Identifying Distinct Subgroups of ICU Patients: A Machine Learning Approach.

about

New perspectives to improve critical care benchmarking.

P2860

Q49356234-7F809623-4278-4DCB-B55F-9E12E2983EA1

P2860

Identifying Distinct Subgroups of ICU Patients: A Machine Learning Approach.

Item

http://www.wikidata.org/entity/Q38668056

description

2017 nî lūn-bûn

@nan

2017年の論文

@ja

2017年論文

@yue

2017年論文

@zh-hant

2017年論文

@zh-hk

2017年論文

@zh-mo

2017年論文

@zh-tw

2017年论文

@wuu

2017年论文

@zh

2017年论文

@zh-cn

name

Identifying Distinct Subgroups of ICU Patients: A Machine Learning Approach.

@en

type

Item

label

Identifying Distinct Subgroups of ICU Patients: A Machine Learning Approach.

@en

prefLabel

Identifying Distinct Subgroups of ICU Patients: A Machine Learning Approach.

@en

P1476

Identifying Distinct Subgroups of ICU Patients: A Machine Learning Approach

@en

P2093

Arnold S Milstein

http://www.w3.org/2001/XMLSchema#string

Christopher G Slatore

http://www.w3.org/2001/XMLSchema#string

Gabriel J Escobar

http://www.w3.org/2001/XMLSchema#string

Jeffrey K Jopling

http://www.w3.org/2001/XMLSchema#string

Kelly C Vranas

http://www.w3.org/2001/XMLSchema#string

Meghan C Ramsey

http://www.w3.org/2001/XMLSchema#string

Vincent X Liu

http://www.w3.org/2001/XMLSchema#string

P2860

Diagnosis of multiple cancer types by shrunken centroids of gene expression

Evaluation Following Staggered Implementation of the "Rethinking Critical Care" ICU Care Bundle in a Multicenter Community Setting.

Development and validation of an electronic medical record-based alert score for detection of inpatient deterioration outside the ICU.

Piloting electronic medical record-based early detection of inpatient deterioration in community hospitals.

Early detection of impending physiologic deterioration among patients who are not in intensive care: development of predictive models using data from an automated electronic medical record.

Expanding a performance improvement initiative in critical care from hospital to system.

Nonelective Rehospitalizations and Postdischarge Mortality: Predictive Models Suitable for Use in Real Time.

Association of Intensive Care Unit Admission With Mortality Among Older Patients With Pneumonia

The prognostic landscape of genes and infiltrating immune cells across human cancers

The Natural History of Changes in Preferences for Life-Sustaining Treatments and Implications for Inpatient Mortality in Younger and Older Hospitalized Adults

P304

1607-1615

http://www.w3.org/2001/XMLSchema#string

P31

scholarly article

P356

10.1097/CCM.0000000000002548

http://www.w3.org/2001/XMLSchema#string

P407

English

P433

http://www.w3.org/2001/XMLSchema#string

P478

http://www.w3.org/2001/XMLSchema#string

P50

Timothy E Sweeney

P577

2017-10-01T00:00:00Z

http://www.w3.org/2001/XMLSchema#dateTime

P698

28640021

http://www.w3.org/2001/XMLSchema#string

P921

machine learning

P932

5600667

http://www.w3.org/2001/XMLSchema#string

Identifying Distinct Subgroups of ICU Patients: A Machine Learning Approach.

about

P2860

P2860

Identifying Distinct Subgroups of ICU Patients: A Machine Learning Approach.

description

name

type

label

prefLabel

P1433

P1476

P2093

P2860

P304

P31

P356

P407

P433

P478

P50

P577

P698

P921

P932

P356

P1433

P1476

P2093

P2860

P304

P31

P356

P407

P433

P478

P50

P577

P698

P921

P932