AIMC Topic: Hospital Mortality

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A method for predicting mortality in acute mesenteric ischemia: Machine learning.

Ulusal travma ve acil cerrahi dergisi = Turkish journal of trauma & emergency surgery : TJTES Jul 1, 2024
BACKGROUND: This study aimed to develop and validate an artificial intelligence model using machine learning (ML) to predict hospital mortality in patients with acute mesenteric ischemia (AMI).
Middle Aged Female Hospital Mortality Aged Predictive Value of Tests Humans Acute Disease Machine Learning Aged, 80 and over Adult Male Mesenteric Ischemia
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Development and external validation of a dynamic risk score for early prediction of cardiogenic shock in cardiac intensive care units using machine learning.

European heart journal. Acute cardiovascular care Jun 30, 2024
AIMS: Myocardial infarction and heart failure are major cardiovascular diseases that affect millions of people in the USA with morbidity and mortality being highest among patients who develop cardiogenic shock. Early recognition of cardiogenic shock ...
Retrospective Studies Intensive Care Units Female Risk Factors Humans Machine Learning Early Diagnosis Coronary Care Units Shock, Cardiogenic Risk Assessment Hospital Mortality Aged ROC Curve Male Myocardial Infarction Middle Aged
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[Prediction of risk of in-hospital death in patients with chronic heart failure complicated by lung infections using interpretable machine learning].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University Jun 20, 2024
OBJECTIVE: To predict the risk of in-hospital death in patients with chronic heart failure (CHF) complicated by lung infections using interpretable machine learning.
Chronic Disease Pneumonia Heart Failure Kaplan-Meier Estimate Male Middle Aged Hospital Mortality Pneumonia, Bacterial Humans Machine Learning Risk Factors Aged Algorithms Female
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BadCLM: Backdoor Attack in Clinical Language Models for Electronic Health Records.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 1, 2024
The advent of clinical language models integrated into electronic health records (EHR) for clinical decision support has marked a significant advancement, leveraging the depth of clinical notes for improved decision-making. Despite their success, the...
Hospital Mortality Decision Support Systems, Clinical Natural Language Processing Computer Security Humans Electronic Health Records
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Development and deployment of interpretable machine-learning model for predicting in-hospital mortality in elderly patients with acute kidney disease.

Renal failure Dec 1, 2022
BACKGROUND: Acute kidney injury (AKI) is more likely to develop in the elderly admitted to the intensive care unit (ICU). Acute kidney disease (AKD) affects ∼45% of patients with AKI and increases short-term mortality. However, there are no studies o...
Acute Disease Acute Kidney Injury Intensive Care Units Aged Hospital Mortality Humans Machine Learning
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Clinical utility of automatic phenotype annotation in unstructured clinical notes: intensive care unit use.

BMJ health & care informatics Nov 1, 2022
OBJECTIVE: Clinical notes contain information that has not been documented elsewhere, including responses to treatment and clinical findings, which are crucial for predicting key outcomes in patients in acute care. In this study, we propose the autom...
Critical Care Humans Intensive Care Units Hospital Mortality Machine Learning Phenotype
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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 Humans Hospital Mortality Retrospective Studies
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[Research progress of in-hospital mortality risk model in patients with acute myocardial infarction].

Zhonghua wei zhong bing ji jiu yi xue May 1, 2022
The incidence of in-hospital death in acute myocardial infarction (AMI) is high, which seriously threatens the life and health of patients. At present, many countries and regions have established a variety of objective assessment models for predictin...
Humans Artificial Intelligence Incidence Hospital Mortality Risk Factors Risk Assessment Myocardial Infarction
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Electronic health record machine learning model predicts trauma inpatient mortality in real time: A validation study.

The journal of trauma and acute care surgery Jan 1, 2022
INTRODUCTION: Patient outcome prediction models are underused in clinical practice because of lack of integration with real-time patient data. The electronic health record (EHR) has the ability to use machine learning (ML) to develop predictive model...
Injury Severity Score Middle Aged Wounds and Injuries Electronic Health Records Risk Assessment Humans Female Hospital Mortality Machine Learning Registries Male Prognosis Critical Care United States Predictive Value of Tests
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Physiological Assessment of Delirium Severity: The Electroencephalographic Confusion Assessment Method Severity Score (E-CAM-S).

Critical care medicine Jan 1, 2022
OBJECTIVES: Delirium is a common and frequently underdiagnosed complication in acutely hospitalized patients, and its severity is associated with worse clinical outcomes. We propose a physiologically based method to quantify delirium severity as a to...
Machine Learning Prognosis Confusion Retrospective Studies Female Electroencephalography Humans Length of Stay Delirium Patient Acuity Middle Aged Hospital Mortality Academic Medical Centers Image Processing, Computer-Assisted Aged Comorbidity Hospitals Severity of Illness Index Adult Male
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