Anomaly Detection from Patient Visit Data

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.advisorHollmén, Jaakko
dc.contributor.authorZhao, Yang
dc.contributor.schoolPerustieteiden korkeakoulufi
dc.contributor.supervisorRousu, Juho
dc.date.accessioned2016-11-02T09:41:36Z
dc.date.available2016-11-02T09:41:36Z
dc.date.issued2016-10-27
dc.description.abstractHospital operation cost rises due to the growing demand for outpatient services by increasing elderly population. To reduce the operation cost and serve the patients better, improvements on the efficiency in healthcare service institutes are required. Among several potential aspects of efficiency improvements, smoother patient visits are highly desired. Thanks to the digital era, patient visits to the hospital can be recorded with all details. The Oulu Hospital in Finland starts to gather patient visits data since 2011, using queue system provided by X-Akseli company. Utilizing these collected data, this thesis aims at designing a practical way of detecting anomalies from patient visits. With the help from this system, the hospital administrative staff could analyze the performance of the queue procedure in the hospital and optimize the procedure. Even better, the system can identify anomalies in real-time so that the patient can get immediate help when it is needed. The thesis explored two categories of methods: clustering methods and generative methods. Four candidate algorithms, K-Means, DBSCAN, Markov Chain, and Hidden Markov Model, are discussed. The discussion suggests that DBSCAN and Hidden Markov Model are more practical. Then we proposed a new data representation and used negative binomial distribution in Hidden Markov Model to model patient states durations. The experiment result was visualized using t- SNE and evaluated by user interpretation. The analyses show that both DBSCAN and Hidden Markov Model can effectively detect anomalies from patient visits data. But in terms of time and space complexity, and real-time detection, Hidden Markov Model is a better choice.en
dc.format.extent54
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/23340
dc.identifier.urnURN:NBN:fi:aalto-201611025441
dc.language.isoenen
dc.programmeMaster’s Programme in Computer, Communication and Information Sciencesfi
dc.programme.majorMachine Learning and Data Miningfi
dc.programme.mcodeSCI3044fi
dc.rights.accesslevelopenAccess
dc.subject.keywordsequence dataen
dc.subject.keywordclusteringen
dc.subject.keywordgenerative Markov modelsen
dc.subject.keywordduration modellingen
dc.subject.keywordPoisson distributionen
dc.subject.keywordnegative binomial distributionen
dc.titleAnomaly Detection from Patient Visit Dataen
dc.typeG2 Pro gradu, diplomityöfi
dc.type.okmG2 Pro gradu, diplomityö
dc.type.ontasotMaster's thesisen
dc.type.ontasotDiplomityöfi
dc.type.publicationmasterThesis
local.aalto.idinssi54972
local.aalto.openaccessyes
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