AIMC Topic: Patient Selection

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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.
Length of Stay Time Factors Smartphone Mobile Applications Male Risk Assessment Deep Learning Endovascular Procedures Enhanced Recovery After Surgery Health Services Research Aortic Aneurysm Treatment Outcome Patient Discharge Patient Selection Multivariate Analysis Humans Decision Support Techniques Databases, Factual Clinical Decision-Making Female Risk Factors Aged Aged, 80 and over
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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...
Models, Theoretical Patient Selection Cartilage Osteoarthritis, Knee Humans Machine Learning Disease Progression 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...
Electronic Health Records Cohort Studies Patient Selection Motivation Humans Machine Learning
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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
Mammaplasty Humans 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...
Humans Evidence-Based Medicine Artificial Intelligence Adenocarcinoma of Lung Retrospective Studies Prognosis Survival Rate Carcinoma, Non-Small-Cell Lung Clinical Decision-Making Decision Support Systems, Clinical Male Follow-Up Studies Female Lung Neoplasms Middle Aged Carcinoma, Squamous Cell Patient Selection United States China Practice Guidelines as Topic
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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...
Natural Language Processing Patient Selection Data Mining Critical Care Female Adult Male Clinical Trials as Topic Humans Computer Simulation Electronic Health Records
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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...
Artificial Intelligence Patient Selection Humans Liver Diseases Machine Learning Algorithms Neural Networks, Computer Liver Transplantation
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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.
Patient Selection Humans Brain Ischemia Prognosis Retrospective Studies Middle Aged Female Deep Learning Aged Magnetic Resonance Imaging Male
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Artificial intelligence in abdominal aortic aneurysm.

Journal of vascular surgery Feb 21, 2020
OBJECTIVE: Abdominal aortic aneurysm (AAA) is a life-threatening disease, and the only curative treatment relies on open or endovascular repair. The decision to treat relies on the evaluation of the risk of AAA growth and rupture, which can be diffic...
Predictive Value of Tests Aortic Aneurysm, Abdominal Patient Selection Image Interpretation, Computer-Assisted Risk Assessment Diagnosis, Computer-Assisted Decision Support Systems, Clinical Treatment Outcome Decision Support Techniques Clinical Decision-Making Risk Factors Humans Artificial Intelligence
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Patient selection for proton therapy: a radiobiological fuzzy Markov model incorporating robust plan analysis.

Physical and engineering sciences in medicine Feb 10, 2020
While proton therapy can offer increased sparing of healthy tissue compared with X-ray therapy, it can be difficult to predict whether a benefit can be expected for an individual patient. Predictive modelling may aid in this respect. However, the pre...
Probability Child, Preschool Humans Radiotherapy, Intensity-Modulated Models, Theoretical Female Markov Chains Life Expectancy Fuzzy Logic Quality of Life Quality-Adjusted Life Years Proton Therapy Patient Selection Uncertainty
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