Bachelor and Master Theses

Title: INTELLIGENT MATCHING FOR CLINICAL DECISION SUPPORT SYSTEM FOR CEREBRAL PALSY USING DOMAIN KNOWLEDGE
Subject: Computer Science
Level: Basic or Advanced (contact supervisor)
Description: Relevant information at the right time can be critically important for clinicians when treating patients with cerebral palsy (CP). Gathering this information could be done through the usage of a clinical decision support system with a matching algorithm that finds relevant patients. The relevancy of this information for clinicians is determined by the relevancy of the matched patients. The aim of this thesis was therefore to investigate how an algorithm that matches similar patients with CP could be improved in terms of relevancy. The goal was also to explore the possibilities of domain knowledge and temporal aspects and how they could be combined and utilized in order to improve the matching algorithm. In this bachelor's thesis, we have conducted a literature study about the domain and a domain knowledge survey. The domain knowledge survey included gathering domain knowledge through contact with an expert in the area of CP. We also implemented an algorithm using intelligent similarity measurements based on validation from experts that could accurately match similar patients according to the domain knowledge gathered. The resulting algorithm is presented through a prototype of a CDSS, which allows clinicians to select and match patients through a GUI, and including features such as adjusting weight values for different attributes. The algorithm uses patient data retrieved from the CPUP database, which is specific to patients with CP, to match with. From the CPUP database many temporal aspects could be concluded to be relevant for similarity assessment. Due to the limited scope of the thesis however, only the most important aspect was utilized. By treating this aspect as an attribute like the other domain knowledge based attributes, but with respect to other variables that affected it, a combination of temporal aspects and domain knowledge was done when identifying similar patients with CP. Using the prototype of the CDSS with the implemented algorithm could help clinicians make better informed decisions, and this leads to improved health care for children and patients with CP, which is why this thesis was important.
Start date: 20170301
Prel. end date: 20170631
Presentation date: 20170530
Student: Filiph Eriksson-Falk ffk13001@student.mdh.se
Student: Fredrik Frenning ffg12002@student.mdh.se
IDT supervisor: Peter Funk
peter.funk@mdh.se, +46-21-103153
Examinator: Ning Xiong
Ning Xiong
ning.xiong@mdh.se, +46-21-151716

Rapport och bilagor

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TR1611.pdf

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2017-05-24, 23:39


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