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Principal Investigator  
Principal Investigator's Name: Anuschka da Silva Spinola
Institution: University of Coimbra
Department: Laboratory of Neurochemistry, CIBB
Country:
Proposed Analysis: For the development and application of my PhD project (which was evaluated by a scientific board in health sciences from FCT in Portugal). I am citing some fragments, bellow is the abstract: "Dementia is a major health issue among older people with more than 47.5 million globally, with a serious impact on families, health-care systems and society. Alzheimer’s disease (AD) represents more than 60% of dementia cases, therefore looking for effective treatments stands as an imperative challenge. The focus relies on therapies that target the early-AD stage, mild cognitive impairment (MCI), preventing or delaying progression to dementia. With an expanding cohort of elderlies with multiple-comorbidities, the complexity of achieving a correct diagnosis and predictive approaches rises. To handle the growing amount of information, machine learning strategies provide an excellent tool in Dementia Clinics by improving classification, processing large amounts of information, lowering costs and predicting outcomes. Acknowledging these necessities, we aim to develop a biomarker-based model to predict progression in AD-spectrum, optimized for the patients at Coimbra University Hospital and to apply it in the clinical-setting to evaluate longitudinal performance." And the objectives: "Our main objective is developing a biomarker-based model to predict progression in early AD, optimized for the patients at Dementia Clinic of CHUC. To achieve this goal, we aim to perform the study as follows: First, we want to generate a theoretical model based on the ADNI database. We plan to perform a pre-analytical screening of the data to select the best features from clinical, neuropsychological, neuroimaging (MRI), biofluids and genetics (Apolipoprotein-E polymorphism – APOE-E4) from the ADNI database. Additionally, create and evaluate several machine learning strategies such as support vector machine, convolutional neural networks, random forests, k nearest neighbor and decision trees to estimate performance; and to establish a time-to-event assessment, for progression from MCI to AD. Second, we expect to apply the optimized predictive model in our clinical context with a longitudinal assessment. The initial step is to compose an internal database of the CHUC-cohort according to the guidelines from ADNI, with comprehensive clinical/neuropsychological information, MRI-structural measures, CSF or PET biomarkers and APOE. Afterwards we intent to test the machine learning algorithms based on the ADNI data with data provided from the European Alzheimer’s Disease Consortium (EADC) and our retrospective data, in order to evaluate variability and performance. We aim to optimize the model to fit the CHUC-cohort normalizing the data and/or improving the algorithm. Being successful, we expect to structure a protocol for standardization of the data of MCI patients to be use into the predictive model on real-time evaluating performance throughout the follow-ups. We expect to deliver a model that would be accurate, practical and complementary to the diagnosis in the Dementia Clinic, suitable to the Portuguese population. We aim to provide a novel technological strategy that would be applied to personalized medicine and give proper classification for individuals in clinical trials." If any more information is necessary I would gladly supply it. This project was develop by me with the assistance of my supervisors which are from different fields to guarantee a successful execution. It would help increase the well-being of the patients of the Dementia Clinic at Coimbra University Hospital.
Additional Investigators