Hyperchloremia in critically ill patients: association with outcomes and prediction using electronic health record data

Early prediction of acute kidney injury following ICU admission using a multivariate panel of physiological measurements

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Predicting in-hospital mortality of patients with acute kidney injury in the ICU using random forest model

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Crystal Balls and Magic Eight Balls

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Big Data and Data Science in Critical Care

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A new prediction model for assessing the clinical outcomes of ICU patients with community-acquired pneumonia: a decision tree analysis

(2018) Shufang Zhang et al.   ANNALS OF MEDICINE

A machine learning-based model for 1-year mortality prediction in patients admitted to an Intensive Care Unit with a diagnosis of sepsis

(2018) J.E. García-Gallo et al.   Medicina Intensiva

Machine learning for real-time prediction of complications in critical care: a retrospective study

(2018) Alexander Meyer et al.   Lancet Respiratory Medicine

The dark sides of fluid administration in the critically ill patient

(2017) Daniel A. Reuter et al.   INTENSIVE CARE MEDICINE

MIMIC-III, a freely accessible critical care database

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Association of Hyperchloremia With Hospital Mortality in Critically Ill Septic Patients

(2015) Javier A. Neyra et al.   CRITICAL CARE MEDICINE

Association Between the Choice of IV Crystalloid and In-Hospital Mortality Among Critically Ill Adults With Sepsis*

(2014) Karthik Raghunathan et al.   CRITICAL CARE MEDICINE

Data mining using clinical physiology at discharge to predict ICU readmissions

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Association Between a Chloride-Liberal vs Chloride-Restrictive Intravenous Fluid Administration Strategy and Kidney Injury in Critically Ill Adults

(2012) Nor’azim Mohd Yunos et al.   JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION