Analyzing Interval Systems of Human T-Cell Lymphotropic Virus Type I Infection of CD4+ T-Cells

Human T-cell lymphotropic virus type I (HTLV-I) infects a type of white blood cell called a T lymphocyte. HTLV-I infection is seen in diverse region of the world such as the Caribbean Islands, southwestern Japan, southeastern United States, and Mashhad (Iran). This virus is the etiological agent of two main types of disease: HTLV-I-associated myelopathy/tropical spastic paraparesis and adult T cell leukemia. Also, the role of HTLV-I in the pathogenesis of autoimmune diseases such as HTLV-I associated arthropathy and systemic lupus erythematosus is under investigation. In this chapter, the author considers an ODE model of T-cell dynamics in HTLV-I infection which was proposed by Stilianakis and Seydel in 1999. Mathematical analysis of the model with fixed parameters has been done by many researchers. The author studies dynamical behavior (local stability) of this model with interval uncertainties, called interval system. Also, effective parameters in the local dynamics of model are found. For this study, interval analysis and particularly of Kharitonov's stability theorem are used.

Applications of Operations Research in Production and Distribution Management of Pharmaceutical Products

Pharmaceutical industry is considered as a global industry because of its effects on the human life. Many researchers used optimization tools to manage the pharmaceutical supply chain (PSC) efficiently. A supply chain may be defined as an integrated process where several business entities work together to produce goods and/or services and deliver them to the end customer. The issue of PSC which includes strategic, tactical and operational decisions, is still a quite hot issue. The intended mission of this chapter is to introduce and discuss the recent developments of procurement, production and distribution management of pharmaceutical products in order to pave the way for the readers who are interested in this area of research. Notably, the focus of the chapter is on quantitative OR-based models which enable the decision makers to appropriately coordinate and manage the whole pharmaceutical industry.

A Case-Based-Reasoning System for Feature Selection and Diagnosing Asthma

Asthma is a chronic disease of the airways in the lungs. The differentiation between asthma, COPD and bronchiectasis in the early stage of disease is very important for the adoption of appropriate therapeutic measures. In this research, a case-based-reasoning (CBR) model is proposed to assist a physician to therapy. First of all, features and symptoms are determined and patients' data is gathered with a questionnaire, then CBR algorithm is run on the data which leads to the asthma diagnosis. The system was tested on 325 asthmatic and non-asthmatic adult cases and the accuracy was eighty percent. The consequences were promising. This study was performed in order to determine risk factors for asthma in a specific society and the results of research showed that the most important variables of asthma disease are symptoms hyper-responsive, frequency of cough and cough.

Notifiable Disease Databases for Client Management and Surveillance

The spread of certain infectious diseases, many of which are preventable, is widely acknowledged to have a detrimental effect on society. Reporting cases of these infections has been embodied in public health laws since the 1800s. Documenting client management and monitoring numbers of cases are the primary goals in collecting these data. A sample notifiable disease database is presented, including database structure, elements and rationales for collection, sources of data, and tabulated output. This chapter is a comprehensive guide to public health professionals on the content, structure, and processing of notifiable disease data for regional, provincial, and federal use.

Leveraging Applications of Data Mining in Healthcare Using Big Data Analytics

Big data analytics has been introduced as a set of scalable, distributed algorithms optimized for analysis of massive data in parallel. There are many prospective applications of data mining in healthcare. In this chapter, the authors investigate whether health data exhibits characteristics of big data, and accordingly, whether big data analytics can leverage the data mining applications in healthcare. To answer this interesting question, potential applications are divided into four categories, and each category into sub-categories in a tree structure. The available types of health data are specified, with a discussion of the applicable dimensions of big data for each sub-category. The authors conclude that big data analytics can provide more advantages for the quality of analysis in particular categories of applications of data mining in healthcare, while having less efficacy for other categories.

Applications of Image Processing in Laparoscopic Surgeries

Laparoscopy is a minimally-invasive surgery using a few small incisions on the patient's body to insert the tools and telescope and conduct the surgical operation. Laparoscopic video processing can be used to extract valuable knowledge and help the surgeons. We discuss the present and possible future role of processing laparoscopic videos. The various applications are categorized for image processing algorithms in laparoscopic surgeries including preprocessing video frames by laparoscopic image enhancement, telescope related applications (telescope position estimation, telescope motion estimation and compensation), surgical instrument related applications (surgical instrument detection and tracking), soft tissue related applications (soft tissue segmentation and deformation tracking) and high level applications such as safe actions in laparoscopic videos, summarization of laparoscopic videos, surgical task recognition and extracting knowledge using fusion techniques. Some different methods have been proposed previously for each of the mentioned applications using image processing.

A Simulation Model for Resource Balancing in Healthcare Systems

This study aims to analyze resource effectiveness through developed model. Changing different number of resources and testing their response, appropriate number of resources can be identified as a basis of resource balancing through what-if analysis. The simulation model for emergency department is developed by Arena package program. The patient waiting times are reduced by the tested scenarios. Health care system is very expensive sector and related costs are very high. To raise service quality, number of doctor and nurse are increased but system target is provided by increased number of register clerk. Testing different scenarios, effective policy can be designed using developed simulation model. This chapter provides the readers to evaluate healthcare system using discrete event simulation. The developed model could be evaluated as a base for new implementations in other hospitals and clinics.

Optimization of Maintenance in Critical Equipment in Neonatology

Maintenance decisions by medical staff play an essential role in achieving availability, quality and safety in care services provided. This has, in turn, an effect on the quality of care perceived by patients. Nonetheless, despite its importance, there is a serious deficiency in models facilitating optimization of maintenance decisions in critical care equipment. This chapter shows a decision support system (DSS) for choosing the best combination of maintenance policies, together with other actions for improvement, such as the increase in the number of back-up devices used in the assisted breathing unit in the Neonatology Service of a hospital. This DSS is combined with an innovative form of continuous time Markov chains, and the multicriteria Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH). The result is a ranking of the various maintenance alternatives to be applied. Finally, the real implications for availability and quality of care of applying the best solution are described.

Overview of Big Data in Healthcare

Big data is a new ubiquitous term for massive data sets having large, more varied and complex structure with the complexities and difficulties of storing, analyzing and visualizing for further processes or results. The use of Big Data in health is a new and exciting field. A wide range of use cases for Big Data and analytics in healthcare will benefit best practice development, outcomes analysis, prediction, and surveillance. Consequently, the aim of this chapter is to provide an overview of Big Data in Healthcare systems including two applications of Big Data analysis in healthcare. The first one is understanding disease outcomes through analyzing Big Data, and the second one is the application of Big Data in genetics, biological, and molecular fields. Moreover, characteristics and challenges of healthcare Big Data analysis as well as technologies and software used for Big Data analysis are reviewed.

Machine Learning Applications in Cancer Therapy Assessment and Implications on Clinical Practice

Precision medicine is an emerging medical model based on the customization of medical decisions and treatments to individuals. In personalized cancer therapy, tailored optimal therapies are selected depending on patient response to treatment rather than just using a one-size-fits-all approach. To this end, the field has witnessed significant advances in cancer response monitoring early after the start of therapy administration by using functional medical imaging modalities, particularly quantitative ultrasound (QUS) methods to monitor cell death at microscopic levels. This motivates the design of computer-assisted technologies for cancer therapy assessment, or computer-aided-theragnosis (CAT) systems. This chapter elaborates recent advances in the design and development of CAT systems based on QUS technologies in conjunction with advanced texture analysis and machine learning techniques with the aim of providing a framework for the early assessment of cancer responses that can potentially facilitate switching to more efficacious treatments in refractory patients.