AIMC Topic: Patient Selection

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Using machine learning to optimize selection of elderly patients for endovascular thrombectomy.

Journal of neurointerventional surgery Feb 2, 2019
BACKGROUND: Endovascular thrombectomy (ET) is the standard of care for treatment of acute ischemic stroke (AIS) secondary to large vessel occlusion. The elderly population has been under-represented in clinical trials on ET, and recent studies have r...
Humans Machine Learning Prospective Studies Treatment Outcome Stroke Patient Selection Endovascular Procedures Thrombectomy Retrospective Studies Female Aged, 80 and over Male Cohort Studies
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Using Machine Learning to Identify Change in Surgical Decision Making in Current Use of Damage Control Laparotomy.

Journal of the American College of Surgeons Jan 9, 2019
BACKGROUND: In an earlier study, we reported the successful reduction in the use of damage control laparotomy (DCL); however, no change in the relative frequencies of specific indications was observed. In this study, we aimed to use machine learning ...
Clinical Decision-Making Female Young Adult Adult Sensitivity and Specificity Humans Machine Learning Algorithms Retrospective Studies Patient Selection Quality Improvement Laparotomy Abdominal Injuries Trauma Severity Indices Male Middle Aged Blood Transfusion Blood Pressure
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Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients.

PloS one Jan 9, 2019
BACKGROUND: Patient with acute coronary syndrome benefits from early revascularization. However, methods for the selection of patients who require urgent revascularization from a variety of patients visiting the emergency room with chest symptoms is ...
Myocardial Revascularization Decision Making, Computer-Assisted Humans Patient Selection Chest Pain Neural Networks, Computer Acute Coronary Syndrome Prognosis Datasets as Topic Sensitivity and Specificity Time Factors Diagnosis, Computer-Assisted Electrocardiography Diagnosis, Differential Predictive Value of Tests Emergency Service, Hospital
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Improving palliative care with deep learning.

BMC medical informatics and decision making Dec 12, 2018
BACKGROUND: Access to palliative care is a key quality metric which most healthcare organizations strive to improve. The primary challenges to increasing palliative care access are a combination of physicians over-estimating patient prognoses, and a ...
Humans Prognosis Electronic Health Records Clinical Decision-Making Deep Learning Patient Selection Palliative Care
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The Role of Artificial Intelligence in Interventional Oncology: A Primer.

Journal of vascular and interventional radiology : JVIR Dec 7, 2018
Liver Neoplasms Workflow Patient Selection Diffusion of Innovation Medical Oncology Machine Learning Forecasting Clinical Decision-Making Diagnosis, Computer-Assisted Radiography, Interventional Humans Neural Networks, Computer Artificial Intelligence
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Defining Massive Transfusion in Civilian Pediatric Trauma With Traumatic Brain Injury.

The Journal of surgical research Dec 4, 2018
The purpose of this study was to identify an optimal definition of massive transfusion in civilian pediatric trauma with severe traumatic brain injury (TBI) METHODS: Severely injured children (age ≤18 y) with severe TBI in the Trauma Quality Improvem...
Male Patient Selection Child Humans Survival Analysis Risk Assessment Retrospective Studies Blood Transfusion Hemorrhage Adolescent Female Brain Injuries, Traumatic Injury Severity Score Time Factors Sensitivity and Specificity Predictive Value of Tests Child, Preschool Registries
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Applying Artificial Intelligence to Address the Knowledge Gaps in Cancer Care.

The oncologist Nov 16, 2018
BACKGROUND: Rapid advances in science challenge the timely adoption of evidence-based care in community settings. To bridge the gap between what is possible and what is practiced, we researched approaches to developing an artificial intelligence (AI)...
Feasibility Studies Humans Patient Selection Artificial Intelligence Medical Oncology Electronic Health Records Clinical Trials as Topic Clinical Decision-Making Evidence-Based Medicine Neoplasms Decision Support Systems, Clinical
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A predictive model for high/low risk group according to oncotype DX recurrence score using machine learning.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology Oct 16, 2018
BACKGROUND: Oncotype DX(ODX) is a 21-gene breast cancer recurrence score(RS) assay that aids in decision-making for chemotherapy in early-stage hormone receptor-positive(HR+)breast cancer. We developed a prediction tool using machine learning for hig...
Middle Aged Risk Assessment Neoplasm Staging Female Humans Neoplasm Invasiveness Aged Neoplasm Grading Decision Making Machine Learning Adult Patient Selection Retrospective Studies Predictive Value of Tests Breast Neoplasms Republic of Korea Biomarkers, Tumor
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Using artificial intelligence to predict prolonged mechanical ventilation and tracheostomy placement.

The Journal of surgical research Apr 11, 2018
BACKGROUND: Early identification of critically ill patients who will require prolonged mechanical ventilation (PMV) has proven to be difficult. The purpose of this study was to use machine learning to identify patients at risk for PMV and tracheostom...
Respiration, Artificial Severity of Illness Index Length of Stay Time Factors Treatment Outcome Feasibility Studies Supervised Machine Learning Patient Selection Critical Care Tracheostomy Decision Support Techniques Databases, Factual Critical Illness Intensive Care Units Risk Assessment Humans
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Randomized controlled trial of Amigo® robotically controlled versus manually controlled ablation of the cavo-tricuspid isthmus using a contact force ablation catheter.

Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing Feb 12, 2018
BACKGROUND: Radiofrequency catheter ablation (RFCA) of the cavo-tricuspid isthmus (CTI) is a common treatment for atrial flutter (AFL). However, achieving bi-directional CTI conduction block may be difficult, partly due to catheter instability.
Tricuspid Valve Cardiac Catheters Atrial Flutter Male Humans Prospective Studies Heart Conduction System Middle Aged Follow-Up Studies Female Risk Assessment Reference Values Robotics Aged Severity of Illness Index Catheter Ablation Equipment Design Survival Rate Treatment Outcome Recurrence Surgery, Computer-Assisted Statistics, Nonparametric Patient Selection
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