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Projeto de investigação
NEUROCLINOMICS2 - Unravelling Prognostic Markers in NEUROdegenerative diseases through CLINical and OMICS data integration
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Neuropsychological predictors of conversion from mild cognitive impairment to Alzheimer’s disease: a feature selection ensemble combining stability and predictability
Publication . Pereira, Telma; Ferreira, Francisco L.; Cardoso, Sandra; Silva, Dina; De Mendonça, Alexandre; Guerreiro, Manuela; Madeira, Sara C.
Background: Predicting progression from Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) is an utmost open issue in AD-related research. Neuropsychological assessment has proven to be useful in identifying MCI patients who are likely to convert to dementia. However, the large battery of neuropsychological tests (NPTs) performed in clinical practice and the limited number of training examples are challenge to machine learning when learning prognostic models. In this context, it is paramount to pursue approaches that effectively seek for reduced sets of relevant features. Subsets of NPTs from which prognostic models can be learnt should not only be good predictors, but also stable, promoting generalizable and explainable models.
Methods: We propose a feature selection (FS) ensemble combining stability and predictability to choose the most relevant NPTs for prognostic prediction in AD. First, we combine the outcome of multiple (filter and embedded) FS methods. Then, we use a wrapper-based approach optimizing both stability and predictability to compute the number of selected features. We use two large prospective studies (ADNI and the Portuguese Cognitive Complaints Cohort, CCC) to evaluate the approach and assess the predictive value of a large number of NPTs.
Results: The best subsets of features include approximately 30 and 20 (from the original 79 and 40) features, for ADNI and CCC data, respectively, yielding stability above 0.89 and 0.95, and AUC above 0.87 and 0.82. Most NPTs learnt using the proposed feature selection ensemble have been identified in the literature as strong predictors of conversion from MCI to AD.
Conclusions: The FS ensemble approach was able to 1) identify subsets of stable and relevant predictors from a consensus of multiple FS methods using baseline NPTs and 2) learn reliable prognostic models of conversion from MCI to AD using these subsets of features. The machine learning models learnt from these features outperformed the models trained without FS and achieved competitive results when compared to commonly used FS algorithms. Furthermore, the selected features are derived from a consensus of methods thus being more robust, while releasing users from choosing the most appropriate FS method to be used in their classification task.
Targeting the uncertainty of predictions at patient-level using an ensemble of classifiers coupled with calibration methods, Venn-ABERS, and Conformal Predictors: a case study in AD
Publication . Pereira, Telma; Cardoso, Sandra; Guerreiro, Manuela; De Mendonça, Alexandre; Madeira, Sara C.
Despite being able to make accurate predictions, most existing prognostic models lack a proper indication about the uncertainty of each prediction, that is, the risk of prediction error for individual patients. This hampers their translation to primary care settings through decision support systems. To address this problem, we studied different methods for transforming classifiers into probabilistic/confidence-based predictors (here called uncertainty methods), where predictions are complemented with probability estimates/confidence regions reflecting their uncertainty (uncertainty estimates). We tested several uncertainty methods: two well-known calibration methods (Platt Scaling and Isotonic Regression), Conformal Predictors, and Venn-ABERS predictors. We evaluated whether these methods produce valid predictions, where uncertainty estimates reflect the ground truth probabilities. Furthermore, we assessed the proportion of valid predictions made at high-certainty thresholds (predictions with uncertainty measures above a given threshold) since this impacts their usefulness in clinical decisions. Finally, we proposed an ensemble-based approach where predictions from multiple pairs of (classifier, uncertainty method) are combined to predict whether a given MCI patient will convert to AD. This ensemble should putatively provide predictions for a larger number of patients while releasing users from deciding which pair of (classifier, uncertainty method) is more appropriate for data under study. The analysis was performed with a Portuguese cohort (CCC) of around 400 patients and validated in the publicly available ADNI cohort. Despite our focus on MCI to AD prognosis, the proposed approach can be applied to other diseases and prognostic problems.
Spreading in ALS: The relative impact of upper and lower motor neuron involvement
Publication . Gromicho, Marta; Figueiral, Manuel; Uysal, Hilmi; Grosskreutz, Julian; Kuzma‐Kozakiewicz, Magdalena; Pinto, Susana; Petri, Susanne; Madeira, Sara C.; Swash, Michael; Carvalho, Mamede
Objective: To investigate disease spread in amyotrophic lateral sclerosis (ALS), and determine the influence of lower (LMN) and upper motor neuron (UMN) involvement.
Methods: We assessed disease spread in ALS in 1376 consecutively studied patients, from five European centers, applying an agreed proforma to assess LMN and UMN signs. We defined the pattern of disease onset and progression from predominant UMN or lower motor neuron (LMN) dysfunction in bulbar, upper limbs, lower limbs, and thoracic regions Non-linear regression analysis was applied to fit the data to a model that described the relation between two random variables, graphically represented by an inverse exponential curve. We analyzed the probability, rate of spread, and both combined (area under the curve).
Results: We found that progression was more likely and quicker to or from the region of onset to close spinal regions. When the disease had a limb onset, bulbar motor neurons were more resistant. Furthermore, in the same time frame more patients progressed from bulbar to lower limbs than vice-versa, whether predominantly UMN or LMN involvement. Patients with initial thoracic involvement had a higher probability for rapid change. The presence of predominant UMN signs was associated with a faster caudal progression.
Interpretation: Contiguous progression was leading pattern, and predominant UMN involvement is important in shortening the time for cranial-caudal spread. Our results can best be fitted to a model of independent LMN and UMN degeneration, with regional progression of LMN degeneration mostly by contiguity. UMN lesion causes an acceleration of rostral-caudal LMN loss.
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Entidade financiadora
Fundação para a Ciência e a Tecnologia
Programa de financiamento
3599-PPCDT
Número da atribuição
PTDC/EEI-SII/1937/2014