KNOWLEDGE BASED REASONING ON CLINICAL INFORMATION SYSTEMS (CIS): IN-FOCUS OF COMMON & RARE DISEASES SYMPTOM FINDINGS

Technology has put his hand into medical field back in the 70's. Computerized system is placed in every sector including medical. Hence, the need of expert system to assist the end users is practically relevant. This research will discuss about the expert system and the concept of artificial intelligence in medical field. Thus, explaining the methodology that goes behind developing the end product of this project. The expert system will become one platform for other medical system. The application is made available through wireless environment. Wireless infrastructure is shown with the usage of notebook (in case of this project). Medical information is gathered from books and experts before modeled out into the system. There are 3 main rare diseases that will be diagnosed from the common symptoms by showing the details and prescription. The system can later on be reproduced or enhanced with added disease information. Furthermore, the data can be updated with the features embedded into the system. The prototype is meant be one of the pioneers for another great invention in the future

Automatic Color Segmentation of Images with Application to Detection of Variegated Coloring in Skin Tumors

A description is given of a computer vision system, developed to serve as the front-end of a medical expert system, that automates visual feature identification for skin tumor evaluation. The general approach is to create different software modules that detect the presence or absence of critical features. Image analysis with artificial intelligence (AI) techniques, such as the use of heuristics incorporated into image processing algorithms, is the primary approach. On a broad scale, this research addressed the problem of segmentation of a digital image based on color information. The algorithm that was developed to segment the image strictly on the basis of color information was shown to be a useful aid in the identification of tumor border, ulcer, and other features of interest. As a specific application example, the method was applied to 200 digitized skin tumor images to identify the feature called variegated coloring. Extensive background information is provided, and the development of the algorithm is described

Interpretation of laboratory results through comprehensive automation of medical laboratory using OpenAI

In modern medicine, laboratory tests play an important role in the diagnosis, treatment and monitoring of patients. However, the volume and complexity of the data obtained can create challenges for interpreting the results. In this paper, we present a study on the application of integrated automation of a medical laboratory using OpenAI for a more accurate and effective interpretation of laboratory results. Interpreting laboratory results through integrated automation using artificial intelligence (AI) and other digital technologies automatically analyzes and interprets laboratory results. This approach aims to streamline the process of interpreting laboratory results and provide more accurate, consistent and timely results to healthcare providers. Comprehensive automation of the interpretation of laboratory results can improve the efficiency and accuracy of laboratory results, leading to improved patient outcomes and better clinical decision-making. However, it is essential to note that AI models are imperfect and can still make mistakes. Therefore, healthcare professionals should always review automated interpretation results before diagnosing or treating. The work presented results in applying OpenAI to interpret laboratory results in the laboratory information system smartLAB Kazakhstan, which provides a complete cycle of automation of all medical laboratory processes. In the course of the study, an automated information system of a medical research complex using artificial intelligence was developed and implemente

Visual cohort baby recording based on internet of things for maternal and child health service

Abstract. Maternal and Child Health Service is a health service that makes it easier for the public to monitor the growth and development of infants and toddlers. The people who get this health service are infants less than one year old and toddlers aged 1 to 5 years. Maternal and Child Health Service in the community, especially in rural areas, the medical equipment used is still inadequate, for example to weigh infants and toddlers, maternal and child health official use scales commonly used to weigh rice. Periodically babies and toddlers are weighed by medical personnel, and the results are then recorded in the maternal and child book. In some cases, the registration process is still not efficient, because the possibility of the book being lost. The aim of this research is to design and develop a Posyandu Information System Application as an early warning system for Maternal and Child Health. In this research, first coding from four sensors used are heart rate sensor, weight sensor, temperature sensor and ultrasonic sensor. In the microcontroller, the Artificial Neural Network artificial intelligence method is embedded to learn from inputs to classify decisions / action information from the sensor medical record results. Medical record data and learning outcomes from Artificial Neural Network (ANN) Algorithm will be sent using Internet of Things modules on the server so that they can be accessed by the application server both web and mobile. Visualization of medical record data and the results of the health conditions of infants and toddlers recapitulated on the cohort book periodically and form of cohort graphical report. With the early warning system in the form of a Posyandu Information System Application it is expected that pregnant women or toddlers who have health problems can immediately obtain information as early as possible

