Medical Expert Systems Research Articles

Web-Based Knowledge Acquisition Approach for Building Diseases Symptoms Ontology

Today medical ontologies have an important role in medicine field to represent medical knowledge. They are much stronger than biomedical vocabularies. In diseases diagnosis process, each disease has number of symptoms associated with it. We can employ ontology in helping to diagnose diseases by building Diseases-Symptoms ontology, which relate diseases and symptoms. Such ontology would be very useful for medical expert systems to assist physicians in diagnosis diseases or as a training tool for medical students. In this paper, we propose a method that automatically extract medical knowledge from Web resources and build Diseases-Symptoms ontology. We use the linguistic pattern and statistical analysis techniques based on Bing search engine. We evaluated the proposed method for two diseases Hyperthyroidism and Eczema by two consultant physicians.

Legal Issues in Medical Computer Software and Expert Systems in United States: Legislation and Practice

This paper is the result of a research project started in 2005 at the Massachusetts Institute of Technology (Computer Science and Artificial Intelligence Laboratory) under the direction of Prof. Peter Szolovits (Head of the Clinical Decision Making Group) and completed at the European University Institute with the help of Prof. Giovanni Sartor.

Development of a Fuzzy Decision Support System to Determine the Severity of Obstructive Pulmonary in Chemical Injured Victims.

Background:Chronic Obstructive Pulmonary Disease (COPD) is the most common known complication of exposure to mustard gas. Thus, all clinical guidelines have provided some recommendation for diagnosis, clinical management and treatment of this disease. Decision support systems are used to increase the acceptance of clinical guidelines. The purpose of this research is to develop a CDSS to determine the severity of COPD in chemical injured victims.Objectives:Development of a decision support system to determine the severity of COPD.Patients and Methods:First, the variables influencing to determining the severity of the disease was classified through studying the clinical guidelines. Then, the fuzzy model was implemented. To testing the system, the data from 50 patients were used.Results:the overall accuracy in determining the severity of the injury is equal to 92%, these indicators reflect the proper functioning of the system to assist the physician regarding the diagnosis of chronic obstructive pulmonary disease and determining its severity.Conclusions:The CDSS has efficient results and satisfactory performance. Although, the medical expert systems cannot be expected to provide 100 percent correct responses, however, they can be useful in the areas of patient management, diagnosis and treatment planning.

Fuzzy rule-based medical expert system to identify the disorders of eyes, ENT and liver

Proposed system has been designed based on fuzzy logic to enhance the control strategies in the medical field with the development of medical expert system capable of detecting human diseases. It is capable to use in medical diagnosis which receives the information in the form of data entry of various relevant parameters from patient and displays the output in form of best possible disease name. It can be used to perform some statistical evaluations on patient's symptoms which we find in our daily life, to detect the particular disease. A decision support system is verified and proposed after taking various trials and tests using general symptoms of a disease from professional physicians and medical human experts. Proposed method creates weighted fuzzy set rule to deal with the medical diagnosing problems from the training data. This research is an attempt to encourage the computer technology in advance to develop software that will assist the doctors, physicians or a normal human being at primary level in making correct decisions before taking any consultation from the specialist of eyes, ear-nose-throat ENT and liver.

다각도 정보융합 방법을 이용한 지능형 에이전트 시스템

In this paper, we design an intelligent agent system with the data mining module and information fusion module as the core components of the system and investigate the possibility for the medical expert system. In the data mining module, fuzzy neural network, OFUN-NET analyzes multi-view data and produces fuzzy cluster knowledge base. In the information fusion module and application module, they serve the diagnosis result with possibility degree and useful information for diagnosis, such as uncertainty decision status or detection of asymmetry. We also present the experiment results on the BI-RADS-based feature data set selected form DDSM benchmark database. They show higher classification accuracy than conventional methods and the feasibility of the system as a computer aided diagnosis system. ∙제1저자 : 이현숙 ∙교신저자 : 이현숙 ∙투고일 : 2014. 11. 5, 심사일 : 2014. 11. 12, 게재확정일 : 2014. 11. 18. *동양미래대학교 소프트웨어정보과(Dept. of Software Engineering, Dongyang Mirae University) ※ 본 논문은 동양미래대학교 2014학년도 교내 학술연구과제 지원사업에 의하여 연구되었음. 12 Journal of The Korea Society of Computer and Information December 2014 ▸

