patient Selection Cartilage Osteoarthritis, Knee Bone and Bones
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Cohort selection for clinical trials using multiple instance learning.

Journal of biomedical informatics May 1, 2020
Identifying patients eligible for clinical trials using electronic health records (EHRs) is a challenging task usually requiring a comprehensive analysis of information stored in multiple EHRs of a patient. The goal of this study is to investigate di...
Cohort Studies Patient Selection Motivation Humans Machine Learning Electronic Health Records
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ASO Author Reflections: Machine Learning Strategies Can Aid Patient Selection in Microvascular Breast Reconstruction.

Annals of surgical oncology Mar 21, 2020
Humans Mammaplasty Machine Learning Patient Selection
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Real world study for the concordance between IBM Watson for Oncology and clinical practice in advanced non-small cell lung cancer patients at a lung cancer center in China.

Thoracic cancer Mar 19, 2020
BACKGROUND: IBM Watson for Oncology (WFO) provides physicians with evidence-based treatment options. This study was designed to explore the concordance of the suggested therapeutic regimen for advanced non-small cell lung (NSCLC) cancer patients betw...
Follow-Up Studies Retrospective Studies Humans Adenocarcinoma of Lung Clinical Decision-Making Patient Selection Decision Support Systems, Clinical Survival Rate Artificial Intelligence Female Practice Guidelines as Topic Male Lung Neoplasms Prognosis Carcinoma, Squamous Cell Evidence-Based Medicine United States Middle Aged Carcinoma, Non-Small-Cell Lung China
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Natural Language Processing for Mimicking Clinical Trial Recruitment in Critical Care: A Semi-Automated Simulation Based on the LeoPARDS Trial.

IEEE journal of biomedical and health informatics Mar 9, 2020
Clinical trials often fail to recruit an adequate number of appropriate patients. Identifying eligible trial participants is resource-intensive when relying on manual review of clinical notes, particularly in critical care settings where the time win...
Humans Computer Simulation Electronic Health Records Patient Selection Data Mining Critical Care Clinical Trials as Topic Female Adult Male Natural Language Processing
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Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review.

Hepatology (Baltimore, Md.) Mar 6, 2020
Machine learning (ML) utilizes artificial intelligence to generate predictive models efficiently and more effectively than conventional methods through detection of hidden patterns within large data sets. With this in mind, there are several areas wi...
Machine Learning Algorithms Liver Transplantation Patient Selection Liver Diseases Humans Neural Networks, Computer Artificial Intelligence
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Use of Deep Learning to Predict Final Ischemic Stroke Lesions From Initial Magnetic Resonance Imaging.

JAMA network open Mar 2, 2020
IMPORTANCE: Predicting infarct size and location is important for decision-making and prognosis in patients with acute stroke.
Brain Ischemia Deep Learning Magnetic Resonance Imaging Patient Selection Female Humans Aged Prognosis Male Retrospective Studies Middle Aged
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