AIMC Topic: APACHE

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Mortality Prediction Performance Under Geographical, Temporal, and COVID-19 Pandemic Dataset Shift: External Validation of the Global Open-Source Severity of Illness Score Model.

Critical care explorations
BACKGROUND: Risk-prediction models are widely used for quality of care evaluations, resource management, and patient stratification in research. While established models have long been used for risk prediction, healthcare has evolved significantly, a...

Machine learning-based model for predicting all-cause mortality in severe pneumonia.

BMJ open respiratory research
BACKGROUND: Severe pneumonia has a poor prognosis and high mortality. Current severity scores such as Acute Physiology and Chronic Health Evaluation (APACHE-II) and Sequential Organ Failure Assessment (SOFA), have limited ability to help clinicians i...

Explainable machine learning model for predicting acute pancreatitis mortality in the intensive care unit.

BMC gastroenterology
BACKGROUND: Current prediction models are suboptimal for determining mortality risk in patients with acute pancreatitis (AP); this might be improved by using a machine learning (ML) model. In this study, we aimed to construct an explainable ML model ...

Machine Learning-Based Mortality Prediction for Acute Gastrointestinal Bleeding Patients Admitted to Intensive Care Unit.

Current medical science
OBJECTIVE: The study aimed to develop machine learning (ML) models to predict the mortality of patients with acute gastrointestinal bleeding (AGIB) in the intensive care unit (ICU) and compared their prognostic performance with that of Acute Physiolo...

Machine learning based peri-surgical risk calculator for abdominal related emergency general surgery: a multicenter retrospective study.

International journal of surgery (London, England)
BACKGROUND: Currently, there is a lack of ideal risk prediction tools in the field of emergency general surgery (EGS). The American Association for the Surgery of Trauma recommends developing risk assessment tools specifically for EGS-related disease...

Prediction of hospital mortality among critically ill patients in a single centre in Asia: comparison of artificial neural networks and logistic regression-based model.

Hong Kong medical journal = Xianggang yi xue za zhi
INTRODUCTION: This study compared the performance of the artificial neural network (ANN) model with the Acute Physiologic and Chronic Health Evaluation (APACHE) II and IV models for predicting hospital mortality among critically ill patients in Hong ...

Comparison of correctly and incorrectly classified patients for in-hospital mortality prediction in the intensive care unit.

BMC medical research methodology
BACKGROUND: The use of machine learning is becoming increasingly popular in many disciplines, but there is still an implementation gap of machine learning models in clinical settings. Lack of trust in models is one of the issues that need to be addre...

Which model is superior in predicting ICU survival: artificial intelligence versus conventional approaches.

BMC medical informatics and decision making
BACKGROUND: A disease severity classification system is widely used to predict the survival of patients admitted to the intensive care unit with different diagnoses. In the present study, conventional severity classification systems were compared wit...

Machine learning-based mortality prediction model for heat-related illness.

Scientific reports
In this study, we aimed to develop and validate a machine learning-based mortality prediction model for hospitalized heat-related illness patients. After 2393 hospitalized patients were extracted from a multicentered heat-related illness registry in ...

A neural network for prediction of risk of nosocomial infection at intensive care units: a didactic preliminary model.

Einstein (Sao Paulo, Brazil)
OBJECTIVE: To propose a preliminary artificial intelligence model, based on artificial neural networks, for predicting the risk of nosocomial infection at intensive care units.