Development of an Expert System for Diabetic Type-2 Diet

A successful intelligent control of patient food for treatment purpose must combines patient interesting food list and doctors efficient treatment food list. Actually, many rural communities in Sudan have extremely limited access to diabetic diet centers. People travel long distances to clinics or medical facilities, and there is a shortage of medical experts in most of these facilities. This results in slow service, and patients end up waiting long hours without receiving any attention. Hence diabetic diet expert systems can play a significant role in such cases where medical experts are not readily available. This paper presents the design and implementation of an intelligent medical expert system for diabetes diet that intended to be used in Sudan. The development of the proposed expert system went through a number of stages such problem and need identification, requirements analysis, knowledge acquisition, formalization, design and implementation. Visual prolog was used for designing the graphical user interface and the implementation of the system. The proposed expert system is a promising helpful tool that reduces the workload for physicians and provides diabetics with simple and valuable assistance.

A model for temporal reasoning in medical expert systems

While the results obtained with medical expert systems such as MYCIN, INTERNIST and CADIAG, have been very encouraging, they have some limitations which prevent them from fully simulating the behaviour of a medical expert. Some of these deficiencies are external (i.e. peculiar to the domain of application) while others are internal (i.e. caused by the technology used). This paper explains these limitations of medical expert systems and addresses the issue of temporal reasoning in medical expert systems. Temporal reasoning is specially important for medical expert systems as the final diagnosis is often strongly affected by the sequence in which the symptoms develop. Current research on time structures in computer science is reviewed and a temporal model based on fuzzy set theory is developed. The proposed model allows a simple and natural representation of symptoms and provides for efficient computation of temporal relationships between symptoms. The applicability of the proposed temporal model to reasoning in expert systems is demonstrated.

The Application of Case-Based Reasoning (CBR) in the Medical Management of Community Health

Currently, the technology of Case-Based Reasoning is used in the medical field,Such as the medical expert system that is developed for some large and medium-sized hospital, The patient 's medical programs are given by patient illness, The mainly property is descripted with non-numeric attributes about illness symptoms. It is the obstacles that the system can not become popularized for adaptive capacity.However,its adaptive capacity is not strong and non-intelligent , At current,the research about statistics of community health and disease prevention information is still limited ,An adaptive system model of CBR-based health management is presented , Case retrieval is important very mach . The system of community health management is developed to validate the model, The health examination data of five community are inputed in experimental platform in the past three years, The result indicating that the application of case-based reasoning makes the overall health of community residents to get continuous improvement .

Medical Expert Systems for Diagnosis of Various Diseases

Diseases should be treated well and on time. If they are not treated on time, they can lead to many health problems and these problems may become the cause of death. These problems are becoming worse due to the scarcity of specialists, practitioners and health facilities. In an effort to address such problems, studies made attempts to design and develop expert systems which can provide advice for physicians and patients to facilitate the diagnosis and recommend treatment of patients. This review paper presents a comprehensive study of medical expert systems for diagnosis of various diseases. It provides a brief overview of medical diagnostic expert systems and presents an analysis of already existing studies. General Terms Artificial intelligence, Expert system, Medical knowledge.

Coronary artery disease detection using a fuzzy-boosting PSO approach.

In the past decades, medical data mining has become a popular data mining subject. Researchers have proposed several tools and various methodologies for developing effective medical expert systems. Diagnosing heart diseases is one of the important topics and many researchers have tried to develop intelligent medical expert systems to help the physicians. In this paper, we propose the use of PSO algorithm with a boosting approach to extract rules for recognizing the presence or absence of coronary artery disease in a patient. The weight of training examples that are classified properly by the new rules is reduced by a boosting mechanism. Therefore, in the next rule generation cycle, the focus is on those fuzzy rules that account for the currently misclassified or uncovered instances. We have used coronary artery disease data sets taken from University of California Irvine, (UCI), to evaluate our new classification approach. Results show that the proposed method can detect the coronary artery disease with an acceptable accuracy. Also, the discovered rules have significant interpretability as well.

