AIMC Topic: Maryland

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Using supervised machine learning and ICD10 to identify non-accidental trauma in pediatric trauma patients in the Maryland Health Services Cost Review Commission dataset.

Child abuse & neglect Jan 11, 2025
BACKGROUND: Identifying non-accidental trauma (NAT) in pediatric trauma patients is challenging. We developed a machine learning model that uses demographic characteristics and ICD10 codes to detect the first diagnosis of NAT.
Infant, Newborn Young Adult Male Infant Humans Machine Learning Female International Classification of Diseases Wounds and Injuries Maryland Child Abuse Adolescent Child, Preschool Child Supervised Machine Learning
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Spatial patterns of rural opioid-related hospital emergency department visits: A machine learning analysis.

Health & place Oct 13, 2024
As opioid-related overdose emergency department visits continue to rise in the United States, there is a need to understand the location and magnitude of the crisis, especially in at-risk rural areas. We analyzed sets of ZIP code level electronic hea...
Rural Population Emergency Service, Hospital Analgesics, Opioid Opioid-Related Disorders Risk Factors Black or African American Adult Maryland Male Humans Opiate Overdose Middle Aged Machine Learning Emergency Room Visits Female
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Using machine learning models to estimate Escherichia coli concentration in an irrigation pond from water quality and drone-based RGB imagery data.

Water research May 31, 2024
The rapid and efficient quantification of Escherichia coli concentrations is crucial for monitoring water quality. Remote sensing techniques and machine learning algorithms have been used to detect E. coli in water and estimate its concentrations. Th...
Agricultural Irrigation Water Microbiology Ponds Maryland Machine Learning Environmental Monitoring Escherichia coli Water Quality
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Primary Prevention Trial Designs Using Coronary Imaging: A National Heart, Lung, and Blood Institute Workshop.

JACC. Cardiovascular imaging Sep 16, 2020
Coronary artery calcium (CAC) is considered a useful test for enhancing risk assessment in the primary prevention setting. Clinical trials are under consideration. The National Heart, Lung, and Blood Institute convened a multidisciplinary working gro...
Humans Artificial Intelligence Aged United States Predictive Value of Tests Maryland Primary Prevention National Heart, Lung, and Blood Institute (U.S.)
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Assessment of Machine Learning vs Standard Prediction Rules for Predicting Hospital Readmissions.

JAMA network open Mar 1, 2019
IMPORTANCE: Hospital readmissions are associated with patient harm and expense. Ways to prevent hospital readmissions have focused on identifying patients at greatest risk using prediction scores.
Humans Machine Learning Research Design Prognosis Female Risk Factors Hospitalization Male United States Middle Aged Medicare Risk Assessment Patient Readmission Length of Stay Maryland
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Real-time prediction of inpatient length of stay for discharge prioritization.

Journal of the American Medical Informatics Association : JAMIA Aug 7, 2015
OBJECTIVE: Hospitals are challenged to provide timely patient care while maintaining high resource utilization. This has prompted hospital initiatives to increase patient flow and minimize nonvalue added care time. Real-time demand capacity managemen...
Patient Discharge Aged Academic Medical Centers Adult Humans Male Hospital Administration Machine Learning Middle Aged Length of Stay Maryland Female Workload Workflow
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The role of fine-grained annotations in supervised recognition of risk factors for heart disease from EHRs.

Journal of biomedical informatics Jun 26, 2015
This paper describes a supervised machine learning approach for identifying heart disease risk factors in clinical text, and assessing the impact of annotation granularity and quality on the system's ability to recognize these risk factors. We utiliz...
Pattern Recognition, Automated Comorbidity Maryland Computer Security Aged Incidence Humans Vocabulary, Controlled Male Confidentiality Longitudinal Studies Cohort Studies Electronic Health Records Middle Aged Diabetes Complications Supervised Machine Learning Narration Female Risk Assessment Data Mining Natural Language Processing Coronary Artery Disease
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Evaluation of a Natural Language Processing Approach to Identify Social Determinants of Health in Electronic Health Records in a Diverse Community Cohort.

Medical care Mar 1, 2022
BACKGROUND: Health care systems in the United States are increasingly interested in measuring and addressing social determinants of health (SDoH). Advances in electronic health record systems and Natural Language Processing (NLP) create a unique oppo...
Social Determinants of Health Housing Maryland District of Columbia Cohort Studies United States Natural Language Processing Young Adult Adult Sensitivity and Specificity Male Middle Aged Surveys and Questionnaires Adolescent Delivery of Health Care Electronic Health Records Female Aged Humans Machine Learning Algorithms
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