Using support vector machines to optimally classify rotator cuff strength data and quantify post-operative strength in rotator cuff tear patients

Journal of Biomechanics

Volumn 39, Issue 5, 2006, Pages 973-979

  Silver, Aaron E ^a   Lungren, Matthew P ^a   Johnson, Marjorie E ^b   O'Driscoll, Shawn W ^c   An, Kai Nan ^c   Hughes, Richard E ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

^b NORTHWESTERN UNIVERSITY   (United States)

^c MAYO CLINIC   (United States)

Author keywords

Rotator cuff tear; Shoulder strength; Support vector machine

Indexed keywords

DIAGNOSIS; PATIENT MONITORING; REGRESSION ANALYSIS; ULTRASONICS;

SHOULDER STRENGTH; SUPPORT VECTOR MACHINES;

BIOMECHANICS;

ARM MOVEMENT; ARTICLE; CLINICAL ARTICLE; CONTROLLED STUDY; HUMAN; MUSCLE ISOMETRIC CONTRACTION; MUSCLE STRENGTH; PRIORITY JOURNAL; ROTATOR CUFF RUPTURE; SHOULDER GIRDLE;

ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DIAGNOSIS, COMPUTER-ASSISTED; HUMANS; ISOMETRIC CONTRACTION; MODELS, BIOLOGICAL; PATTERN RECOGNITION, AUTOMATED; PHYSICAL EXAMINATION; PROGNOSIS; REPRODUCIBILITY OF RESULTS; ROTATOR CUFF; SENSITIVITY AND SPECIFICITY; TENDON INJURIES; TREATMENT OUTCOME;

EID: 32644450162     PISSN: 00219290     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbiomech.2005.01.011     Document Type: Article

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