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Analiza omrežja simptomi-bolezni pri človeku
MALENŠEK, SIMONA (Author), Šubelj, Lovro (Mentor) More about this mentor... This link opens in a new window

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Abstract
Ugotavljanje povezav med simptomi in boleznimi je za diagnostiko in zdravljenje ključnega pomena, saj vplivajo na razumevanje bolezni in oblikovanje zdravil. S pomočjo analize omrežij lahko te povezave podrobno preučimo, zanje izračunamo različne mere in odkrivamo morebitne vzorce. Načinov, kako povezati simptome in bolezni, je več, na primer, da povezave predstavljajo število skupnih pojavitev v osnutkih znanstvenih člankov. V diplomskem delu omrežje simptomov in bolezni zgradimo tako, da za njihove uteži uporabimo število zadetkov, ki jih vrne iskalnik Google za posamezno kombinacijo simptoma in bolezni. Osredotočimo se na projekcijo bolezni na podlagi skupnih simptomov in zanje s pomočjo različnih algoritmov poiščemo skupnosti. Tako pridobljene rezultate analiziramo in interpretiramo, ter jih primerjamo z rezultati referenčne raziskave.

Language:Slovenian
Keywords:analiza omrežij, simptomi, bolezni, iskalnik Google, odkrivanje skupnosti
Work type:Bachelor thesis/paper (mb11)
Organization:FRI - Faculty of computer and information science
Year:2019
COBISS.SI-ID:1538346691 Link is opened in a new window
Views:259
Downloads:100
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Secondary language

Language:English
Title:Analysis of human symptoms-disease network
Abstract:
Identifying the links between symptoms and diseases is crucial for diagnosis and treatment, as they affect understanding of the disease and the development of medication. Through network analysis, we can examine these connections in detail by calculating different measures for them and identifying potential patterns. There are several ways to build a network of symptoms and diseases, for example, by linking them with the number of co-occurrences in abstracts of scientific articles. In the thesis, we build a network of symptoms and diseases by using the number of Google Search hits as the edge weight for each combination of symptom and disease. We focus on the network’s projection on diseases based on common symptoms and use different algorithms to find communities of diseases. The results obtained are analyzed, interpreted and compared with the results of a reference study.

Keywords:network analysis, symptoms, diseases, Google Search, community detection

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