Browsing by Author "Talvet, Annika"

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    Antibiootikumide kasutamine suurtes Eesti piimaveisekarjades
    (2022) Talvet, Annika; Kaart, Tanel, juhendaja; Kalmus, Piret, juhendaja; Tartu Ülikool. Matemaatika ja statistika instituut; Tartu Ülikool. Loodus- ja täppisteaduste valdkond
    Käesoleva bakalaureusetöö eesmärk on välja selgitada, kui palju, milliseid ja mis põhjustel suurtes Eesti piimafarmides antibiootikume kasutatakse. Töö baseerub aastatel 2018-2021 48 piimafarmis kogutud lehmade antibiootikumiravi andmetel. Esmalt antakse ülevaade uuringus osalenud piimafarmidest ja raviandmete struktuurist. Seejärel analüüsitakse ravisid ja ravikuure, ravitud haiguseid ja sümptomeid. Viimaseks vaadeldakse kasutatud ravimeid ja toimeaineid ning toimeainete kasutamist ja koguseid erinevate haiguste ja sümptomite korral ning erinevates farmides.
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    Laborianalüüside diskretiseerimine ja analüüs
    (Tartu Ülikool, 2024) Talvet, Annika; Laur, Sven, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
    When interpreting the results of patients’ clinical analyses, reference ranges are important as they define the range within which a measurement result could fall for a healthy individual. These ranges can depend on age and gender, but may also vary depending on the methodology used in a particular laboratory. Using analysis results that are discretized based on reference ranges simplifies data analysis and model training. However, analysis results may be associated with incorrect LOINC codes or units of measurement. The aim of this Master’s thesis is to identify analyses and reference ranges grouped incorrectly or with incorrect units. Additionally, it aims to investigate whether discretized analysis results are beneficial for predicting medical events and if there is a difference in prediction accuracy using different discretization methods. In order to identify incorrectly grouped analysis results, the data was clustered using a Gaussian mixture model. To assess the predictive capability of discretized results, dependencies between the occurrence of medical events and differently discretized measurements, as well as measurement facts, were examined and models were trained to predict the occurrence of medical events. The results revealed that there is no significant difference in the prediction accuracy between models using different inputs. This suggests that in predicting medical events, the occurrence of measurement is as important as the discretized analysis result.