KNOWLEDGE BASED REASONING ON CLINICAL INFORMATION SYSTEMS (CIS): IN-FOCUS OF COMMON & RARE DISEASES SYMPTOM FINDINGS

Technology has put his hand into medical field back in the 70's. Computerized system is placed in every sector including medical. Hence, the need of expert system to assist the end users is practically relevant. This research will discuss about the expert system and the concept of artificial intelligence in medical field. Thus, explaining the methodology that goes behind developing the end product of this project. The expert system will become one platform for other medical system. The application is made available through wireless environment. Wireless infrastructure is shown with the usage of notebook (in case of this project). Medical information is gathered from books and experts before modeled out into the system. There are 3 main rare diseases that will be diagnosed from the common symptoms by showing the details and prescription. The system can later on be reproduced or enhanced with added disease information. Furthermore, the data can be updated with the features embedded into the system. The prototype is meant be one of the pioneers for another great invention in the future

Digital Health Care in Taiwan

This open access book introduces the National Health Insurance (NHI) system of Taiwan with a particular emphasis on its application of digital technology to improve healthcare access and quality. The authors explicate how Taiwan integrates its strong Information and Communications Technology (ICT) industry with 5G to construct an information system that facilitates medical information exchange, collects data for planning and research, refines medical claims review procedures and even assists in fighting COVID-19. Taiwan's NHI, launched in 1995, is a single-payer system funded primarily through payroll-based premiums. It covers all citizens and foreign residents with the same comprehensive benefits without the long waiting times seen in other single-payer systems. Though premium rate adjustment and various reforms were carried out in 2010, the NHI finds itself at a crossroads over its financial stability. With the advancement of technologies and an aging population, it faces challenges of expanding coverage to newly developed treatments and diagnosis methods and applying the latest innovations to deliver telemedicine and more patient-centered services. The NHI, like the national health systems of other countries, also needs to address the privacy concerns of the personal health data it collects and the issues regarding opening this data for research or commercial use. In this book, the 12 chapters cover the history, characteristics, current status, innovations and future reform plans of the NHI in the digital era. Topics explored include: Income Strategy Payment Structure Pursuing Health Equity Infrastructure of the Medical Information System Innovative Applications of the Medical Information Applications of Big Data and Artificial Intelligence Digital Health Care in Taiwan is essential reading for academic researchers and students in healthcare administration, health policy, health systems research, and health services delivery, as well as policymakers and public officials in relevant government departments. It also would appeal to academics, practitioners, and other professionals in public health, health sciences, social welfare, and health and biotechnology law

Digital Health Care in Taiwan

This open access book introduces the National Health Insurance (NHI) system of Taiwan with a particular emphasis on its application of digital technology to improve healthcare access and quality. The authors explicate how Taiwan integrates its strong Information and Communications Technology (ICT) industry with 5G to construct an information system that facilitates medical information exchange, collects data for planning and research, refines medical claims review procedures and even assists in fighting COVID-19. Taiwan's NHI, launched in 1995, is a single-payer system funded primarily through payroll-based premiums. It covers all citizens and foreign residents with the same comprehensive benefits without the long waiting times seen in other single-payer systems. Though premium rate adjustment and various reforms were carried out in 2010, the NHI finds itself at a crossroads over its financial stability. With the advancement of technologies and an aging population, it faces challenges of expanding coverage to newly developed treatments and diagnosis methods and applying the latest innovations to deliver telemedicine and more patient-centered services. The NHI, like the national health systems of other countries, also needs to address the privacy concerns of the personal health data it collects and the issues regarding opening this data for research or commercial use. In this book, the 12 chapters cover the history, characteristics, current status, innovations and future reform plans of the NHI in the digital era. Topics explored include: Income Strategy Payment Structure Pursuing Health Equity Infrastructure of the Medical Information System Innovative Applications of the Medical Information Applications of Big Data and Artificial Intelligence Digital Health Care in Taiwan is essential reading for academic researchers and students in healthcare administration, health policy, health systems research, and health services delivery, as well as policymakers and public officials in relevant government departments. It also would appeal to academics, practitioners, and other professionals in public health, health sciences, social welfare, and health and biotechnology law

