جستجوی مقالات مرتبط با کلیدواژه "تشخیص بیماری" در نشریات گروه "پزشکی"

Today, heart attack is one of the most common causes of death in adults all over the world. According to the announcement of the Ministry of Health, Treatment, and Medical Education, 11 to 15 percent of deaths in Iran are caused by heart attacks, and in the world, Iran has the highest number of deaths due to heart disease in 2022. It has been estimated that deaths from these diseases will increase to 20 million people. Therefore, predicting this disease is one of the most challenging topics in the medical field, and today most prediction systems are created using artificial intelligence and machine learning algorithms.

This applied and descriptive research was conducted in 2022 using the information of 600 people who referred to Peymaniyeh and Motahari hospitals in Jahrom city. In this research, data were collected based on the available sources from both hospitals. To implement and evaluate the results, MATLAB programming language was used.

The parameters used in this research are among the classification parameters, including classification rate, precision, recall, and F-measure, which were obtained as 90.7, 90.2, 91.5, and 90.8, respectively.

The results show that the proposed model will be able to predict the probability of stroke with a high percentage.

Thyroid diseases are common disorders worldwide. The timely diagnosis and control of this disease can prevent its progression and reduce associated complications. This study proposes a novel hybrid method by combining particle swarm optimization (PSO) algorithm and artificial neural network (ANN) for the timely detection of thyroid disorders.

This is an applied survey study, conducted in 2022. In this study, the target population consisted of the data of 400 patients referred to Imam Reza Hospital in Lar County, Iran from 2021 to 2022 which were collected by field study. Among them, 300 had thyroid disease and 100 were healthy. MATLAB software was used for implementing the proposed learning model and analyzing the results.

The regression coefficient of the proposed model in there modes of training, validation, and testing were 0.98, 0.97, and 0.95, respectively. The area under the ROC curve was 0.98, the error rate was 0.004, and the overall accuracy was 96%.

The proposed model can distinguish patients with thyroid disease from healthy individuals with acceptable accuracy and low errors. This model can be used as a useful tool in predicting thyroid diseases.

Pupil and iris disorders form an important category of eye diseases. Accurate segmentation of the pupil is the first and most important step in the automatic diagnosis of diseases related to the pupil and iris. Most of the existing methods do not have enough accuracy and are sensitive to the effects of noise and specular spot reflection. In addition, the images used in these methods usually have limitations, such as the viewing angle.

In the proposed algorithm, a stable method is offered to remove the effects of specular spot reflection in the pupil, and necessary preprocessing is done to detect the exact location of the pupil. An optimized color mapping algorithm is proposed and the mapping is calculated with the help of the LM algorithm to accurately determine the pupil boundary. This method does not impose any restrictions on the eye image and shape, and the angle of the pupil in the image can be in any shape and direction.

The proposed method does not assume any specific model as the final pupil boundary (circle or oval) and is robust to noise and specular reflection factors as well. This method has been able to accurately detect the pupil boundary with the accuracy of 98.8% using UBIRIS dataset and 98% using the collected data by authors.

The method presented in this paper can be used to increase the accuracy in determining the internal and external border of the iris to diagnose diseases related to the pupil and iris, as well as identity identification based on iris tissue.

In this article, proving fault in medical accidents, by emphasizing the expansion of the concept and scope of fault and providing solutions for the presumed fault of the physician, and explaining the types of fault in different stages of treatment, with the aim of using the experience of French legislation and jurisprudence our country has been examined.

The research method is descriptive-analytical and the data collection method is library, with emphasis on the interpretation of judgments and legal texts and the analysis of the views of lawyers.

In this research, the principles of fidelity, honesty, impartiality and originality of the research have been observed.

French jurisprudence has used various legal tools to develop the concept of medical fault and facilitate its proof; this should be taken into consideration by our courts as well.

