AIMC Topic: Pennsylvania

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Implementing a robotics curriculum at an academic general surgery training program: our initial experience.

Journal of robotic surgery Mar 19, 2016
The robotic surgical platform is being utilized by a growing number of hospitals across the country, including academic medical centers. Training programs are tasked with teaching their residents how to utilize this technology. To this end, we have d...
Teaching Humans Robotics Robotic Surgical Procedures Pennsylvania Curriculum Internship and Residency General Surgery
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Exploring the forest instead of the trees: An innovative method for defining obesogenic and obesoprotective environments.

Health & place Sep 19, 2015
Past research has assessed the association of single community characteristics with obesity, ignoring the spatial co-occurrence of multiple community-level risk factors. We used conditional random forests (CRF), a non-parametric machine learning appr...
Humans Machine Learning Algorithms Female Risk Factors Pediatric Obesity Male Residence Characteristics Adolescent Pennsylvania Child
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Positive Surgical Margin Trends in Patients with Pathologic T3 Prostate Cancer Treated with Robot-Assisted Radical Prostatectomy.

Journal of endourology Dec 15, 2014
INTRODUCTION: We analyzed the trends of positive surgical margin (PSM) location in patients who had pT3 disease at robot-assisted radical prostatectomy (RARP). We aimed to describe our changing incidence of PSMs in the largest series to date of patie...
Pennsylvania Neoplasm Staging Magnetic Resonance Imaging Neoplasm Recurrence, Local Robotics Prostatectomy Prostatic Neoplasms Humans Male Middle Aged
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A Simulated Prospective Evaluation of a Deep Learning Model for Real-Time Prediction of Clinical Deterioration Among Ward Patients.

Critical care medicine Aug 1, 2021
OBJECTIVES: The National Early Warning Score, Modified Early Warning Score, and quick Sepsis-related Organ Failure Assessment can predict clinical deterioration. These scores exhibit only moderate performance and are often evaluated using aggregated ...
Adult Male Middle Aged Humans Retrospective Studies Critical Care Organ Dysfunction Scores Pennsylvania Clinical Deterioration Risk Assessment Deep Learning Sepsis
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Can Machine Learning Improve Screening for Targeted Delinquency Prevention Programs?

Prevention science : the official journal of the Society for Prevention Research Feb 1, 2020
The cost-effectiveness of targeted delinquency prevention programs for children depends on the accuracy of the screening process. Screening accuracy is often poor, resulting in wasted resources and missed opportunities to avert negative outcomes. Thi...
Algorithms Male Child Humans Machine Learning Violence Pennsylvania Juvenile Delinquency Logistic Models Mass Screening Quality Improvement
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Predictors of Dementia in the Oldest Old: A Novel Machine Learning Approach.

Alzheimer disease and associated disorders Jan 1, 2020
BACKGROUND: Incidence of dementia increases exponentially with age; little is known about its risk factors in the ninth and 10th decades of life. We identified predictors of dementia with onset after age 85 years in a longitudinal population-based co...
Humans Mental Status and Dementia Tests Dementia Incidence Hypertension Risk Factors Longitudinal Studies Machine Learning Aged Pennsylvania Prospective Studies Male Diagnostic Self Evaluation Female
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Screening Electronic Health Record-Related Patient Safety Reports Using Machine Learning.

Journal of patient safety Mar 1, 2017
INTRODUCTION: The objective of this study was to develop a semiautomated approach to screening cases that describe hazards associated with the electronic health record (EHR) from a mandatory, population-based patient safety reporting system.
Mandatory Reporting Electronic Health Records Bayes Theorem Data Mining Medical Informatics Automation Pennsylvania Algorithms Patient Safety Humans Databases, Factual Machine Learning
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Evaluation of a hospital admission prediction model adding coded chief complaint data using neural network methodology.

European journal of emergency medicine : official journal of the European Society for Emergency Medicine Apr 1, 2015
OBJECTIVE: Our objective was to apply neural network methodology to determine whether adding coded chief complaint (CCC) data to triage information would result in an improved hospital admission prediction model than one without CCC data.
Hospitalization Emergency Service, Hospital Predictive Value of Tests Pennsylvania Confidence Intervals Male Humans Retrospective Studies Emergency Medicine Triage Aged Patient Admission Neural Networks, Computer Risk Assessment Area Under Curve Aged, 80 and over Female Emergency Treatment Algorithms Cohort Studies Reproducibility of Results
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