Modelling medical diagnostic processes

The thesis investigates the development of medical reasoning processes and how student modelling of such processes can be achieved in intelligent tutoring systems. The domain of orthopaedics was chosen for the research. Literature has shown that medical reasoning has been modelled mainly from an expert point of view. The research problem addressed is to model explicitly various levels of medical expertise in terms of reasoning strategies. The thesis reports on a system, DEMEREST (DEvelopment of MEdical REasoning STrategies), a developmental user model component which describes successive stages of medical reasoning and which could ultimately be part of a medical tutor. The system diagnoses physicians' reasoning strategies, determines the level of expertise and produces a plan corresponding to the application of these strategies. As a basis of doing so, a set of seven reasoning strategies was identified in the medical problem solving literature. These strategies are based on generalisation, specialisation, confirmation, elimination, problem refinement, hypothesis generation and anatomy. An empirical study was carried out to examine the development of these strategies. Protocols of ten physicians at various levels of expertise were collected and analysed. A number of interactions of strategies at different levels of expertise was identified in half of these protocols and this information was used to construct a model of changes of strategies over time. Planning in· artificial intelligence was used as a means of decomposing medical problem solving into a set of goals; the goals being associated with the reasoning strategies. By taking this approach, medical reasoning is viewed as a planning process. The remaining protocols from the empirical study were used to evaluate DEMEREST. The system was tested for its ability to determine a level of expertise for each protocol, model the reasoning strategies applied and their interactions, and generate a plan for each protocol. The assessment of the overall performance of the system showed that it was successful. This assessment also helped to identify conceptual as well as implementation constraints of the prototype system. The main result of the research undertaken in this thesis is that the design of the system DEMEREST demonstrates the feasibility of modelling the development of medical reasoning strategies and its usefulness for student modelling

Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey

Cough acoustics contain multitudes of vital information about pathomorphological alterations in the respiratory system. Reliable and accurate detection of cough events by investigating the underlying cough latent features and disease diagnosis can play an indispensable role in revitalizing the healthcare practices. The recent application of Artificial Intelligence (AI) and advances of ubiquitous computing for respiratory disease prediction has created an auspicious trend and myriad of future possibilities in the medical domain. In particular, there is an expeditiously emerging trend of Machine learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting cough signatures. The enormous body of literature on cough-based AI algorithms demonstrate that these models can play a significant role for detecting the onset of a specific respiratory disease. However, it is pertinent to collect the information from all relevant studies in an exhaustive manner for the medical experts and AI scientists to analyze the decisive role of AI/ML. This survey offers a comprehensive overview of the cough data-driven ML/DL detection and preliminary diagnosis frameworks, along with a detailed list of significant features. We investigate the mechanism that causes cough and the latent cough features of the respiratory modalities. We also analyze the customized cough monitoring application, and their AI-powered recognition algorithms. Challenges and prospective future research directions to develop practical, robust, and ubiquitous solutions are also discussed in detail.Comment: 30 pages, 12 figures, 9 table

Towards the Use of Big Data in Healthcare: a literature review

The interest in new and more advanced technological solutions is paving the way for the diffusion of innovative and revolutionary applications in healthcare organizations. The application of an artificial intelligence system to medical research has the potential to move toward highly advanced e-Health. This analysis aims to explore the main areas of application of big data in healthcare, as well as the restructuring of the technological infrastructure and the integration of traditional data analytical tools and techniques with an elaborate computational technology that is able to enhance and extract useful information for decision-making. We conducted a literature review using the Scopus database over the period 2010-2020. The article selection process involved five steps: the planning and identification of studies, the evaluation of articles, the extraction of results, the summary, and the dissemination of the audit results. We included 93 documents. Our results suggest that effective and patient-centered care cannot disregard the acquisition, management, and analysis of a huge volume and variety of health data. In this way, an immediate and more effective diagnosis could be possible while maximizing healthcare resources. Deriving the benefits associated with digitization and technological innovation, however, requires the restructuring of traditional operational and strategic processes, and the acquisition of new skills