French jurisprudence provides a broad concept and scope of medical malpractice and any fault on the part of a physician or health center, however simple and any misrepresentation of scientific information or medical conscience has been held liable and effective measures have been taken to facilitate proof. Also, the types and examples of faults in different stages of diagnosis, selection of treatment, medical practice and supervision, have been well separated and regulated by the doctrine and judicial procedure of this country and using personal responsibility and responsibility arising from other acts for the surgeon and Anesthesiologists and the admitting presumed faults and the like, have sought to facilitate the Compensation of victims of medical accidents; this is also suggested by the Iranian judicial procedure.Please cite this article as:Karimi A, Javaherkalam MH. Proof of Fault in Medical Accidents in French Law. Medical Law Journal. 2022; 16(57): e18.

Determining the rank of important parameters in ranking health care and providing health services to patients in medical centers.

The research method is descriptive-analytical and applied in terms of classification. The data is from the database of the Faculty of Health and Medicine of the University of Tehran, which was collected as an archive from April 1998 to May 1999. The statistical population were all experts, specialists and experts of the Faculty of Health 29 of whom completed the questionnaire. The weight of the criteria was evaluated using the AHP method and in the next stage, the medical centers were ranked using the DS-VIKOR method. The analysis method in this study consists of the following steps steps:• Implementing a simple VIKOR method.  Implementing the Dempster-Shafer and Vicker method.  Implementing the AHP

 Implementation of the Topsis method. Comparing the proposed methods to review and compare which ones work best.

The weight of medical centers was assessed by AHP method and then the combined centers were ranked by Dempster-Shafer and VIKOR combined methods  using the information of four medical centers, the DS-Vikor approach was implemented. The purpose of six criteria and three experts was used for evaluation. The results show that the effectiveness of care and treatment process is more important from the experts' point of view. Dempester-Schaefer and Vicor The medical centers in question are ranked. For validation, at the end, the medical centers were ranked by TOPSIS method.The integrated system includes various subsystems giving caring and providing health services to patients in medical centers that can be built and configured and are ranked.The model can investigate the effectiveness of giving caring and providing health services to patients in medical centers.

By combining the two methods of Dempster-Shafer and Vicker, the confidence in the whole uncertainty is improved and the results are more reliable. This approach can help reduce the uncertainty caused by people's cognition to increase the level of decision-making, allowing us to overcome the problem of choosing the right level of uncertainty and to deal with uncertainty in a practical and justified way.

Confirmed and suspected cases of new coronavirus 2019 have increased not only in China, but also in Iran and the world. The overwhelming demand for Covid-19 outbreaks has challenged both health care workers and the medical procurement system. Accordingly, this study was conducted to investigate the new coronavirus infection (2019-nCOV). Various articles on Covid-19 disease have been collected and translated. The results of this study include clinical explanation, diagnosis, causes and risk factors, diagnostic procedures, treatment, complication prediction, screening and prevention.

Clinical Decision Support Systems (CDSS) are designed in the form of computer programs that help medical professionals make decisions about disease diagnosis. The main aim of these systems is to assist physicians in diagnosing diseases, in other words, a physician can interact with the system and use them to analyze patient data, diagnose diseases, and other medical activities.
This is a descriptive-analytic study. The datasets include 768 records of diabetes with 8 features and 155 records of hepatitis with 19 features, which were provided by the Global Website of UCI. In this study, the Particle Swarm Optimization (PSO) algorithm was used for Feature Selection (FS) and the Firefly Algorithm (FA) was used to classify diabetes and hepatitis into two healthy and unhealthy classes. 80% of the data was used for training and the remaining (20%) was used for testing.
The experiments showed that the accuracy of the PSO and FA for the diabetes dataset was 84.41% and 82.08%, respectively. Also, the accuracy of the PSO and FA for the hepatitis dataset was 81.84% and 80.34%, respectively. The accuracy of the proposed model for the diabetes and hepatitis datasets was 95.38% and 94.09%, respectively.
According to the results, the proposed model had a lower error rate in diagnosis compared to the PSO and FA. The results of this study can help doctors in timely diagnosis of diabetes and hepatitis

