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Pregled in uporaba metod za populacijsko analizo ritmičnosti
ID Velikajne, Nina (Author), ID Moškon, Miha (Mentor) More about this mentor... This link opens in a new window, ID Rozman, Damjana (Comentor)

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Abstract
V okviru magistrskega dela smo vzpostavili metodologijo za analizo populacijske ritmičnosti. Implementirali smo zbirko funkcij, ki jih lahko neposredno uporabimo pri analizi ritmičnih longitudinalnih podatkov. Za analizo tovrstnih podatkov je potrebno združiti ritmične metode z metodami za analizo longitudinalnih podatkov. Implementacija omogoča uporabo treh ritmičnih metod, tj. COSOPT, cosinor in ARSER. Predlagana implementacija omogoča uporabo večkomponentnih metod cosinor in ARSER. Za analizo longitudinalnih podatkov smo implementirali tri različne metode, tj. povprečenje individualnih modelov, GEE modele in modele z mešanimi učniki. Implementacija omogoča uporabniku testiranje različnih kombinacij ritmičnih in longitudinalnih metod. Implementirano metodologijo smo naprej testirali na generiranih sintetičnih podatkih. Kot najbolj robustna kombinacija se je izkazala uporaba metode cosinor in modelov GEE. Uporabo modelov GEE in modelov z mešanimi učinki smo demonstrirali na realnih podatkih, pridobljenih na podlagi meritev pametnih ur. Kombinacijo ritmične metode cosinor in GEE modela, ki se je izkazala kot najbolj robustna pri analizi sintetičnih podatkov, smo preizkusili še na konkretnih eksperimentalnih podatkih bolnikov z obstruktivno spalno apnejo.

Language:Slovenian
Keywords:populacijski podatki, ritmični podatki, regresija, cirkadiane metode
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FRI - Faculty of Computer and Information Science
Year:2024
PID:20.500.12556/RUL-154845 This link opens in a new window
COBISS.SI-ID:191044611 This link opens in a new window
Publication date in RUL:06.03.2024
Views:306
Downloads:368
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Secondary language

Language:English
Title:Review and Application of Computational Approaches for Population-Based Analysis of Rhythmicity
Abstract:
As part of the master's thesis, we have established a methodology for analyzing population rhythmicity. We have implemented a set of functions that can be directly used for the analysis of rhythmic longitudinal data. Analyzing such data requires combining rhythmic methods with methods for longitudinal data analysis. The implementation enables the use of three rhythmic methods, namely COSOPT, cosinor, and ARSER. The proposed implementation allows for the use of multi-component methods cosinor and ARSER. For the analysis of longitudinal data, we have implemented three different methods, namely averaging individual models, GEE models, and mixed-effects models. The implementation allows users to test various combinations of rhythmic and longitudinal methods. We tested the implemented methodology on generated synthetic data. The most robust combination proved to be the use of the cosinor method and GEE models. We demonstrated the use of GEE models and mixed-effects models on real data obtained from smartwatch measurements. We tested the combination of the cosinor rhythmic method and GEE model, which proved to be the most robust in the analysis of synthetic data, on concrete experimental data of patients with open sleep apnea.

Keywords:population data, rhythmic data, regression, circadian methods

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