Browsing by Author "Adamson, Ainika"

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    Assessment of the suitability of the Estonian Health Record data for the prediction of ischemic stroke
    (Tartu Ülikool, 2021) Adamson, Ainika; Haller, Toomas, juhendaja; Alasoo, Kaur, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
    An increase in the instances of cardiovascular diseases has elevated the need for better and more efficient prediction models for ischemic stroke as well. Therefore, it is vitally important to assess the Estonian Health Record laboratory data to find out its suitability for ischemic stroke prediction models. To that effect five different approaches and three methods were utilized in three tiers in this Thesis. The potential of binary statement of measurement facts, as well as the actual analysis results, calculated z-scores and medical reference values were evaluated as the input for prediction models. It was found that the binary statement of measurements itself contained enough information for a competitive prediction model. However, several analytes were identified that had increased the quality of the prediction outcomes and therefore should be studied further.
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    Meetod tõenäosusliku ravimisoovituse andmiseks osalise geeniinfo põhjal
    (2018) Adamson, Ainika; Sulev Reisberg
    Personaalmeditsiinis on pead tõstmas valdkond, kus ravimidoos määratakse inimese geneetika põhjal, sest ravimite ainevahetuse kiirus sõltub suuresti just geneetikast. Geeniinfo põhjal ravimisoovituste tegemine on keeruline. Suurimaks probleemiks on puudulik informatsioon, sest enimlevinud genotüpiseerimise võimalused tuvastavad pärilikkusainest ainult osalise info. Probleemi leevendamiseks luuakse käesolevas töös meetod, mis oskab puuduva info osas teha tõenäosuslikke ennustusi. Meetod kasutab puuduva info asemel varem publitseeritud Euroopa alleelisagedusi, mille abil ennustatakse erinevate metabolismikiiruste esinemistõenäosused. Loodud algoritmi rakendati mitme elutähtsa ravimi ainevahetust reguleeriva geeni CYP2C19 andmetel. Tulemusena selgus, et puuduva geeniinfo korral on võimalik ennustada fenotüüpide tõenäosusi.