The urban sprawl dynamics: does a neural network understand the spatial logic better than a cellular automata?

Cellular Automata are usually considered the most efficient technology to understand the spatial logic of urban dynamics: they are inherently spatial, they are simple and computationally efficient and are able to represent a wide range of pattern and situations. Nevertheless the implementation of a CA requires the formulation of explicit spatial rules which represents the greatest limit of this approach. Whatever rich and complex the rules are, they don`t are able to capture satisfactorily the variety of the real processes. Recent developments in natural algorithms, and particularly in Artificial Neural Networks (ANN), allow to reverse the approach by learning the rules and the behaviours in urban land use dynamics directly from the Data Base, following a bottom-up process. The basic problem is to discover how and in to what extent the land use change of each cell i at time t+1 is determined by the neighbouring conditions (CA assumptions) or by other social, environmental, territorial features (i.e. political maps, planning rules) which where holding at the previous time t. Once the NN has learned the rules, it is able to predict the changes at time t+2 and following. In this paper we show and discuss the prediction capability of different architectures of supervised and unsupervised ANN. The Case study and Data Base concern the land use dynamics, between two temporal thresholds, in the South metropolitan area of Milan. The records have been randomly split in two sets which have been alternatively used in Training and in Testing phase in each ANN. The different ANNs performances have been evaluated with Statistical Functions. Finally, for the prediction, we have used the average of the prediction values of the 10 ANNs, and tested the results through the usual Statistical Functions.

Artificial neural networks allow the use of simultaneous measurements of Alzheimer Disease markers for early detection of the disease

BACKGROUND: Previous studies have shown that in platelets of mild Alzheimer Disease (AD) patients there are alterations of specific APP forms, paralleled by alteration in expression level of both ADAM 10 and BACE when compared to control subjects. Due to the poor linear relation among each key-element of beta-amyloid cascade and the target diagnosis, the use of systems able to afford non linear tasks, like artificial neural networks (ANNs), should allow a better discriminating capacity in comparison with classical statistics. OBJECTIVE: To evaluate the accuracy of ANNs in AD diagnosis. METHODS: 37 mild-AD patients and 25 control subjects were enrolled, and APP, ADM10 and BACE measures were performed. Fifteen different models of feed-forward and complex-recurrent ANNs (provided by Semeion Research Centre), based on different learning laws (back propagation, sine-net, bi-modal) were compared with the linear discriminant analysis (LDA). RESULTS: The best ANN model correctly identified mild AD patients in the 94% of cases and the control subjects in the 92%. The corresponding diagnostic performance obtained with LDA was 90% and 73%. CONCLUSION: This preliminary study suggests that the processing of biochemical tests related to beta-amyloid cascade with ANNs allows a very good discrimination of AD in early stages, higher than that obtainable with classical statistics methods

Artificial neural networks in the recognition of the presence of thyroid disease in patients with atrophic body gastritis

AIM: To investigate the role of artificial neural networks in predicting the presence of thyroid disease in atrophic body gastritis patients

Upper GI endoscopy in elderly patients: predictive factors of relevant endoscopic findings

Elderly patients are at increased risk for peptic ulcer and cancer. Predictive factors of relevant endoscopic findings at upper endoscopy in the elderly are unknown. This was a post hoc analysis of a nationwide, endoscopic study. A total of 3,147 elderly patients were selected. Demographic, clinical, and endoscopic data were systematically collected. Relevant findings and new diagnoses of peptic ulcer and malignancy were computed. Both univariate and multivariate analyses were performed. A total of 1,559 (49.5%), 213 (6.8%), 93 (3%) relevant findings, peptic ulcers, and malignancies were detected. Peptic ulcers and malignancies were more frequent in >85-year-old patients (OR 3.1, 95% CI = 2.0-4.7, p = 0.001). The presence of dysphagia (OR = 5.15), weight loss (OR = 4.77), persistent vomiting (OR = 3.68), anaemia (OR = 1.83), and male gender (OR = 1.9) were significantly associated with a malignancy, whilst overt bleeding (OR = 6.66), NSAIDs use (OR = 2.23), and epigastric pain (OR = 1.90) were associated with the presence of peptic ulcer. Peptic ulcer or malignancies were detected in 10% of elderly patients, supporting the use of endoscopy in this age group. Very elderly patients appear to be at higher risk of such lesions

Appropriateness guidelines and predictive rules to select patients for upper endoscopy: a nationwide multicenter study

Selecting patients appropriately for upper endoscopy (EGD) is crucial for efficient use of endoscopy. The objective of this study was to compare different clinical strategies and statistical methods to select patients for EGD, namely appropriateness guidelines, age and/or alarm features, and multivariate and artificial neural network (ANN) models