AIMC Topic: Patient Readmission

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Predicting hospital readmission for lupus patients: An RNN-LSTM-based deep-learning methodology.

Computers in biology and medicine Aug 31, 2018
Hospital readmission is one of the critical metrics used for measuring the performance of hospitals. The HITECH Act imposes penalties when patients are readmitted to hospitals if they are diagnosed with one of the six conditions mentioned in the Act....
Deep Learning Predictive Value of Tests Lupus Erythematosus, Systemic Models, Biological Patient Readmission Humans Neural Networks, Computer Female Male
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A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data.

BMC medical informatics and decision making Jun 22, 2018
BACKGROUND: Heart failure is one of the leading causes of hospitalization in the United States. Advances in big data solutions allow for storage, management, and mining of large volumes of structured and semi-structured data, such as complex healthca...
Middle Aged Deep Learning Models, Theoretical Heart Failure Patient Readmission Female Aged Aged, 80 and over Male Humans Prognosis Retrospective Studies Electronic Health Records
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Predicting Hospital Readmission via Cost-Sensitive Deep Learning.

IEEE/ACM transactions on computational biology and bioinformatics Apr 16, 2018
With increased use of electronic medical records (EMRs), data mining on medical data has great potential to improve the quality of hospital treatment and increase the survival rate of patients. Early readmission prediction enables early intervention,...
Deep Learning Patient Readmission Humans ROC Curve Algorithms Models, Statistical Electronic Health Records Data Mining
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How Confounder Strength Can Affect Allocation of Resources in Electronic Health Records.

Perspectives in health information management Jan 1, 2018
When electronic health record (EHR) data are used, multiple approaches may be available for measuring the same variable, introducing potentially confounding factors. While additional information may be gleaned and residual confounding reduced through...
Humans Algorithms Smoking Electronic Health Records Confounding Factors, Epidemiologic Natural Language Processing Patient Readmission Data Accuracy
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Validation of a definition of excessive postoperative bleeding in infants undergoing cardiac surgery with cardiopulmonary bypass.

The Journal of thoracic and cardiovascular surgery Dec 13, 2017
OBJECTIVE: To derive and validate an objective definition of postoperative bleeding in neonates and infants undergoing cardiac surgery with cardiopulmonary bypass.
Erythrocyte Transfusion Length of Stay Infant, Newborn Humans Age Factors Risk Factors Time Factors Cardiopulmonary Bypass Male Prospective Studies Drainage Hospital Mortality Patient Readmission Risk Assessment Retrospective Studies Postoperative Hemorrhage Treatment Outcome Terminology as Topic Infant Female Chest Tubes Reoperation Cardiac Surgical Procedures
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Estimating causal effects for survival (time-to-event) outcomes by combining classification tree analysis and propensity score weighting.

Journal of evaluation in clinical practice Dec 12, 2017
RATIONALE, AIMS AND OBJECTIVES: A common approach to assessing treatment effects in nonrandomized studies with time-to-event outcomes is to estimate propensity scores and compute weights using logistic regression, test for covariate balance, and then...
Socioeconomic Factors Age Factors Models, Statistical Decision Trees Health Services Male Middle Aged Comorbidity Time Factors Proportional Hazards Models Propensity Score Monte Carlo Method Heart Failure Sex Factors Patient Readmission Survival Analysis Humans Machine Learning Female Pulmonary Disease, Chronic Obstructive
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Prediction of early unplanned intensive care unit readmission in a UK tertiary care hospital: a cross-sectional machine learning approach.

BMJ open Sep 15, 2017
OBJECTIVES: Unplanned readmissions to the intensive care unit (ICU) are highly undesirable, increasing variance in care, making resource planning difficult and potentially increasing length of stay and mortality in some settings. Identifying patients...
ROC Curve Area Under Curve Tertiary Care Centers Patient Readmission United Kingdom Aged Adult Cross-Sectional Studies Male Middle Aged Machine Learning Retrospective Studies Intensive Care Units Female Humans
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Predicting all-cause risk of 30-day hospital readmission using artificial neural networks.

PloS one Jul 14, 2017
Avoidable hospital readmissions not only contribute to the high costs of healthcare in the US, but also have an impact on the quality of care for patients. Large scale adoption of Electronic Health Records (EHR) has created the opportunity to proacti...
Neural Networks, Computer Databases, Factual Electronic Health Records Infant, Newborn Risk Factors Aged Child, Preschool Middle Aged Aged, 80 and over Child Humans Young Adult Infant ROC Curve Length of Stay Area Under Curve Adult Adolescent Patient Readmission
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A Natural Language Processing Framework for Assessing Hospital Readmissions for Patients With COPD.

IEEE journal of biomedical and health informatics Mar 17, 2017
With the passage of recent federal legislation, many medical institutions are now responsible for reaching target hospital readmission rates. Chronic diseases account for many hospital readmissions and chronic obstructive pulmonary disease has been r...
Humans Algorithms Pulmonary Disease, Chronic Obstructive Decision Support Systems, Clinical Natural Language Processing Area Under Curve Patient Readmission Medical Informatics
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Acute systolic heart failure with normal admission BNP: clinical features and outcomes.

International journal of cardiology Dec 20, 2016
INTRODUCTION: The characteristics and outcomes of patients hospitalized with acute systolic heart failure (HF) and normal admission B-type natriuretic peptide (BNP) has not been previously explored.
Patient Readmission Natriuretic Peptide, Brain Heart Failure, Systolic Patient Admission Humans United States Biomarkers Follow-Up Studies Prognosis Acute Disease Retrospective Studies Morbidity Stroke Volume Female Middle Aged Survival Rate Disease Progression Male Time Factors Ventricular Function, Left Hospital Mortality
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