Management and Analysis of Biological and Clinical Data: How Computer Science May Support Biomedical and Clinical Research

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on the use of networks in biomedicine

Abstract The concept of "neural network" emerges by electronic models inspired to the neural structure of human brain. Neural networks aim to solve problems currently out of computer's calculation capacity, trying to mimic the role of human brain. Recently, the number of biological based applications using neural networks is growing up. Biological networks represent correlations, extracted from sets of clinical data, diseases, mutations, and patients, and many other types of clinical or biological features. Biological networks are used to model both the state of a range of functionalities in a particular moment, and the space-time distribution of biological and clinical events. The study of biological networks, their analysis and modeling are important tasks in life sciences. Most biological networks are still far from being complete and they are often difficult to interpret due to the complexity of relationships and the peculiarities of the data. Starting from preliminary notions about neural networks, we focus on biological networks and discuss some well-known applications, like protein-protein interaction networks, gene regulatory networks (DNA-protein interaction networks), metabolic networks, signaling networks, neuronal network, phylogenetic trees and special networks. Finally, we consider the use of biological network inside a proposed model to map health related data

A novel Network Science Algorithm for Improving Triage of Patients

Patient triage plays a crucial role in healthcare, ensuring timely and appropriate care based on the urgency of patient conditions. Traditional triage methods heavily rely on human judgment, which can be subjective and prone to errors. Recently, a growing interest has been in leveraging artificial intelligence (AI) to develop algorithms for triaging patients. This paper presents the development of a novel algorithm for triaging patients. It is based on the analysis of patient data to produce decisions regarding their prioritization. The algorithm was trained on a comprehensive data set containing relevant patient information, such as vital signs, symptoms, and medical history. The algorithm was designed to accurately classify patients into triage categories through rigorous preprocessing and feature engineering. Experimental results demonstrate that our algorithm achieved high accuracy and performance, outperforming traditional triage methods. By incorporating computer science into the triage process, healthcare professionals can benefit from improved efficiency, accuracy, and consistency, prioritizing patients effectively and optimizing resource allocation. Although further research is needed to address challenges such as biases in training data and model interpretability, the development of AI-based algorithms for triaging patients shows great promise in enhancing healthcare delivery and patient outcomes

Extracting Dense and Connected Subgraphs in Dual Networks by Network Alignment

The use of network based approaches to model and analyse large datasets is currently a growing research field. For instance in biology and medicine, networks are used to model interactions among biological molecules as well as relations among patients. Similarly, data coming from social networks can be trivially modelled by using graphs. More recently, the use of dual networks gained the attention of researchers. A dual network model uses a pair of graphs to model a scenario in which one of the two graphs is usually unweighted (a network representing physical associations among nodes) while the other one is edge-weighted (a network representing conceptual associations among nodes). In this paper we focus on the problem of finding the Densest Connected sub-graph (DCS) having the largest density in the conceptual network which is also connected in the physical network. The problem is relevant but also computationally hard, therefore the need for introducing of novel algorithms arises. We formalise the problem and then we map DCS into a graph alignment problem. Then we propose a possible solution. A set of experiments is also presented to support our approach

vocal signal analysis in patients affected by multiple sclerosis

Abstract Multiple Sclerosis (MS) is one of the most common neurodegenerative disorder that presents specific manifestations among which the impaired speech (known also as dysarthria). The evaluation of the speech plays a crucial role in the diagnosis and follow-up since the identification of anomalous patterns in vocal signal may represent a valid support to physician in diagnosis and monitoring of these neurological diseases. In this contribution, we present a method to perform voice analysis of neurologically impaired patients affected by MS aiming to early detection, differential diagnosis, and monitoring of disease progression. This method integrates two well-known methodologies to support the health structure in MS diagnosis in clinical practice. Acoustic analysis and vowel metric methodologies have been considered to implement this procedure to better define the pathological voices compared to healthy voices. Specifically, the method acquires and analyzes vocal signals performing features extraction and identifying possible important patterns useful to associate impaired speech with this neurological disease. The contribution consists in furnishing to physician a guide method to support MS trend. As result, this method furnishes patterns that could be valid indicators for physician in monitoring of patients affected by MS. Moreover, the procedure is appropriate to be used in early diagnosis that is critical in order to improve the patient's quality of life

Applying Mining Techniques to Analyze Vestibular Data

AbstractThe vestibular apparatus allows to perform audiological and equilibrium human functions and to capture movements with respect to gravity. Damages to the vestibular system causes diseases that can be measured by using Vestibular Evoked Myogenic Potentials (VEMPs) test. The test produces a lot of data that has to be collected and analyzed to allow a disease study and classification. We propose a framework that includes algorithms able to perform pathology distribution and classification. It has been tested on electronic patient records loaded from the University Hospital database. The software allows to manage the structure and framework and a blind application of one of the available classification techniques shows a relation among gender and vestibular apparatus disease

A System for the Analysis of Snore Signals

AbstractSleep apnoea syndrome (SAS) is a disease consisting in the nocturnal cessation of oronasal airflow at least 10 seconds in duration. The standard method for SAS diagnosis is the polysomnographic exam (PSG). However it does not permit a mass screening because it has high cost and requires long term monitoring.This paper presents a preliminary software system prototype for snoring signal analysis, whose main goal is to support the doctor in SAS diagnosis and patient follow-up. The design of the system is modular to allow a future hardware implementation in a portable device for personal snore collection and monitoring

Bioinformatics Solutions for Image Data Processing

In recent years, the increasing use of medical devices has led to the generation of large amounts of data, including image data. Bioinformatics solutions provide an effective approach for image data processing in order to retrieve information of interest and to integrate several data sources for knowledge extraction; furthermore, images processing techniques support scientists and physicians in diagnosis and therapies. In addition, bioinformatics image analysis may be extended to support several scenarios, for instance, in cyber-security the biometric recognition systems are applied to unlock devices and restricted areas, as well as to access sensitive data. In medicine, computational platforms generate high amount of data from medical devices such as Computed Tomography (CT), and Magnetic Resonance Imaging (MRI); this chapter will survey on bioinformatics solutions and toolkits for medical imaging in order to suggest an overview of techniques and methods that can be applied for the imaging analysis in medicine