AIMC Topic: Hospital Mortality

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Healing with hierarchy: Hierarchical attention empowered graph neural networks for predictive analysis in medical data.

Artificial intelligence in medicine Jul 1, 2025
In healthcare, predictive analysis using unstructured medical data is crucial for gaining insights into patient conditions and outcomes. However, unstructured data, which contains valuable patient information such as symptoms and medical histories, o...
Intensive Care Units Electronic Health Records Length of Stay Hospital Mortality Humans Neural Networks, Computer Machine Learning Graph Neural Networks
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Machine learning risk-prediction model for in-hospital mortality in Takotsubo cardiomyopathy.

International journal of cardiology Jul 1, 2025
BACKGROUND: Takotsubo cardiomyopathy (TC) is an acute heart failure syndrome characterized by transient left ventricular dysfunction, often triggered by stress. Data on risk scores predicting mortality in TC is sparse. We developed a machine-learning...
Takotsubo Cardiomyopathy Hospital Mortality United States Predictive Value of Tests Humans Machine Learning Databases, Factual Aged, 80 and over Retrospective Studies Male Female Middle Aged Risk Factors Risk Assessment Aged
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EVALUATION OF PROGNOSTIC RISK MODELS BASED ON AGE AND COMORBIDITY IN SEPTIC PATIENTS: INSIGHTS FROM MACHINE LEARNING AND TRADITIONAL METHODS IN A LARGE-SCALE, MULTICENTER, RETROSPECTIVE STUDY.

Shock (Augusta, Ga.) Jul 1, 2025
Background: Age and comorbidity significantly impact the prognosis of septic patients and inform treatment decisions. To provide clinicians with effective tools for identifying high-risk patients, this study assesses the predictive value of the age-a...
Machine Learning Prognosis Retrospective Studies Female Humans Comorbidity Sepsis Hospital Mortality Age Factors Aged Aged, 80 and over Male Middle Aged
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Machine learning models for predicting in-hospital mortality from acute pancreatitis in intensive care unit.

BMC medical informatics and decision making May 27, 2025
BACKGROUND: Acute pancreatitis (AP) represents a critical medical condition where timely and precise prediction of in-hospital mortality is crucial for guiding optimal clinical management. This study focuses on the development of advanced machine lea...
Humans Machine Learning Intensive Care Units Female Middle Aged Aged Hospital Mortality Adult Acute Disease Male Pancreatitis
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Exploring treatment effects and fluid resuscitation strategies in septic shock: a deep learning-based causal inference approach.

Scientific reports May 25, 2025
Septic shock exhibits diverse etiologies and patient characteristics, necessitating tailored fluid management. We aimed to compare resuscitation strategies using normal saline, Ringer's lactate, and albumin, and to determine which patient factors are...
Shock, Septic Treatment Outcome Fluid Therapy Albumins Resuscitation Ringer's Lactate Male Middle Aged Deep Learning Hospital Mortality Acute Kidney Injury Female Aged Humans
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A predictive model for hospital death in cancer patients with acute pulmonary embolism using XGBoost machine learning and SHAP interpretation.

Scientific reports May 25, 2025
The prediction of in-hospital mortality in cancer patients with acute pulmonary embolism (APE) remains a significant clinical challenge. This study aimed to develop and validate a machine learning model using XGBoost to predict in-hospital mortality ...
Male Middle Aged Neoplasms Hospital Mortality Prognosis Retrospective Studies Female Aged ROC Curve Acute Disease Pulmonary Embolism Boosting Machine Learning Algorithms Humans Machine Learning
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Optimal Vasopressin Initiation in Septic Shock: The OVISS Reinforcement Learning Study.

JAMA May 20, 2025
IMPORTANCE: Norepinephrine is the first-line vasopressor for patients with septic shock. When and whether a second agent, such as vasopressin, should be added is unknown.
Humans Critical Illness Norepinephrine Middle Aged Vasoconstrictor Agents Hospital Mortality Electronic Health Records Clinical Decision-Making Datasets as Topic Organ Dysfunction Scores Shock, Septic Female Practice Guidelines as Topic Vasopressins Aged Observational Studies as Topic Reinforcement Machine Learning Male
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Assessing the validity of ICD-10 administrative data in coding comorbidities.

BMJ health & care informatics May 13, 2025
OBJECTIVES: Administrative data are commonly used to inform chronic disease prevalence and support health informatic research. This study assessed the validity of coding comorbidities in the International Classification of Diseases, 10th Revision (IC...
International Classification of Diseases Clinical Coding Humans Alberta Female Reproducibility of Results Aged Male Middle Aged Comorbidity Hospital Mortality
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Rapid Response System Restructure: Focus on Prevention and Early Intervention.

Critical care nursing quarterly May 13, 2025
This article describes the staged restructure of the rapid response program into a dedicated 24/7 proactive rapid response system in a quaternary academic medical center in the southern United States. Rapid response nurses (RRNs) completed clinical l...
Heart Arrest Intensive Care Units Humans Hospital Mortality Artificial Intelligence United States Inpatients
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Machine learning-based prediction of 90-day prognosis and in-hospital mortality in hemorrhagic stroke patients.

Scientific reports May 9, 2025
This study aims to predict hemorrhagic stroke outcomes, including 90-day prognosis and in-hospital mortality, using machine learning models and SHapley Additive exPlanations (SHAP) analysis. Data were collected from a national Stroke Registry from Ja...
Middle Aged Severity of Illness Index Stroke Hospital Mortality Female Humans Registries Hemorrhagic Stroke Aged Machine Learning Aged, 80 and over Prognosis Male
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