AIMC Topic: APACHE

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Optimal intensive care outcome prediction over time using machine learning.

PloS one Nov 14, 2018
BACKGROUND: Prognostication is an essential tool for risk adjustment and decision making in the intensive care unit (ICU). Research into prognostication in ICU has so far been limited to data from admission or the first 24 hours. Most ICU admissions ...
Patient Admission Feasibility Studies Critical Care Datasets as Topic APACHE Female Aged Male Middle Aged Risk Assessment Sepsis Hospital Mortality ROC Curve Decision Support Systems, Clinical Logistic Models Humans Machine Learning Prognosis Retrospective Studies Intensive Care Units Clinical Decision-Making
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Low vitamin D at ICU admission is associated with cancer, infections, acute respiratory insufficiency, and liver failure.

Nutrition (Burbank, Los Angeles County, Calif.) Oct 24, 2018
OBJECTIVES: Vitamin D deficiency may be associated with comorbidities and poor prognosis. However, this association in patients in the intensive care unit (ICU) has not been fully elucidated. The aim of this study was to investigate whether the serum...
Infections Liver Failure Middle Aged Organ Dysfunction Scores Female Neoplasms Patient Admission Severity of Illness Index Aged Humans Respiration, Artificial Time Factors Adult C-Reactive Protein Prognosis Comorbidity Hospital Mortality Cross-Sectional Studies Critical Illness Acute Disease Vitamin D Male Intensive Care Units Vitamin D Deficiency Respiratory Insufficiency APACHE
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Risk Prediction for Portal Vein Thrombosis in Acute Pancreatitis Using Radial Basis Function.

Annals of vascular surgery Sep 22, 2017
BACKGROUND: Acute pancreatitis (AP) can induce portosplenomesenteric vein thrombosis (PVT), which may generate higher morbidity and mortality. However current diagnostic modalities for PVT are still controversial. In recent decades, artificial neural...
Male Middle Aged Risk Assessment ROC Curve Logistic Models Models, Theoretical Venous Thrombosis Acute Disease Portal Vein Biomarkers Retrospective Studies Female Adult Humans Sensitivity and Specificity APACHE Neural Networks, Computer Pancreatitis Hematologic Tests
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Towards generalizable entity-centric clinical coreference resolution.

Journal of biomedical informatics Apr 21, 2017
OBJECTIVE: This work investigates the problem of clinical coreference resolution in a model that explicitly tracks entities, and aims to measure the performance of that model in both traditional in-domain train/test splits and cross-domain experiment...
Semantics APACHE Humans Software Electronic Health Records Natural Language Processing
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Learning a Severity Score for Sepsis: A Novel Approach based on Clinical Comparisons.

AMIA ... Annual Symposium proceedings. AMIA Symposium Nov 5, 2015
Sepsis is one of the leading causes of death in the United States. Early administration of treatment has been shown to decrease sepsis-related mortality and morbidity. Existing scoring systems such as the Acute Physiology and Chronic Health Evaluatio...
Humans Electronic Data Processing Machine Learning Prognosis Sepsis Shock, Septic APACHE
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[Establishing of mortality predictive model for elderly critically ill patients using simple bedside indicators and interpretable machine learning algorithms].

Zhonghua wei zhong bing ji jiu yi xue Feb 1, 2025
OBJECTIVE: To explore the feasibility of incorporating simple bedside indicators into death predictive model for elderly critically ill patients based on interpretability machine learning algorithms, providing a new scheme for clinical disease assess...
Humans Aged Prognosis Machine Learning Male Retrospective Studies Algorithms Hospital Mortality Critical Illness APACHE Intensive Care Units Organ Dysfunction Scores Female
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Development and validation of a deep learning model to predict the survival of patients in ICU.

Journal of the American Medical Informatics Association : JAMIA Aug 16, 2022
BACKGROUND: Patients in the intensive care unit (ICU) are often in critical condition and have a high mortality rate. Accurately predicting the survival probability of ICU patients is beneficial to timely care and prioritizing medical resources to im...
Critical Care APACHE Intensive Care Units Deep Learning China Hospital Mortality Humans Retrospective Studies
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Explainable Machine Learning Model for Predicting GI Bleed Mortality in the Intensive Care Unit.

The American journal of gastroenterology Oct 1, 2020
INTRODUCTION: Acute gastrointestinal (GI) bleed is a common reason for hospitalization with 2%-10% risk of mortality. In this study, we developed a machine learning (ML) model to calculate the risk of mortality in intensive care unit patients admitte...
Aged, 80 and over Male Middle Aged Risk Assessment ROC Curve Area Under Curve Humans APACHE Machine Learning Gastrointestinal Hemorrhage Intensive Care Units Female Aged
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Predictive Modeling of Pressure Injury Risk in Patients Admitted to an Intensive Care Unit.

American journal of critical care : an official publication, American Association of Critical-Care Nurses Jul 1, 2020
BACKGROUND: Pressure injuries are an important problem in hospital care. Detecting the population at risk for pressure injuries is the first step in any preventive strategy. Available tools such as the Norton and Braden scales do not take into accoun...
Electronic Health Records Female Risk Factors Aged Aged, 80 and over Humans Machine Learning Intensive Care Units Risk Assessment Adolescent Child Hospitals, University Hemoglobins Young Adult Adult Sensitivity and Specificity Male Middle Aged Data Mining Critical Care APACHE Pressure Ulcer
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Predicting weaning difficulty for planned extubation patients with an artificial neural network.

Medicine Oct 1, 2019
This study aims to construct a neural network to predict weaning difficulty among planned extubation patients in intensive care units.This observational cohort study was conducted in eight adult ICUs in a medical center about adult patients experienc...
Ventilator Weaning Intensive Care Units Female Aged Aged, 80 and over Male Middle Aged ROC Curve APACHE Airway Extubation Humans Neural Networks, Computer Algorithms Retrospective Studies
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