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dc.contributor.authorVasiljeva, Ieva
dc.contributor.authorArandelovic, Ognjen
dc.date.accessioned2018-07-06T23:34:21Z
dc.date.available2018-07-06T23:34:21Z
dc.date.issued2017-08-01
dc.identifier.citationVasiljeva , I & Arandelovic , O 2017 , ' Diagnosis prediction from electronic health records (EHR) using the binary diagnosis history vector representation ' , Journal of Computational Biology , vol. 24 , no. 8 , pp. 767-768 . https://doi.org/10.1089/cmb.2017.0023en
dc.identifier.issn1066-5277
dc.identifier.otherPURE: 249668189
dc.identifier.otherPURE UUID: 143aa4f0-aaea-4ef2-9a47-2d606334bd3a
dc.identifier.otherScopus: 85032199900
dc.identifier.otherWOS: 000406243000005
dc.identifier.urihttp://hdl.handle.net/10023/15079
dc.description.abstractLarge amounts of rich, heterogeneous information nowadays routinely collected by health care providers across the world possess remarkable potential for the extraction of novel medical data and the assessment of different practices in real-world conditions. Specifically in this work our goal is to use Electronic Health Records (EHRs) to predict progression patterns of future diagnoses of ailments for a particular patient, given the patient’s present diagnostic history. Following the highly promising results of a recently proposed approach which introduced the diagnosis history vector representation of a patient’s diagnostic record, we introduce a series of improvements to the model and conduct thorough experiments that demonstrate its scalability, accuracy, and practicability in the clinical context. We show that the model is able to capture well the interaction between a large number of ailments which correspond to the most frequent diagnoses, show how the original learning framework can be adapted to increase its prediction specificity, and describe a principled, probabilistic method for incorporating explicit, human clinical knowledge to overcome semantic limitations of the raw EHR data.
dc.format.extent20
dc.language.isoeng
dc.relation.ispartofJournal of Computational Biologyen
dc.rights© 2017, Mary Ann Liebert. This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at online.liebertpub.com / https://doi.org/10.1089/cmb.2017.0023en
dc.subjectBayesianen
dc.subjectDiseaseen
dc.subjectElectronic medical recordsen
dc.subjectEMRsen
dc.subjectEpidemiologyen
dc.subjectRisken
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectQH301 Biologyen
dc.subjectRA Public aspects of medicineen
dc.subjectNDASen
dc.subject.lccQA75en
dc.subject.lccQH301en
dc.subject.lccRAen
dc.titleDiagnosis prediction from electronic health records (EHR) using the binary diagnosis history vector representationen
dc.typeJournal articleen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.identifier.doihttps://doi.org/10.1089/cmb.2017.0023
dc.description.statusPeer revieweden
dc.date.embargoedUntil2018-07-07


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