Effects of Type 2 Diabetes on the Neuropsychological Profile in Mild Cognitive Impairment.

Background: Diabetes is one of the main risk factors for developing mild cognitive impairment (MCI) and Alzheimer's disease. Most studies have demonstrated a worse performance in executive function, verbal fluency, and information processing speed in patients with diabetes. Objective: To assess the cognitive functioning of persons with type 2 diabetes and amnesic mild cognitive impairment (aMCI-T2DM) compared to persons with aMCI without diabetes and persons without diabetes or aMCI as controls, to understand the role of diabetes in the neuropsychological profile. Methods: Cross-sectional study involving a sample of 83 patients, ranging in age from 61 to 85 years and divided into three groups: aMCI-T2DM (27 patients), aMCI (29 patients), Controls (27 individuals). All the participants undertook an exhaustive neuropsychological assessment (auditory-verbal and visual memory, attention, information processing speed, language, executive function, and depression). Results: Both groups of aMCI patients performed significantly worse than the controls in all the neuropsychological tests. A significant linear tendency (p trend < 0.05) was found between groups, with the aMCI-T2DM group presenting worse results in global cognition assessed by the Mini-Mental State Examination and Montreal Cognitive Assessment; Rey-Osterrieth Complex Figure Test; Auditory Verbal Learning Test; Trail Making Test A and B, Verbal Fluency Test, and Hamilton Depression Rating Scale. Conclusions: aMCI patients with or without diabetes showed worse cognitive function compared to persons without diabetes or aMCI. Additionally, aMCI patients without T2DM presented a different cognitive profile than aMCI patients with T2DM, which tended towards presenting worse cognitive functions such as global cognition, memory, attention, executive function, and language.

Cohort selection for clinical trials using deep learning models.

OBJECTIVE: The goal of the 2018 n2c2 shared task on cohort selection for clinical trials (track 1) is to identify which patients meet the selection criteria for clinical trials. Cohort selection is a particularly demanding task to which natural language processing and deep learning can make a valuable contribution. Our goal is to evaluate several deep learning architectures to deal with this task. MATERIALS AND METHODS: Cohort selection can be formulated as a multilabeling problem whose goal is to determine which criteria are met for each patient record. We explore several deep learning architectures such as a simple convolutional neural network (CNN), a deep CNN, a recurrent neural network (RNN), and CNN-RNN hybrid architecture. Although our architectures are similar to those proposed in existing deep learning systems for text classification, our research also studies the impact of using a fully connected feedforward layer on the performance of these architectures. RESULTS: The RNN and hybrid models provide the best results, though without statistical significance. The use of the fully connected feedforward layer improves the results for all the architectures, except for the hybrid architecture. CONCLUSIONS: Despite the limited size of the dataset, deep learning methods show promising results in learning useful features for the task of cohort selection. Therefore, they can be used as a previous filter for cohort selection for any clinical trial with a minimum of human intervention, thus reducing the cost and time of clinical trials significantly.