The prevalence of Crimean-Congo fever, a common disease between human and animal, shows an increasing rate by coming summer season. Detection of this disease by the use of necessary tests, lasts at least about one week. There are several data mining and machine learning techniques to create predictive models for identifying at risk people. In this study, C4.5 decision tree method has been used due to its simplicity and efficiency.
In this applied descriptive study, data related to suspected cases of Crimean-Congo fever were used. These data have been collected from health centers of Iran in a four-year period since 2014 and contained 965 records with 29 features. First, by using the quadratic programming feature selection method, the variables which were effective on the model were selected and then, the C4.5 decision tree model was created through using input variables and determining the target variable. Data analysis was performed through Matlab software.
According to the applied model, it was found that fever, bleeding, sudden onset of symptoms, increased liver enzyms, increased total Bilirubin, decreased Hemoglobin, Hematuria, Leukocytosis, Proteinuria and Leukopenia have the greatest impact in the diagnosis of this disease.
According to the obtained results, the sensitivity of the proposed model is 95% and its specificity is 50%. Therefore, this model showed acceptable efficiency in diagnosing this disease in comparison with other studies done in medical data mining field.

Protecting patient’s secrets and private information such as disease name and diagnosis can play an important role in establishing trust in the treatment team. The present study aimed to investigate the issues surrounding the confidentiality of patients’ disease names and diagnoses in hospitals affiliated with Mazandaran University of Medical Sciences.
This was a cross-sectional study conducted on 216 patients in internal, surgical and women’s wards who had been hospitalized for at least 24 hours. Research tool was a questionnaire consisting of two parts: demographic data, and questions on the disclosure or confidentiality of information such as disease names. Patient's views were investigated through simple sampling method, and data were analyzed using SPSS software and chi-square test. Of the 216 patients entered in the study, 122 (56.5%) were male and 94 (43.5 %) were female. Participants were aged between 16 and 80, and their mean age was 47 ± 18.6 years.
The results showed that 49 (22.7 %) of the patients did not approve of an open display of their disease names and diagnoses above their beds, while a difference was observed between male and female patients in this regard (P

Non-Alcoholic Fatty Liver Disease (NAFLD) is one of the most prevalent liver diseases with several levels of severity. Recently, the FibroScan device has been used as a non-invasive method for measurement of elasticity of the liver and consequently its fattiness. The current study aimed at providing a low-cost and simple method for diagnosing of the disease through the clinical symptoms.
A collection of data obtained from 726 patients was used for conducting this study with each patient having a fatty liver disease with different levels of intensity. The severity of the disease for each patient was measured by FibroScan device along with medical tests and ultrasound monitoring. Then, the artificial neural networks were used for determination of the relationship between the data obtained from the patients and the intensity levels. Finally, by the aid of artificial intelligence techniques, a method is employed for extracting rules from artificial neural networks for representing the relationship between the data.
According to the results obtained from FibroScan device, among the 726 patients, 5 were located in F4 class, 23 in F3 class, 132 in F2class, 151 in F1, and 415 in F0 Class (Healthy people). According to the proposed method, the accuracy of diagnosis for various classes is as follows: 100% for F4 class, 99.31% for F3 class, 93.94% for F2 class, and 80.58% for F1 Class. Accordingly, this method can identify the samples in F4 and F3 classes with an ideal accuracy and the samples in F1 and F2 classes with a good accuracy.
Results in this research indicate that the proposed method can be used for diagnosing NAFLD and identifying its intensity levels with a lower costs and easier accessibility. It extracts the required rules for diagnosing the dieses that can be used by the physicians in their diagnosis.

Diagnostic methods that are practiced on the human or animal body have been explicitly excluded from patentability in most countries’ patent laws including European Patent Convention. However, rapid advances in science and technology, and consequently, the emergence of wide and diverse range of inventions in medical fields, have given rise to the difficulty and complexity of determining the scope of excluded diagnostic methods. Here, the approach of Technical Boards of Appeal to three issues has been controversial. First, what is the definition of “diagnostic method” as intended by the European lawmaker? Whether a claimed method is a “diagnostic method” only if it involves participation of a physician? What criteria can make a claimed method “practiced on the human or animal body” as it is required by the European Patent Convention? The response to these questions has been challenging and caused inconsistency and in some cases contradiction in European case-law. This Article tries to study and analyze the approach of Technical and Enlarged Boards of Appeal in a few key cases toward the patentability of diagnostic methods in Europe.

