Article

OASIS+: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality

2021-01-04

Abstract excerpt

<h4>ABSTRACT</h4> Severity scores assess the acuity of critical illness by penalizing for the deviation of physiologic measurements from normal and aggregating these penalties (also called “weights” or “subscores”) into a final score (or probability) for quantifying the severity of critical illness (or the likelihood of in-hospital mortality). Although these simple additive models are human readable and interpreta...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 7b7f1d0e-8198-5d6b-b968-21276377aab3
DOI: 10.1101/2020.12.28.20248946

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

OASIS+: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortalityDOI 10.1101/2020.12.28.20248946Read

Integrating Machine Learning and SHAP for Interpretable Prediction of 28-D ay Mortality in ICU Patients: A Comprehensive Analysis of Initial Physiologic al FeaturesSemantic proximity Read

Clinical Risk-Weighted Evaluation of ICU Mortality Prediction Models on MIMIC-IVSemantic proximity Read

Development and Validation of a Machine-Learning Model for Predicting Prognosis in Critically Ill Patients Undergoing Major SurgerySemantic proximity Read

Development and External Validation of a High-Precision Model for Predicting ICU Admission from Emergency Department TriageSemantic proximity Read

Head-to-head comparison of seven prognostic scoring systems for predicting in-hospital mortality and mechanical ventilation need in CAP patients with comorbidities: a retrospective cohort studySemantic proximity Read

A Machine Learning-Based Prediction of Hospital Mortality in Mechanically Ventilated ICU PatientsSemantic proximity Read

XGBoost-Based Prediction of ICU Mortality in Sepsis-Associated Acute Kidney Injury Patients Using MIMIC-IV Database with Validation from eICU DatabaseSemantic proximity Read

Multicenter analysis of COVID-19 hospitalizations and stacking machine learning algorithms for prediction of high-risk patientsSemantic proximity Read

Development of an ensemble machine learning prognostic model to predict 60-day risk of major adverse cardiac events in adults with chest painSemantic proximity Read

Improving mortality prediction in critically ill cancer patients with a multidimensional machine learning modelSemantic proximity Read

Application Of Multi-Inflammatory Index To Predict 28-Day Mortality In ICU Patients With Heart Failure: A Retrospective Machine Learning Study Based On The MIMIC-IV DatabaseSemantic proximity Read

A 9-Variable Lactate-to-Albumin Ratio-Driven Clinical Risk Score for Early Mortality Prediction in Very Elderly Mechanically Ventilated ICU Patients: Multicenter Derivation and External ValidationSemantic proximity Read