Design of an efficient mobile health system for achieving HIPAA privacy-security regulations

A mobile phone based solution to fulfil the Health Insurance Portability and Accountability Act HIPAA regulations is proposed in this paper for providing health services to patients especially residing in remote areas. Initially, patients and doctors register to a Medical Centre Server MCS, which stores all doctors' information and patients' Protected Health Information PHI. During treatment, a patient consults a medical expert system and its output is uploaded to MCS for specialised doctor's information and accordingly the patient contacts the doctors for treatment. The doctor retrieves the diagnostic results from MCS, gives advices to patient and finally generates the patient's PHI and uploads it to MCS. A copy of PHI is then sent to the patient by MCS. The proposed scheme, which also includes the treatments in emergency situation and foreign countries, is implementable using existing infrastructure and supports treatment in shortest possible time without patient's trip to doctors.

Expert Doctor Verdis: Integrated medical expert system

Innovative medical technologies are developing day by day, as there is an important need for integrated medical expert systems (ESs) that will help to effectively manage and control diagnosis and treatment processes. These systems, with new approaches, have improved the experiences and capabilities of physicians to make the diagnosis of diseases. In this work, an integrated medical ES called Expert Doctor Verdis (Ex-Dr Verdis) is developed, which combines an advanced medical information system containing various medical services supported by information technologies, with ES capabilities in a single system. This system is also one kind of decision support system. Implementation of this system is applied for vertebral column diseases. Ex-Dr Verdis is a strong decision support tool with 94% sensitivity, 71% specificity, 87% positive, and 86% negative predictive values for the diagnosis of vertebral diseases. In addition to its facilities of medical information, Ex-Dr Verdis, with a sharing platform, provides physicians with the opportunity to share and discuss their own patients, cases, experiences, and expert knowledge with other colleagues. This integrated medical ES can be used in all hospital services, such as hematology, neurology, or cardiology, by adding new expert modules for other diseases.

Diagnosis of Some Diseases in Medicine via computerized Experts System

Nowadays medical application especially diagnosis of some heart diseases has been rapidly increased because its importance and effectiveness to detect diseases and classify patients. In this research, we present the design of an expert system that aims to provide the patient with background for suitable diagnosis and treatment (Especially Angina Pectoris and Myocardial infarction). The proposed methodology is composed of four stages. The first stage is receiving the symptoms from the patient. The second stage is requesting from the patient to make some analysis and investigation to help the system to make a correct decision in the diagnosis. The third stage is doing diagnosis of patient according to information from patient (symptoms, analysis and investigation). The four stage is determining the name of appropriate medication or what should be done until the patient recovers (step therapy), so this system is able to give appropriate diagnosis and treatment for two heart diseases namely; angina pectoris and infarction. There are several programs used for diagnosis and system analysis, such as CLIPS and PROLOG. A medical expert system in this search made by Visual Prolog 7.3 is proposed.

The Diagnosis of Some Lung Diseases in a Prolog Expert System

This medical expert system is used to diagnose the main lung diseases among the patients. The diagnosis is made taking into account the symptoms that can be seen or felt. This medical expert system helps the doctor or expert in making the appropriate diagnosis of the patient. The lung diseases have many common symptoms and some of them are very much alike. This creates many difficulties for the lungs doctor to reach at a right decision or diagnosis. This expert system can remove these difficulties and it is having knowledge of thirtytwo lung diseases. This expert system is implemented in SWIProlog. General Terms Computer Science, Artificial Intelligence, Expert System

Using reduced rule base with Expert System for the diagnosis of disease in hypertension

Hypertension, also called the "Silent Killer", is a dangerous and widespread disease that seriously threatens the health of individuals and communities worldwide, often leading to fatal outcomes such as heart attack, stroke, and renal failure. It affects approximately one billion people worldwide with increasing incidence. In Turkey, over 15 million people have hypertension. In this study, a new Medical Expert System (MES) procedure with reduced rule base was developed to determine hypertension. The aim was to determine the disease by taking all symptoms of hypertension into account in the Medical Expert System (7 symptoms, 2(7)=128 different conditions). In this new MES procedure, instead of checking all the symptoms, the reduced rule bases were used. In order to get the reduced rule bases, the method of two-level simplification of Boolean functions was used. Through the use of this method, instead of assessing 2(7)=128 individual conditions by taking 7 symptoms of hypertension into account, reduced cases were evaluated. The average rate of success was 97.6% with the new MES procedure.

