No abstract

Background: A cheap and minimum-invasive method for early identification of Alzheimer’s disease (AD) pathogenesis is key to disease management and the success of emerging treatments targeting the prodromal phases of the disease. Objective: To develop a machine learning-based blood panel to predict the progression from mild cognitive impairment (MCI) to dementia due to AD within a four-year time-to-conversion horizon. Methods: We created over one billion models to predict the probability of conversion from MCI to dementia due to AD and chose the best-performing one. We used Alzheimer’s Disease Neuroimaging Initiative (ADNI) data of 379 MCI individuals in the baseline visit, from which 176 converted to AD dementia. Results: We developed a machine learning-based panel composed of 12 plasma proteins (ApoB, Calcitonin, C-peptide, CRP, IGFBP-2, Interleukin-3, Interleukin-8, PARC, Serotransferrin, THP, TLSP 1-309, and TN-C), and which yielded an AUC of 0.91, accuracy of 0.91, sensitivity of 0.84, and specificity of 0.98 for predicting the risk of MCI patients converting to dementia due to AD in a horizon of up to four years. Conclusion: The proposed machine learning model was able to accurately predict the risk of MCI patients converting to dementia due to AD in a horizon of up to four years, suggesting that this model could be used as a minimum-invasive tool for clinical decision support. Further studies are needed to better clarify the possible pathophysiological links with the reported proteins.

No abstract

In this paper, we address the problem of scheduling jobs in a no-wait flow shop with sequence-dependent setup times with the objective of minimizing the make span and the total flow time. As this problem is well-known for being NPhard, we present two new constructive heuristics in order to obtain good approximate solutions for the problem in a short CPU time, named GAPH and QUARTS. GAPH is based on a structural property for minimizing make span and QUARTS breaks the problem in quartets in order to minimize the total flow time. Experimental results demonstrate the superiority of the proposed approaches over three of the best-know methods in the literature: BAH and BIH, from Bianco, Dell´Olmo and Giordani (1999) and TRIPS, by Brown, McGarvey and Ventura (2004).