Discovering disease trajectories from the Finnish Hospital Discharge Register with the MCL algorithm

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dc.contributor Helsingin yliopisto, Matemaattis-luonnontieteellinen tiedekunta, Matematiikan ja tilastotieteen laitos fi
dc.contributor University of Helsinki, Faculty of Science, Department of Mathematics and Statistics en
dc.contributor Helsingfors universitet, Matematisk-naturvetenskapliga fakulteten, Institutionen för matematik och statistik sv
dc.contributor.author Sandoval Zárate, América Andrea
dc.date.issued 2015
dc.identifier.uri URN:NBN:fi-fe2017112251645
dc.identifier.uri http://hdl.handle.net/10138/157023
dc.description.abstract Personalised medicine involves the use of individual information to determine the best medical treatment. Such information include the historical health records of the patient. In this thesis, the records used are part of the Finnish Hospital Discharge Register. This information is utilized to identify disease trajectories for individuals for the FINRISK cohorts. The techniques usually implemented to analyse longitudinal register data use Markov chains because of their capability to capture temporal relations. In this thesis a first order Markov chain is used to feed the MCL algorithm that identifies disease trajectories. These trajectories highlight the most prevalent diseases in the Finnish population: circulatory diseases, neoplasms and musculoskeletal disorders. Also, they defined high level interactions between other diseases, some of them showing an agreement with physiological interactions widely studied. For example, circulatory diseases and their thoroughly studied association with symptoms from the metabolic syndrome. en
dc.language.iso en
dc.publisher Helsingfors universitet sv
dc.publisher University of Helsinki en
dc.publisher Helsingin yliopisto fi
dc.title Discovering disease trajectories from the Finnish Hospital Discharge Register with the MCL algorithm en
dc.type.ontasot pro gradu-avhandlingar sv
dc.type.ontasot pro gradu -tutkielmat fi
dc.type.ontasot master's thesis en
dc.subject.discipline Statistics en
dc.subject.discipline Tilastotiede fi
dc.subject.discipline Statistik sv
dct.identifier.urn URN:NBN:fi-fe2017112251645

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