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An interpretable mortality prediction model for COVID-19 patients

published in: Nature Machine Intelligence

date of publication: 2020-05

main subject: COVID-19

Cites articles 16

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2020-01-24

Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China

Yi Hu, Chaolin Huang, Yeming Wang
2020-01-30

Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study

Nanshan Chen
2020-02-24

Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study
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From Local Explanations to Global Understanding with Explainable AI for Trees

Ronit Katz, Jonathan Himmelfarb, Nisha Bansal
2020-03-23

The Toughest Triage - Allocating Ventilators in a Pandemic

Robert D Truog
2020-02-07

Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China

Yong Xiong, Dawei Wang
2020-02

A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster

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2020-02-18

Pathological findings of COVID-19 associated with acute respiratory distress syndrome
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Longitudinal characteristics of lymphocyte responses and cytokine profiles in the peripheral blood of SARS-CoV-2 infected patients
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XGBoost: A Scalable Tree Boosting System

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Aetiology, outcomes & predictors of mortality in acute respiratory distress syndrome from a tertiary care centre in north India

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Staging of acute exacerbation in patients with idiopathic pulmonary fibrosis
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Isolation and characterization of a bat SARS-like coronavirus that uses the ACE2 receptor

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Plasma C-reactive protein levels are associated with improved outcome in ARDS.

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Article - wd:Q107404426

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