Recent development and perspectives of virtual slides (VS) and telepathology in Europe

Present stage Telepathology started in Europe in the 1980s. It was implemented in closed communication systems and focussed on frozen section service [1,2]. Open systems embedded in open source software replaced the early communication about 10 years ago. They became to age, and nearly all of them closed [3]. A new era started with the medical electronic communication expert system (MECES, http://www.diagnomx.eu) and the Virtual International Pathology Institute (VIPI,http://www.diagnomx. eu/vipi) that combine an internal communication network with external information nodes. It uses acoustic and visual information transfer as well as information sources at different levels such as access to libraries, image content information analysis, and diagnosis assistants [3]. It also incorporates VS, which are available from different companies. Although delivered in noncongruent formats certain medical platforms (MECES) and open access scientific journals (journal of diagnostic pathology (http://www.diagnosticpathology.org)) can handle VS via their specific viewers. VS implementation in routine tissue – based diagnosis is on its way. Most companies try to specifically connect their VS scanners to laboratory information systems (LIS) and / or to digital radiology imaging systems (picture archiving and communication system (PACS)) [4,5]. VS are provided with their own specific image analyzing system that focuses on evaluation of suitable immunohistochemistry scores such as Her2_neu or hormone receptors in breast cancer. Obligatory VS standards are still missing [6,7].

ST Algorithm for Medical Diagnostic Reasoning

The authors have previously described an approach for medical diagnostic reasoning based on the ST (Select and Test) model introduced by Ramoni and Stefanelli et al. This paper extends the previous approach by introducing the required algorithm for medical expert system development. The algorithm involves a bottom-up and recursive process using logical inferences, abduction, deduction, and induction. Pseudocode for the algorithm, and the data structures involved, are described, and the algorithm's implementation using a small sample knowledgebase and programmed in Java is included in appendixes. Implementation of a successful expert system is a challenging process; development of the necessary algorithm for its inference engine, and definition of a knowledgebase structure that models expert diagnostic reasoning and knowledge, only fulfils the initial step. Challenges associated with the remaining steps of the development process can be identified and dealt with using the CLAP software process model.

Implementation of XpertMalTyph: An Expert System for Medical Diagnosis of the Complications of Malaria and Typhoid

The dearth of medical experts in the developing world has subjected a large percentage of its populace to preventable ailments and deaths. Also, because of the predominant rural communities, the few medical experts that are available always opt for practice in the few urban cities. This consequently puts the rural communities at a disadvantage with respect to access to quality health care services. In this work, we designed and implemented XpertMalTyph; a novel medical diagnostic expert system for the various kinds of malaria and typhoid complications. A medical diagnostic expert system uses computer(s) to simulate medical doctor skills in diagnosis of ailments and prescription of treatments, hence can be used to provide the same service in the absence of the experts. XpertMalTyph is based on JESS (Java Expert System Shell) programming because of its robust inference engine and rules for implementing expert systems.

Medical Diagnosis Expert System as Service in Cloud

Medical Diagnosis Expert System as Service in Cloud

A Computer Aided Diagnostic Tool for the Detection of Uterine Fibroids

The integration of information technology with biomedicine has provided viable diagnostic tools to the medical community. Such computer aided procedures fastens the clinical decision process without any hurdle. Among different medical imaging modalities, Ultrasonic Imaging plays a vital role in detecting gynecological pathologies. Of importance, Uterine fibroid detection requires significant attention where symptoms such as, infertility and miscarriage can be predicted. This paper suggests an automated computer aided diagnostic tool for the detection of uterine fibroid. Gabor wavelets are applied for texture segmentation and statistical features such as mean, variance, standard deviation, skewness, kurtosis, Eigen values, GLCM contrast and energy are extracted from the user defined region of interest (ROI). The qualitative procedure is examined using the morphological operations and gray level intensity variations. Two neural network models, multilayer perceptron neural network (MLP) and probabilistic neural network (PNN) are applied to classify the normal and fibroid uterus image. It is found from the experimental computer simulation, a classification accuracy of 97.25% is obtained using combinational statistical features, mean and standard deviation with PNN classifier. It can be concluded that the proposed tool can applied as an efficient Medical Expert System for diagnosing the Ultrasonic Uterus images.