Although diagnostics methods in Article 4 of the patents, industrial designs and trademark act are excluded from patent protection, but about the nature and limits of the exception in our legal system has not raised much debate. The nature of the exception requires an understanding of the reasons of exception and regardless of these reasons we cannot reach a proper understanding of the nature and limits of diagnostic methods. Many reasons for excluding diagnostic methods from the scope of patentable invention are mentioned and the right to health are one of the most important of these reasons. In this paper, we firstly examine the human rights challenges of diagnostic methods patentability, and then interpret the nature of diagnostic methods in the light of the right to health. On this base we will examine the European Patent Office interpretations for these methods. By taking advantage of this base, we examine subjects, such as the role of medical intervention and practicing the methods on the human body in the coverage of this exclusion from patent protection.

One instance of errors in diagnosis which cannot be considered medical malpractice pertain to rare cases of VasculitisHenoch-Schonlein. The aim of this study was to shed light on the legal aspects of VasculitisHenoch-Schonlein by illuminating this disease, providing a comprehensive definition of what constitutes medical malpractice, and pointing out cases of errors in diagnosis resulting from the insufficient development of medical sciences. The disease: Henoch-Schonleinpurpura is a immunoglobulin Amediated non-autoimmunegranulomatosi self-limited multisystemic Vasculitis which affects small vessels. Symptoms used for the diagnosis of this clinical tetrad include non- thrombocytopenic palpable purpura, abdominal pain, arthritis, and kidney problems. In some cases, the symptoms of this disease manifest themselves a mere one or two days before the appearance of the rash, thus making diagnosis complicated.
Discussion and Failure to diagnosis the disease in patients suffering from severe abdominal pain prior to the appearance of rashes is a normal occurrence and in cases in which the area of the abdomen where it is most severe is not opened, even the doctor can be held accountable. Furthermore, plastering a patient’s limb, considering joint pain and movement limitations prior to the appearance of rashes, cannot be considered medical malpractice. In this study, it is argued that cases in which damage to the patient caused by the medical staff resulting from insufficient development of medical sciences and technology cannot be considered medical malpractice unless the doctor has failed to benefit from accessible and widely accepted scientific methods of diagnosis.

Today, “Computer Decision Support Systems” are designed and used in various fields of science. In this new atmosphere, “Expert Systems”, as a sub-branch of Artificial Intelligence, play a central role. In Expert Systems, decisions are made by the computer [1]. Expert Systems are knowledge-based systems that initially acquire knowledge from an expert person, and then transfer it to the computer, so that by making decisions based on the data collected, they can be of help to humans. So far, many Expert Systems have been presented in various fields like industry, control, space, financial decisions, etc [2]. Expert Systems have also found their way into medicine. For example; Dendralsoftware program (1965) to describe and express molecular structure [3], MYCIN software (1976) for diagnosis of coronary heart diseases [4], VM for monitoring Intensive Care patients [1], CADCUCEUS for diagnosing internal medicine related diseases [1], BLUEBOX for diagnosis and treatment of depression [1], as well as systems that detect acidic electrolytes, andmanagement of anesthesia education [1]. Our aim is to present an Expert System for diagnosing TB. A medical Expert System is a computer program that helps with decision making for diagnosis of diseases and suggests treatment methods. Once patient’s data are entered, the system performs diagnosis of disease, predicts potential complications, and suggests treatment method. These systems are distinct from other medical software, since, to arrive at a medical result, they imitate the rationale of an expert physician. Expert systems require many rules and facts of medical science about diseases and condition of the disease to be able to make decisions and present accurate results.