AI IN CONTEXT BASED STATISTICS IN CLINICAL DECISION SUPPORT

Some treatments may cause unwanted effects and may make it difficult to achieve an optimal personalised decision for a specific patient. Decision support systems in healthcare is a topic that is getting much attention today. The purpose of using such a system is to enhance treatment's quality and to make it easier for clinicians to process and providing information by having access to patient's electronic health record and past experience. In this thesis, the developed a Clinical decision support system (CDSS) that helps clinicians to identify similar patients and extracting relevant experience. The vision here is to enable clinicians to make more informed decisions when choosing a suitable treatment for patient’s condition. So, here we focus on a more generic approach using case-based reasoning (CBR) and clustering in order to enable context-based statistics for a wider usage of CDSS in healthcare. We are testing our framework on a specific register that considers patients with cerebral pares and their ability to walk. In addition, the solution in our framework will measure how much the range of motions during the foot changes (increase or decrease) before and after an operation of the patient. During this work, an interview has been conducted with a clinical expert to collect requirements to develop such systems. The main function of the system is to check if a patient is similar to any previous patients so the clinician can get relevant information in choosing better treatment solution for a patient. The clinician involved in the project was convinced that our approach could become a valuable tool in a clinical decision-making situation.