Decision Support for Mental Health

Decision support systems represent an important tool offering assistance with the decision making process in a variety of applications. This paper starts with recalling the basic principles and structure of decision support systems in medicine from a general perspective. Their effect in terms of both potential and limitations for finding the diagnosis, prognosis and therapy are overviewed from the points of view of health care effectiveness and patient safety. The authors are particularly interested in the specialty field of psychiatry. They discuss its specific challenges and analyze the slower penetration of telemedicine tools to psychiatry compared to other clinical fields. Finally, they claim that the development of decision support systems play a key role in the development of the concept of information-based medicine in general as well as to the particular context of information-based psychiatry.

Development of Stress Management System for Prevention of a Mental Disease using Smart Finger Plethysmogram Measurement

In this modern society, with its multitude of stressors that people encounter on a daily basis, a characteristic of mental disorders is that there is a risk of developing them at the unconscious level, and even if the patient were to detect the condition, they are difficult to treat. Furthermore, while there are tests that evaluate the level of stress, these tests still have many elements. Therefore, it is extremely important to be able to objectively assess ones stress levels, as well as to raise awareness of and pay attention to internal signals in order to control the level of risk, to create a mechanism which provides medical help. Thus, this study aims to visualize the internal signals through the heart rate which is affected by stress, develop a system to provide assistance in returning stress to normal levels, and assisting in helping patients manage their own risk levels.

Subspace Clustering of DNA Microarray Data

Identification of biological significant subspace clusters (biclusters and triclusters) of genes from microarray experimental data is a very daunting task that emerged, especially with the development of high throughput technologies. Several methods and applications of subspace clustering (biclustering and triclustering) in DNA microarray data analysis have been developed in recent years. Various computational and evaluation methods based on diverse principles were introduced to identify new similarities among genes. This review discusses and compares these methods, highlights their mathematical principles, and provides insight into the applications to solve biological problems.

Rational Drug Design

For the past three decades rationale drug design (RDD) has been developing as an innovative, rapid and successful way to discover new drug candidates. Many strategies have been followed and several targets with diverse structures and different biological roles have been investigated. Despite the variety of computational tools available, one can broadly divide them into two major classes that can be adopted either separately or in combination. The first class involves structure-based drug design, when the target's 3-dimensional structure is available or it can be computationally generated using homology modeling. On the other hand, when only a set of active molecules is available, and the structure of the target is unknown, ligand-based drug design tools are usually used. This review describes some recent advances in rational drug design, summarizes a number of their practical applications, and discusses both the advantages and shortcomings of the various techniques used.

A Technique to Exploit Free-Form Notes to Predict Customer Churn

As healthcare costs rise, hospitals are seeking ways to improve operations. This paper examines the usefulness of free-form notes to solve a classification problem commonly associated with customer churn. The authors show that classifiers which incorporate free-form notes, using natural language processing techniques, are up to 9% more accurate than classifiers that are solely developed using structured data. The authors suggest that hospitals and chronic disease management clinics can use structured data and free-form notes from electronic health records to predict which patients are likely to cease receiving care from their facilities. Classification tools for predicting patient churn are of interest to hospital administrators; such information can aid in resource planning and facilitate smoother handoffs between care providers.

Algorithmic Analysis of Clinical and Neuropsychological Data in Localization-Related Epilepsy

The current study examines algorithmic approaches for analysis of nonimaging (i.e., clinical, electrographic and neuropsychological) attributes in localization-related epilepsy (LRE), specifically, their impact on the selection of patients for surgical consideration. Both invasive electrographic and imaging data are excluded here to concentrate upon the initial clinical presentation and the varied elements of the seizure history, ictal semiology, risk and seizure-precipitating factors and physical findings in addition to several features of the neuropsychological profile including various parameters of cognition and both speech and memory lateralization. The data was accrued in a database of temporal lobe epilepsy patients (HBIDS). Six algorithms comprising feature selection, clustering and classification approaches were used. The Correlation-Based Feature Selection (CFS) and the Classifier Subset Evaluator (CSE) with the Genetic Algorithm (GA) search tool and ReliefF Attribute Evaluation approaches provided for feature selection. The Expectation Maximization (EM) Class Clustering and Incremental Conceptual Clustering (COBWEB) provided data clustering and the Multilayer Perceptron (MLP) Classifier was the classification tool at all stages of the study. The Engel Classification was used as an output of classifier for surgical success. Attributes demonstrating the highest correlation with the outcome class and the least intercorrelation with each other, according to CFS, were selected. These were then ranked using ReliefF and the top rankings chosen. The best attribute combination for each cluster was found by MLP. COBWEB provided the best results showing an association of 56% with Engel class. In conclusion, an algorithmic approach to the study of LRE is feasible with current findings supporting the need for correlative electrographic and imaging data and a greater archival population.

