Assessment of machine-learning techniques on large pathology data sets to address assay redundancy in routine liver function test profiles

Diagnosis

Volumn 2, Issue 1, 2015, Pages 41-51

  Lidbury, Brett A ^a   Richardson, Alice M ^a,b   Badrick, Tony ^c  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^b UNIVERSITY OF CANBERRA   (Australia)

^c BOND UNIVERSITY   (Australia)

Author keywords

Clinical chemistry; liver function test; machine learning; glutamyl transferase (GGT)

Indexed keywords

ALANINE AMINOTRANSFERASE; ALBUMIN; ALKALINE PHOSPHATASE; ASPARTATE AMINOTRANSFERASE; BILIRUBIN; CHOLESTEROL; GAMMA GLUTAMYLTRANSFERASE; LACTATE DEHYDROGENASE; URATE;

ACCURACY; ALANINE AMINOTRANSFERASE BLOOD LEVEL; ALBUMIN BLOOD LEVEL; ARTICLE; ASPARTATE AMINOTRANSFERASE BLOOD LEVEL; BILIRUBIN BLOOD LEVEL; CHOLESTEROL BLOOD LEVEL; CLINICAL CHEMISTRY; DECISION TREE; HUMAN; KNOWLEDGE DISCOVERY; LACTATE DEHYDROGENASE BLOOD LEVEL; LIVER FUNCTION TEST; MACHINE LEARNING; PRIORITY JOURNAL; REFERENCE VALUE; SUPPORT VECTOR MACHINE; URIC ACID BLOOD LEVEL;

EID: 84992227117     PISSN: 21948011     EISSN: 2194802X     Source Type: Journal    
DOI: 10.1515/dx-2014-0063     Document Type: Article

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