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Table representation of search results timeline featuring number of search results per year.

Year Number of Results
1982 1
1988 1
1989 2
1990 1
1991 1
1992 1
1993 2
1994 4
1995 1
1997 7
1998 3
1999 2
2001 3
2002 4
2003 6
2004 2
2005 6
2006 10
2007 10
2008 12
2009 11
2010 8
2011 17
2012 18
2013 16
2014 20
2015 17
2016 36
2017 25
2018 25
2019 20
2020 4
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102 results
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Filters applied: in the last 5 years. Clear all
Page 1
Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU.
Mao Q, et al. BMJ Open 2018 - Clinical Trial. PMID 29374661 Free PMC article.
SETTING: A mixed-ward retrospective dataset from the University of California, San Francisco (UCSF) Medical Center (San Francisco, California, USA) as the primary source, an intensive care unit dataset from the Beth Israel Deaconess Medical Center (Boston, Massachusetts, USA) as a transfer-learning source and four additional institutions' datasets to evaluate generalisability. ...
SETTING: A mixed-ward retrospective dataset from the University of California, San Francisco (UCSF) Medical Center (San Francisco, Ca …
Artificial Intelligence Based Hierarchical Clustering of Patient Types and Intervention Categories in Adult Spinal Deformity Surgery: Towards a New Classification Scheme that Predicts Quality and Value.
Ames CP, et al. Spine (Phila Pa 1976) 2019 - Clinical Trial. PMID 31205167
OBJECTIVE: To apply artificial intelligence (AI)-based hierarchical clustering as a step toward a classification scheme that optimizes overall quality, value, and safety for ASD surgery. ...Both were built with Ward distances and optimized with the gap method. For each possible n patient cluster by m surgery, normalized 2-year improvement and major complication rates were computed. ...
OBJECTIVE: To apply artificial intelligence (AI)-based hierarchical clustering as a step toward a classification scheme that o …
Artificial Intelligence Based Hierarchical Clustering of Patient Types and Intervention Categories in Adult Spinal Deformity Surgery: Towards a New Classification Scheme that Predicts Quality and Value.
Ames CP, et al. Spine (Phila Pa 1976) 2019. PMID 30633115
OBJECTIVE: To apply artificial intelligence (AI)-based hierarchical clustering as a step toward a classification scheme that optimizes overall quality, value, and safety for ASD surgery. ...Both were built with Ward distances and optimized with the gap method. For each possible n patient cluster by m surgery, normalized 2-year improvement and major complication rates were computed. ...
OBJECTIVE: To apply artificial intelligence (AI)-based hierarchical clustering as a step toward a classification scheme that o …
Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards
Churpek MM, et al. Crit Care Med 2016 - Clinical Trial. PMID 26771782 Free PMC article.
PATIENTS: Hospitalized ward patients INTERVENTIONS: None MEASUREMENTS AND MAIN RESULTS: Demographic variables, laboratory values, and vital signs were utilized in a discrete-time survival analysis framework to predict the combined outcome of cardiac arrest, intensive care unit transfer, or death. ...
PATIENTS: Hospitalized ward patients INTERVENTIONS: None MEASUREMENTS AND MAIN RESULTS: Demographic variables, laboratory values, and …
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