A Mobile Agent-Based Technique for Medical Monitoring (Supports of Patients with Diabetes)

Telemedicine is a particularly useful means to optimize the quality of care by fast medical exchanges that benefit patients whose state of health requires an appropriate and fast response, regardless of their geographic location. In this paper, the authors propose a mobile agent based architecture (DiabMAS) for remote medical monitoring of diabetic patients on an outpatient basis using mobile devices (laptops, PDAs, etc ...) by exploring the new operating Mobile system, Android. DiabMAS is a multi-agent system having as main objective the improvement of the transmission of information between patients and their physicians, especially the management of specific and critical cases.

Visual Gnosis and Face Perception

There are two major parallel pathways in humans: the parvocellular (P) and magnocellular (M) pathways. The former has excellent spatial resolution with color selectivity, while the latter shows excellent temporal resolution with high contrast sensitivity. Visual stimuli should be tailored to answer specific clinical and/or research questions. This chapter examines the neural mechanisms of face perception using event-related potentials (ERPs). Face stimuli of different spatial frequencies were used to investigate how low-spatial-frequency (LSF) and high-spatial-frequency (HSF) components of the face contribute to the identification and recognition of the face and facial expressions. The P100 component in the occipital area (Oz), the N170 in the posterior temporal region (T5/T6) and late components peaking at 270-390 ms (T5/T6) were analyzed. LSF enhanced P100, while N170 was augmented by HSF irrespective of facial expressions. This suggested that LSF is important for global processing of facial expressions, whereas HSF handles featural processing. There were significant amplitude differences between positive and negative LSF facial expressions in the early time windows of 270-310 ms. Subsequently, the amplitudes among negative HSF facial expressions differed significantly in the later time windows of 330–390 ms. Discrimination between positive and negative facial expressions precedes discrimination among different negative expressions in a sequential manner based on parallel visual channels. Interestingly, patients with schizophrenia showed decreased spatial frequency sensitivities for face processing. Taken together, the spatially filtered face images are useful for exploring face perception and recognition.

A Multiscale Computational Model of Chemotactic Axon Guidance

In the development of the nervous system, the migration of neurons driven by chemotactic cues has been known since a long time to play a key role. In this mechanism, the axonal projections of neurons detect very small differences in extracellular ligand concentration across the tiny section of their distal part, the growth cone. The internal transduction of the signal performed by the growth cone leads to cytoskeleton rearrangement and biased cell motility. A mathematical model of neuron migration provides hints of the nature of this process, which is only partially known to biologists and is characterized by a complex coupling of microscopic and macroscopic phenomena. This chapter focuses on the tight connection between growth cone directional sensing as the result of the information collected by several transmembrane receptors, a microscopic phenomenon, and its motility, a macroscopic outcome. The biophysical hypothesis investigated is the role played by the biased re-localization of ligand-bound receptors on the membrane, actively convected by growing microtubules. The results of the numerical simulations quantify the positive feedback exerted by the receptor redistribution, assessing its importance in the neural guidance mechanism.

A New Mechanical Algorithm for Calculating the Amplitude Equation of the Reaction-Diffusion Systems

Amplitude equation may be used to study pattern formatio. In this article, the authors establish a new mechanical algorithm AE_Hopf for calculating the amplitude equation near Hopf bifurcation based on the method of normal form approach in Maple. The normal form approach needs a large number of variables and intricate calculations. As a result, deriving the amplitude equation from diffusion-reaction is a difficult task. Making use of our mechanical algorithm, we derived the amplitude equations from several biology and physics models. The results indicate that the algorithm is easy to apply and effective. This algorithm may be useful for learning the dynamics of pattern formation of reaction-diffusion systems in future studies.