Detecting bipolar depression from geographic location data

IEEE Transactions on Biomedical Engineering

Volumn 64, Issue 8, 2017, Pages 1761-1771

  Palmius, N ^a   Tsanas, A ^a,b   Saunders, K E A ^a   Bilderbeck, A C ^a   Geddes, J R ^a   Goodwin, G M ^a   De Vos, M ^a,b  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

Bipolar disorder; Classification; Community study; Depression; Geolocation

Indexed keywords

CLASSIFICATION (OF INFORMATION); DIAGNOSIS; DISCRIMINANT ANALYSIS; LINEAR REGRESSION; LOCATION; MHEALTH; REGRESSION ANALYSIS; SURVEYS;

BIPOLAR DISORDER; COMMUNITY STUDY; DEPRESSION; FEATURE SELECTION METHODS; GENERALIZED LINEAR MODEL; GEOLOCATIONS; LINEAR REGRESSION MODELS; QUADRATIC DISCRIMINANT ANALYSIS;

FEATURE EXTRACTION;

ADULT; ARTICLE; BIPOLAR DEPRESSION; BIPOLAR DISORDER; COMMUNITY; DEPRESSION; DISCRIMINANT ANALYSIS; DISEASE CLASSIFICATION; ENTROPY; FEMALE; HEALTH CARE PERSONNEL; HUMAN; MAJOR CLINICAL STUDY; MALE; MEDICAL GEOGRAPHY; MOBILE PHONE; PROSPECTIVE STUDY; QUESTIONNAIRE; ACTIMETRY; GEOGRAPHIC INFORMATION SYSTEM; MIDDLE AGED; PATIENT IDENTIFICATION; PROCEDURES; REMOTE SENSING; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ACTIGRAPHY; ADULT; BIPOLAR DISORDER; CELL PHONES; FEMALE; GEOGRAPHIC INFORMATION SYSTEMS; HUMANS; MALE; MIDDLE AGED; PATIENT IDENTIFICATION SYSTEMS; REMOTE SENSING TECHNOLOGY; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 85029229029     PISSN: 00189294     EISSN: 15582531     Source Type: Journal    
DOI: 10.1109/TBME.2016.2611862     Document Type: Article

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