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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20210402T160552Z
LOCATION:Track 3
DTSTART;TZID=America/New_York:20201113T155500
DTEND;TZID=America/New_York:20201113T161000
UID:submissions.supercomputing.org_SC20_sess222_ws_cafcw111@linklings.com
SUMMARY:Toward a Data-Driven System for Personalized Cervical Cancer Scree
 ning
DESCRIPTION:Workshop\n\nToward a Data-Driven System for Personalized Cervi
 cal Cancer Screening\n\nLangberg\n\nMass-screening programs for cervical c
 ancer in the Nordic countries have a proven strong effect for preventing c
 ancer at the population level and have produced large amounts of data at c
 entrally organized at nationwide registries. Despite this success, minimiz
 ing over-screening and under-treatment remains a major challenge. The main
  difficulties in deriving personalized models from the cancer screening da
 ta are due to its high scarcity, irregularity and skewness. In this paper,
  we present a novel approach based on matrix factorization for personalize
 d time-dependent risk assessment of cervical cancer development. This can 
 be cast as a time-series prediction model, where data from each female is 
 represented as a sparse vector in the time dimension and data from the who
 le population is collected in a single matrix. We explore the latent struc
 ture of this matrix by imposing novel temporal regularization, and derive 
 a small number of basic profiles to describe the population.\n\nWe validat
 e the algorithm on both synthetic and real data from the Cancer Registry o
 f Norway and demonstrate the potential for more efficient and personalized
  cancer screening by showing that the proposed approach can predict the ri
 sk of cervical cancer development up to 36 months ahead in time.\n\nRegist
 ration Category: Workshop Reg Pass
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