Disease state index and disease state fingerprint: supervised learning applied to clinical decision support in Alzheimer’s disease
Research output: Book/Report › Doctoral thesis › Collection of Articles
|Place of Publication||Espoo|
|Number of pages||96|
|Publication status||Published - 9 May 2014|
|Publication type||G5 Doctoral dissertation (article)|
This thesis presents the Disease State Index (DSI), a supervised machine learning method intended for the analysis of patient data. The DSI comprehensively compares patient data with previously diagnosed cases with or without a disease. Based on this comparison, the method provides an estimate of the state of disease progression in the patient. Interpreting the DSI is made possible by its visual counterpart, the Disease State Fingerprint (DSF), which allows domain experts to gain a comprehensive view of patient data and the state of the disease at a quick glance. In the design and development of these methods, both performance and applicability in clinical use were taken into account equally.
Alzheimer’s disease (AD) is a slowly progressing neurodegenerative disease and one of the largest social and economic burdens in the world today, and it will continue to be so in the future. Studies with large patient cohorts have significantly improved our knowledge of AD during the last decade. This information should be made extensively available at memory clinics to maximize the benefits for diagnostics and treatment of the disease. The DSI and DSF methods proposed in this thesis were studied in the early diagnosis of AD and as a measure of disease progression in six original publications. The methods themselves and their implementation within a clinical decision support system, the PredictAD tool, were quantitatively evaluated with regard to their performance and potential benefits in clinical use. The results show that the methods and clinical decision support tool based on these methods can be used to follow disease progression objectively and provide earlier diagnoses of AD. These, in turn, could improve treatment efficacy due to earlier interventions and make drug trials more efficient by allowing better patient selection.