title: Identifying Physical Activity Profiles in COPD Patients Using Topic Models.
abstract: With the growing amount of physical activity (PA) measures, the need for methods and algorithms that automatically analyze and interpret unannotated data increases. In this paper, PA is seen as a combination of multimodal constructs that can cooccur in different ways and proportions during the day. The design of a methodology able to integrate and analyze them is discussed, and its operation is illustrated by applying it to a dataset comprising data from COPD patients and healthy subjects acquired in daily life. The method encompasses different stages. The first stage is a completely automated method of labeling low-level multimodal PA measures. The information contained in the PA labels are further structured using topic modeling techniques, a machine learning method from the text processing community. The topic modeling discovers the main themes that pervade a large set of data. In our case, topic models discover PA routines that are active in the assessed days of the subjects under study. Applying the designed algorithm to our data provides new learnings and insights. As expected, the algorithm discovers that PA routines for COPD patients and healthy subjects are substantially different regarding their composition and moments in time in which transitions occur. Furthermore, it shows consistent trends relating to disease severity as measured by standard clinical practice.
authors.forename: Gabriele
authors.initials: G
authors.lastname: Spina
authors.forename: Pierluigi
authors.initials: P
authors.lastname: Casale
authors.forename: Paul S
authors.initials: PS
authors.lastname: Albert
authors.forename: Jennifer
authors.initials: J
authors.lastname: Alison
authors.forename: Judith
authors.initials: J
authors.lastname: Garcia-Aymerich
authors.forename: Richard W
authors.initials: RW
authors.lastname: Costello
authors.forename: Nidia A
authors.initials: NA
authors.lastname: Hernandes
authors.forename: Arnoldus J R
authors.initials: AJ
authors.lastname: van Gestel
authors.forename: Jörg D
authors.initials: JD
authors.lastname: Leuppi
authors.forename: Rafael
authors.initials: R
authors.lastname: Mesquita
authors.forename: Sally J
authors.initials: SJ
authors.lastname: Singh
authors.forename: Frank W J M
authors.initials: FW
authors.lastname: Smeenk
authors.forename: Ruth
authors.initials: R
authors.lastname: Tal-Singer
authors.forename: Emiel F M
authors.initials: EF
authors.lastname: Wouters
authors.forename: Martijn A
authors.initials: MA
authors.lastname: Spruit
authors.forename: Albertus C
authors.initials: AC
authors.lastname: den Brinker
mesh_terms: D000368:Aged; D000465:Algorithms; D005260:Female; D006801:Humans; D008297:Male; D008490:Medical Informatics; D008875:Middle Aged; D008962:Models, Theoretical; D018670:Monitoring, Ambulatory / Q000379:methods*; D009043:Motor Activity / Q000502:physiology*; D029424:Pulmonary Disease, Chronic Obstructive / Q000503:physiopathology*; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted