149 research outputs found
Sort by
Diagnosis of diseases using data mining
Get PDFIntroduction: In the information age, data are the most important asset for health organizations. In the case of using data in useful and optimal manner, they can become financial resources for organization. Data mining is an appropriate method to transform this potential value into strategic information. Data mining means extraction of hidden information, recognition of hidden relationships and patterns, and in general, discovery of useful knowledge at high volume. The objective of this review paper was to evaluate using data mining in diagnoses of diseases.
Methods: This research is a review paper conducted based on a structured review of the papers published in Science Direct, Pubmed, Google Scholar, SID, Magiran (between years 2005 and 2015) and books related to using data mining in medical science and using it in diagnose of diseases with related keywords.
Results: Nowadays, data mining is used in many medical science studies, including diagnosis of diseases, discovering the hidden patterns in data, and so on. New ideas such as discovery of Knowledge from Discovery and Data Mining Database, which includes data mining techniques, have found more popularity and they has becomedesired research tool for researchers. Researchers can use them to identify patterns and relationshipsamong great number of variables. Using them, researchers have been able to predict theresults obtained from one disease by using information stores available in databases.
Several studies have indicated that data mining is used widely in diagnosis of diseases based on types of information (medical images, characteristics of patients, and so on), such as tuberculosis, types of cancers, infectious diseases, and diagnosis of anomalies rarely diagnosed by human (spots and particular points within aye, which is the symptom of onset of blindness resulting from diabetes), determining type of behavior with patients, and predicting the success rate of surgical surgeries, determining the success rate of therapeutic methods in coping with incurable diseases, and so on.
Conclusion: One of the most important challenging topics in healthcare is transformation of raw clinical data into meaningful information following continuous generation of great number of data. In current competitive environment, health organizations using technologies such as data mining to improve healthcare quality will achieve success faster. Many of research centers in Iran are faced with large volume of information, which is not analyzed at all or will be time-consuming due to using traditional methods, even in the case of using analysis and converting them to knowledge. In light of using data mining and its implementation, health organizations can transform the data into a powerful and competitive tool and take new steps in preventing, diagnosing, treating, and providing high-quality services for clients. 
Health e-learning using virtual-reality technology in Algerian universities
Get PDFBackground: The virtual reality environments around the world are increasingly used in many areas, including education, where they offer new learning opportunities. This technology (virtual reality) is used as an information resource or as an educational tool where the student takes an active part in learning by interacting with the device. For the past ten years or so, virtual environments have been used in teaching, especially in history and science.
Methods: Our work focuses on the educational potential offered by this technology at the Algerian University, and for this we have experimented virtual reality applications intended for the education of medicine using a virtual reality helmet “VR BOXâ€, which is a viewer hosting a smartphone. The students and teachers were looking through the VR BOX at a virtual human body and exploring the different body organs in 3D.
Results: This simulation, not otherwise possible in class with the classical teaching tools, offered students and teachers the opportunity to immerse themselves in an extremely realistic environment and allowed them to have a fun, memorable and fascinating experience. According to them, the use of this technology could intervene, in addition to the course, to facilitate the understanding of concepts difficult to explain. One of the main advantages of using this technology, say some teachers, is above all the interest and the motivation it arouses among the students.
Conclusion: The study presented in this article demonstrates the results of using virtual-reality environments for e-learning in the Algerian University in general, and for Health e-learning in particular. Furthermore, this experience and in view of the availability of virtual reality tools in Algeria at a very reasonable price, shows that virtual reality is very promising for the class of tomorrow and seems reinvigorating the teaching in Algeria as well as elsewhere
Towards developing an integrated index of access to dialysis facilities: A systematic review
Get PDFBackground: The equitable Access to Healthcare Services (AHS) constitutes one of the main priorities of the healthcare providers. Access to Dialysis Facilities (ADF) has an important impact on the renal dialysis patients. There are many spatial and non-spatial factors that potentially can affect ADF.
Objectives: We aimed to review available literature on factors affecting ADF. We have also tried to identify knowledge gaps in current studies in order to use those elicited factors to cover these gaps in developing an integrated index of ADF.
Methods: In May 2016, the literature was systematically searched using the following electronic databases: PubMed, Embase, Web of science, Scopus, Science Direct, and IEEE Xplore. A 3-step method to identify studies related to ADF was used. Study selection processes were performed by two independent reviewers. The quality of studies was assessed using a mixed approach scoring system.
Results: Initially, 975 literature were identified searching the selected databases. After removing duplicates, study screening, and applying inclusion/exclusion criteria, 34 studies were identified for final review. Given the content of selected studies, three groups of studies were identified and 42 factors with the potential effect on ADF were determined.
Conclusion: Our systematic research revealed that most of the factors with the potential effect on ADF are non-spatial. Such factors were underseen in many studies focusing mainly on the spatial dimensions of ADF. We recommended that all possible spatial and non-spatial factors together should be taken into account as part of an integrated index of ADF
Detection of Lung Cancer using Multilayer Perceptron Neural Network
Get PDFIntroduction: Lung cancer is the most common cancer in terms of prevalence and mortality. The cancer can be detected once it is reached to a stage that is visible in the CT imaging. Eighty six percent of the patients with lung cancer because they are late understand their disease, surgery has little effect on their improvement. Therefore, the existence of an intelligent system that can detect lung cancer in the early stages is necessary.
Methods: In this study, a lung cancer dataset of UCI database was used. This dataset consists of 32 samples, 57 variables and 3 classes (each class including 10, 9 and 13 samples). The data were normalized within the range 0 to 1. Then, to increase the detection speed, the dimensions of the data were reduced by using the Principal Components Analysis (PCA). Then, using a multilayer perceptron neural network, a model for classification and prediction of lung cancer was developed. Finally, the performance of the model was measured using sensitivity, specificity, positive predictive value and negative predictive value. It should be noted that all analyzes were done using Weka software.
Results: After developing and evaluating an artificial neural network model, the developed model had a sensitivity of 66.7%, a 98.5% specificity, a positive predictive value of 75%, and a negative predictive value of 97.7%.
Conclusion: In intelligent diagnostic systems, in addition to high accuracy of diagnosis, the speed of diagnosis and decision making is also important. Therefore, researchers increased the speed of the prediction model by reducing 57 variables to 8 variables using PCA. Also, the high sensitivity and high specificity of developed model demonstrates high power of artificial neural network model in detecting lung cancer
Applying decision tree for detection of a low risk population for type 2 diabetes: A population based study
Get PDFIntroduction: The aim of current study was to create a prediction model using data mining approach, decision tree technique, to identify low risk individuals for incidence of Type 2 diabetes (T2DM), using the Mashhad Stroke and Heart Atherosclerotic Disorders (MASHAD) Study program.
Methods: a prediction model was developed using classification by the decision tree method on 9528 subjects recruited from MASHAD database. Moreover, the receiver operating characteristic (ROC) curve was applied.
Results: The prevalence rate of T2DM was ~14% in our population. For decision tree model, the accuracy, sensitivity, and specificity value for identifying the related factors with T2DM were 78.7%, 47.8% and 83%, respectively. In addition, the area under the ROC curve (AUC) value for recognizing the risk factors associated with T2DM was 0.64. Moreover, we found that subjects with family history of T2DM, age>=48, SBP>=130, DBP>=81, HDL>=29, LDL>=148 and occupation=other have more than 59% chance of this disorder, while the chance of T2DM in subjects without history with TG>=184, age>=48 and hs-CRP>=2.2, have approximately 51% chance.
Conclusion: Our findings demonstrated that decision tree analysis, using routine demographic, clinical, anthropometric and biochemical measurements, which combined with other risk score models, could create a simple strategy to predict individuals at low risk for type 2 diabetes in order to decrease substantially the number of subjects needing for screening and recognition of subject at high risk
DETERMINING THE MINIMUM DATA SET FOR DIABETES REGISTRY
Get PDFIntroduction:
The number of people with diabetes\u27s increasing. More than 220 million people have diabetes, more than 70% of whom live in middle and lower-income countries. already exist many innovations around the world on improving the managed care of diabetes .diabetes registries are one of them. in Iran, development and evaluation of diabetes information systems is one of the most research priorities. since defining health regulations and evaluation of diabetes prevention programs depend on the powerful information system, but in Iran don\u27t exist complete information about incidence and prevalence of diabetes. determine standard data elements (Des) and design diabetes registry is one the most important country requirements. the main purpose of this study is investigating to this subject.
Methods:
This is a descriptive- analytic study. Resource related to diabetes DEs collected from selective minimum data sets. Then diabetes DEs set derived from selective minimum data sets were investigated in focus group sessions with endocrine specialists, health informatics, and health information management. Duplicate DEs were removed and similar DEs were combined. Then seven endocrine specialists evaluated diabetes DEs set. They determine the value of each DEs using the Delphi technique (scores range from 0 to 5). The DEs that received more than 75% of grade 4 and 5 remained in the study. Following the expert opinion, the final version of the diabetes DEs set was designed.
Results:
According to literature review 455 DEs included studying, after Delphi sessions, 293 data element remained to study. Main categories of DEs are:1-patient demographic characterizes (12 DEs), 2-patient referral (5 DEs), 3-diabetes care follow up (15 DEs), 4-physical exam, chief complaint and assessment (40 DEs), 5-history (such as: individual, grow up, family, drug abuse) (10 DEs), 6-pregnancy management (13 DEs), 7-screening (10 DEs), 8-specialty evolutions ( such as: cardiovascular (18 DEs), neuropathy (16 DEs), nephropathy (7 DEs), teeth and mouse (3 DEs), eyes (14 DEs), psychology situation (2 DEs), sexual ability (1 DEs)), 9-laboratory exams (33 DEs), 10-drugs (oral antidiabetics drugs (14 DEs), injectable antidiabetics (7 DEs), lipid (11 DEs), hypertension (20 DEs), anti placates (2 DEs)), cardiac (3 DEs), preparing insulin method (5 DEs)), 11-physical activity (4 DEs),12- diet (12 DEs), 13-education and self care (13 DEs).
Conclusion:
In the study diabetes, DEs set were determined that provide appropriate yield for data gathering and record all required information for diabetes care. Hence diabetes is a chronic disease and Patients suffer from it for years, implementation diabetes DEs can improve documentation and improve diabetes care. 
The new design of a remote real time embedded medical platform
Get PDFBackground: The aim of this work is to develop an electronic medical platform that enables us to monitor the physiological data of a patient and allows in cases of urgent problems to trigger an alarm remotely controlled by an expert to intervene quickly in case of emergency.
Methods: In this paper, we present the design of a new medical platform based on Arduino and its shields. This platform is made of two embedded electronic circuits. The first one that may be carried by the patient is implemented using the Arduino Uno and two expanded cards, namely the E-health and the XBee shields. This circuit reads periodically physiological data (ECG, temperature, spo2 etc.) and wirelessly transmits them to the second circuit which is connected to the internet, based on the Arduino Ethernet electronic module and containing the XBee shield and an RFID module, as well as additional electronic circuits to report the critical situations, such as an LCD display and a buzzer to indicate an abnormal situation requiring local supervision.The patient is identified automatically by an RFID tag.
Results:This platform was practically implemented as a final electronic product and tested. The results were very satisfactory as the embedded circuit was functioning correctly and we could upload and archive the collected data in real time in a specific database. A web server was also developed which gave remote access to the medical data of the patient making the control by a doctor possible for remote fast treatment.
Conclusion: The practical realization of our medical platform allowed us to test in real time remote monitoring of a patient\u27s physiological data while identifying the patient with an RFID tag.
 
Research Excellence: The Imperative Primers
Get PDFResearch excellence characteristics are comprehensively delineated, encompassing creativity through transnationality and translation-capability. The research iterative primers of “Re†and “search†as the underlying push-engine, is briefly illustrated. Subsequently, a visual model depicting three major modules of the entire research tasking-process illustrates the need to balance the preparation and the rendition of data and interpretation components, through the mediating role of data capture. Traits of the primers characterising research excellence are consequently deliberated and discussed thoroughly using the acronym R-E-S-E-A-R-C-H. The initial three entities, RES, fittingly portray the literature review matrix; with reviewing of references through examining “slacks†or gaps in available literature. The E is then envisaged as representing exclusivity through ethical considerations; A as audience through imbibition of overt and covert objectives, to audit trail analysis; R as rationale through research frame (interpretive, analytical, simulative); C as encompassing creativity through coherence (tone, voice, style); and, H as the human factors- trailing from biases through needs/wants to influences. Optimising values of these primers are deemed imperative to achieve excellence in a research project process and outcome
Impact of information technology in increasing quality of data in disease registries
Get PDFIntroduction: The Disease Registry records and maintains the necessary data for each individual patient according to the purpose for which it was designed. These data are widely used to calculate statistical indicators, resource management, resource allocation, and clinical research. One of the main challenges in using disease registries is their low quality data. Incompleteness, inaccuracy and untimeliness are some of the problems with the quality of the data in the disease registries. In this study, the researchers reviewed the views of some medical specialists on ways to improve the quality of data in the diseases registries. In this study, researchers looked at the views of some medical specialist who were in charge of maintaining a disease registry on ways to improve the quality of data in disease registries.
Methods: The qualitative method of in-depth interviews was used to explore the views of medical specialist. They were specialist doctors who were responsible for keeping a disease registry in their specialty. All interviews were conducted by one of the researchers. All interviews were conducted by one of the researchers and the interviews were digitally recorded during the interview. After the interview, the sounds recorded by the researcher were written in the form of text and the themes discussed in them were coded and analyzed. Eventually the coding was reviewed by another researcher, and the opposing opinions of the researchers were resolved through the discussion.
Results: The number of medical specialists interviewed was 6, which all of them was internist. The minimum and maximum interview time was 15 and 35 minutes respectively. The most important solutions to improve the quality of data in the diseases registries are: utilization of software systems (suggested by all doctors), utilization of coded data like icd-10 code (suggested by 5 doctors) and connecting disease registries to hospital information systems and other health information system (suggested by 3 doctors).
Conclusion: According to the views of the medical specialists who participated in present study, the use of IT-based methods as well as information management methods is the best way to improve the quality of data in the registry