Integrirani dijagnostički postupak za posttraumatski stresni poremećaj

doctoral thesis

• Title (english): Integrative diagnostic algorithm for posttraumatic stress disorder
• Author: Igor Marinić; MBZ: 267113
• Mentor: Dragica Kozarić-Kovačić (mentor)
• Committee member: nije dostupno (član povjerenstva)
• Granter: University of Zagreb; School of Medicine; Zagreb
• Defense date and country: 2014-09-09, Croatia
• Scientific / art field, discipline and subdiscipline: BIOMEDICINE AND HEALTHCARE; Clinical Medical Sciences
• Universal decimal classification (UDC): 61 - Medical sciences

Abstract

Dijagnosticiranje posttraumatskog stresnog poremećaja (PTSP) je složen postupak, što se očituje u kliničkom i forenzičkom radu te istraživanjima. Zbog složenosti dijagnosticiranja PTSP-a postoji potreba za formiranjem dodatnih i sveobuhvatnijih dijagnostičkih modela. Namjera rada je bila da se primjenom postupaka inteligentne analize podataka (eng. data mining) ostvari integracija podataka vezanih uz psihijatrijske bolesnike s PTSP-om (anamnestički podaci, podaci iz psihijatrijskih i psihologijskih upitnika i obrade), da se iz tih podataka izdvoje značajne varijable i pravila te formira postupnik koji bi doprinjeo unaprijeđenju i objektivizaciji dijagnostičkog postupka. Provedenom analizom podataka izdvojene su značajne varijable i pravila koji karakteriziraju bolesnike s PTSP-om, provedena je regresijska analiza rezultata i zatim je korištenjem značajnih varijabli i pravila formiran dijagnostički postupnik za PTSP. Ovo je prvo istraživanje ovog tipa u psihijatriji, korištenjem metoda inteligentne analize, usmjereno na integraciju širokog skupa podataka i formiranje modela za dijagnosticiranje PTSP-a. Od značaja za kliničku praksu je to što su izdvojene varijable koje mogu pomoći u dijagnostičkom procesu kod bolesnika sa PTSP-om. Uz to, razvijen je sustav koji omogućuje prikupljanje velikog broja varijabli ispitanika i njihovo strukturiranje na način koji je pogodan za daljnju obradu korištenjem metoda inteligentne analize podataka, a koji može poslužiti kao osnova za izradu sličnih modela u psihijatriji.

Abstract (english)

The diagnosis of post-traumatic stress disorder (PTSD) is a complex process, which is reflected in clinical and forensic work and research. Because of the complexity of diagnosing PTSD, there is a need for additional and more comprehensive diagnostic models. The intention of this work was to use data mining methods for integration of data related to psychiatric patients with PTSD (personal history, data from psychiatric and psychological questionnaires and evaluation), to select significant variables and rules, and form an algorithm that would contribute to improvement and objectivization of diagnostic procedure. After performing the data analysis, we have extracted significant variables and rules that characterize patients with PTSD, conducted regression analysis and then formed diagnostic algorithm for PTSD. This is the first study of this type in psychiatry, using data mining methods, which is focusing on the integration of a broad set of data and formation of models for diagnosing PTSD. Of importance for clinical practice is that the variables that are found significant may help in the diagnostic process in patients with PTSD. In addition, we have developed a system that allows the collection of a large number of variables and their structuring in a way that is suitable for further processing using data mining methods, which can serve as a basis for the development of similar models for other psychiatric disorders.

• Keywords: medicina
• Keywords (english): medicine
• Language: croatian
• URN:NBN: urn:nbn:hr:105:557456
• Study programme: Title: Biomedicine and Health Sciences; Study programme type: university; Study level: postgraduate; Academic / professional title: doktor/doktorica znanosti, područje biomedicine i zdravstva (doktor/doktorica znanosti, područje biomedicine i zdravstva)
• Type of resource: Text
• Extent: 188 str.
• File origin: Born digital
• Access conditions: Open access
• Created on: 2023-05-05 07:23:13