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Hospital Mortality

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A Multidatabase ExTRaction PipEline (METRE) for facile cross validation in critical care research.

Journal of biomedical informatics
Transforming raw EHR data into machine learning model-ready inputs requires considerable effort. One widely used EHR database is Medical Information Mart for Intensive Care (MIMIC). Prior work on MIMIC-III cannot query the updated and improved MIMIC-...

Predicting heart failure in-hospital mortality by integrating longitudinal and category data in electronic health records.

Medical & biological engineering & computing
Heart failure is a life-threatening syndrome that is diagnosed in 3.6 million people worldwide each year. We propose a deep fusion learning model (DFL-IMP) that uses time series and category data from electronic health records to predict in-hospital ...

Clinical utility of a deep-learning mortality prediction model for cardiac surgery decision making.

The Journal of thoracic and cardiovascular surgery
OBJECTIVES: The aim of this study using decision curve analysis (DCA) was to evaluate the clinical utility of a deep-learning mortality prediction model for cardiac surgery decision making compared with the European System for Cardiac Operative Risk ...

Model for Predicting In-Hospital Mortality of Physical Trauma Patients Using Artificial Intelligence Techniques: Nationwide Population-Based Study in Korea.

Journal of medical Internet research
BACKGROUND: Physical trauma-related mortality places a heavy burden on society. Estimating the mortality risk in physical trauma patients is crucial to enhance treatment efficiency and reduce this burden. The most popular and accurate model is the In...

Development, validation, and feature extraction of a deep learning model predicting in-hospital mortality using Japan's largest national ICU database: a validation framework for transparent clinical Artificial Intelligence (cAI) development.

Anaesthesia, critical care & pain medicine
OBJECTIVE: While clinical Artificial Intelligence (cAI) mortality prediction models and relevant studies have increased, limitations including the lack of external validation studies and inadequate model calibration leading to decreased overall accur...

Machine learning-derived prediction of in-hospital mortality in patients with severe acute respiratory infection: analysis of claims data from the German-wide Helios hospital network.

Respiratory research
BACKGROUND: Severe acute respiratory infections (SARI) are the most common infectious causes of death. Previous work regarding mortality prediction models for SARI using machine learning (ML) algorithms that can be useful for both individual risk str...

Machine learning to predict adverse outcomes after cardiac surgery: A systematic review and meta-analysis.

Journal of cardiac surgery
BACKGROUND: Machine learning (ML) models are promising tools for predicting adverse postoperative outcomes in cardiac surgery, yet have not translated to routine clinical use. We conducted a systematic review and meta-analysis to assess the predictiv...

Phenotypes of sickle cell intensive care admissions: an unsupervised machine learning approach in a single-center retrospective cohort.

Annals of hematology
Sickle cell disease (SCD) is associated with multiple known complications and increased mortality. This study aims to further understand the profile of intensive care unit (ICU) admissions of SCD patients. In this single-center retrospective cohort (...

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...

Dynamic Mortality Risk Predictions for Children in ICUs: Development and Validation of Machine Learning Models.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies
OBJECTIVES: Assess a machine learning method of serially updated mortality risk.