Clinical chemistry in higher dimensions: Machine-learning and enhanced prediction from routine clinical chemistry data

Clinical Biochemistry

Volumn 49, Issue 16-17, 2016, Pages 1213-1220

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

^a The Australian National University   (Australia)

^b Biology and Environment   (Australia)

^c RCPAQAP   (Australia)

Author keywords

Anaemia; Big data; Bilirubin; Biomarkers; Hepatitis; Liver function tests; Misconceptions; Predictive modelling; Statistics

Indexed keywords

BILIRUBIN; GAMMA GLUTAMYLTRANSFERASE;

ALGORITHM; ANEMIA; BIOCHEMISTRY; CLINICAL CHEMISTRY; FIT DIAGNOSE; HEPATITIS B; HEPATITIS C; HEPATITIS VIRUS; HUMAN; INFORMATION PROCESSING; LABORATORY DIAGNOSIS; LABORATORY TEST; LEUKOCYTE COUNT; LIVER FUNCTION TEST; MACHINE LEARNING; PREDICTION; PRIORITY JOURNAL; RED BLOOD CELL DISTRIBUTION WIDTH; REVIEW; STATISTICAL SIGNIFICANCE; TRAIN TEST; STATISTICAL ANALYSIS;

CLINICAL CHEMISTRY TESTS; DATA INTERPRETATION, STATISTICAL; MACHINE LEARNING;

EID: 84979692017     PISSN: 00099120     EISSN: 18732933     Source Type: Journal    
DOI: 10.1016/j.clinbiochem.2016.07.013     Document Type: Review

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