toktam khatibi

The paper introduces a new method called ABCL-EHI for human identification using electroencephalographic (EEG) signals. EEG signals have unique information among individuals, but current systems lack accuracy and usability. ABCL-EHI addresses this by combining a convolutional neural network (CNN) and a long short-term memory (LSTM) network with an attention mechanism. This attention mechanism enhances the utilization of spatial and temporal characteristics of EEG signals. The proposed system is evaluated using a public dataset of EEG signals from 109 subjects performing motor/imagery tasks. The results demonstrate that ABCL-EHI achieves high accuracy, with F1-Score scores of 99.65, 99.65, and 99.52 when using 64, 14, and 9 EEG channels, respectively. This outperforms previous studies and highlights the system's reliability and ease of deployment in real-life applications, as it maintains high accuracy even with a small number of EEG channels and allows users to perform various tasks while recording signals.

Optimizing resource allocation in emergency departments (ED) is challenging due to limited resources and high costs.

The objective of this study was to utilize data mining algorithms and simulation modeling to predict the length of stay (LOS) of patients and compare scenarios for increasing bed productivity.

Data mining algorithms, including Random Forest (RF) regression and CatBoost (CB) regression models, were used to predict the LOS based on patient demographic information and vital signs. The process of admission to discharge in the ED was simulated, and different scenarios were compared to identify strategies for increasing bed productivity.

The combination of RF regression and CB regression models performed better than other methods in predicting the LOS of patients. Simulation modeling demonstrated that optimal resource allocation and increased bed productivity could be achieved using predicted LOS values.

This study demonstrates that a combined approach of data mining and simulation can effectively manage ED resources and reduce congestion. The findings highlight the potential of advanced analytical techniques for improving healthcare service delivery and patient outcomes.

Process mining connects the disciplines of data mining and machine learning to business process management techniques. A business process is a series of independent and interdependent activities that transform inputs (data, materials, etc.) using one or more resources (such as time, employees, and money). It utilizes the necessary outputs. It is possible to examine the actual behavior of organizations, including the performance of individuals, departments, and resources, using process analysis techniques. The results of the process analysis, which typically includes the organization's business process models, can be compared to the organization's documents and requirements. Thus, processes will be able to be compared, reviewed, monitored, and enhanced. Process mining methods operate based on event logs stored in information systems. Using process mining without high-quality input data will not result in accurate conclusions about an organization's business processes. In recent years, researchers have focused on the evaluation and enhancement of the quality of input data using process mining techniques. The objective of this study is to identify and categorize the most significant data quality issues, as well as recognize the approaches proposed to address this challenge in process mining.

This research employs a systematic review with the intent of analyzing all valid evidence in order to answer the research questions. This study investigates 102 academic studies published between 2007 and 2021, including conference papers, journal articles, and theses. Towards this end, a systematic three-part research methodology was employed. In the first section, which included the research definition, the research field was defined first, followed by the research objectives and queries. In the concluding step of this section, the research's scope is defined. In the second section, the research methodology and entry criteria for the studies discovered during the search for scientific resources are defined. Finally, the identified studies are evaluated in terms of their citations and classified. In the third section, which is devoted to the evaluation of the research, the concluding research of the study is conducted, and then, based on the investigation of the preceding studies, the findings and conclusions are determined. Important data and evidence were extracted from the collated research, allowing for the creation of the necessary tables and graphs.

In recent years, researchers have paid more attention to data quality challenges in the process mining, according to the findings of recent research. In 2019 and 2020, the greatest number of studies will have been published. It was also discovered that the majority of articles were published in three scientific databases, namely Springer, IEEE, and Elsevier. 51% of the studies examined were presented at prestigious conferences. 36% of the studies were published in prestigious scientific journals, while the remaining 13% were represented in dissertations and university reports. The study of the selected articles revealed that 20 data quality issues that can arise in the input data have been investigated in the literature. These challenges have been categorized into five levels: trace, event, case, activity, and timestamps, and four foundational approaches have been identified that have been used to evaluate and resolve data quality challenges in the mining process. 1) data quality frameworks 2) preprocessing 3) anomaly detection 4) repair. Our findings indicate that preprocessing techniques that seek to remove chaotic and infrequent behaviors from the event log have received more attention than other techniques. In addition, these results demonstrate that, in recent years, the discovery of anomalies and the reconstruction of missing events have become popular research topics within the field of process mining. Examining studies related to the field of data quality in the data mining process reveals an abundance of approaches and methods for addressing data quality challenges. Investigations revealed that the use of colorful Petri nets as a mathematical method has been considered in all selected research projects.

The data needed for process mining methods can be obtained from various sources. One of the major advantages of process mining is that it is not limited to a specific type of system. Any workflow-based system, such as ticketing, resource management, databases, data warehouses, legacy systems, and even manually collected data, can be analyzed as long as it can be separated using case ID, activity, and timestamp attributes. In real-world scenarios, most data is not collected for process mining purposes or is unsuitable for use in process mining analyses. Especially data that is recorded manually or scattered among various isolated systems can contain errors. Despite the efforts made to improve the quality of input data in the mining process, it is still necessary to develop efficient frameworks and methods to identify, evaluate, and address data quality challenges in real business processes, which are often characterized by high volume and complexity. The results of this research can offer a fresh perspective for researchers, data science specialists, and business analysts.

Noise is a critical concern for practical machine learning, especially medical applications. There exist two kinds of noise, including attributes and class noises. Class noise is potentially more dangerous, so various filtering techniques, particularly prediction-based, have been proposed to control it. Great attention to class noise has made the researchers ignorant that attribute noise, in turn, is harmful. Hence, it is improper to utilize prediction-based filtering to correct class noise without regarding attribute noise.

To tackle this problem, we developed a method to fix class noise in the presence of attribute noise. This method excludes noisy components of attributes, based on the information bottleneck principle, by compressing attributes locally and gradually in successive iterations. It uses heterogeneous ensemble filtering to correct class noise. In the initial iteration, filtering is conservative and progressively, in succeeding iterations, tends to majority vote.

We compared the proposed method's predictive performance with the RF majority-vote filter on three real binary classification problems from the UCI repository, including Breast, Transfusion, and Ionosphere. Random forest, adaptive boosting, support vector machines, and naïve Bayes were used for assessing methods from different viewpoints. Results show that the proposed method performed better than the RF majority-vote filter and seems to open a promising research scope for noise filtering.

Our study revealed that correcting class noise by controlling attribute noise enhances the predictive performance of classifiers.

Background and Objective:

Left atrium is a heart chamber which volume changes has much importance for identifying, controlling and treatment of cardiovascular diseases. In the current methods, left atrium chamber volume (LAV) is estimated from echocardiography images.

For this purpose, the image segmentation and feature extraction tasks have been performed. The accuracy of these methods highly depends on the quality and performance of the method used for image segmentation and the expertise of the specialist. Therefore, left atrium chamber quantification using automatic image analysis methods is necessitated. In this study, a novel automatic approach by combining convolutional neural network with Convolutional Block Attention Module is proposed for left atrium chamber quantification in echocardiography images with an end-to-end fashion without requiring any prior image segmentation. Two different channel and spatial attention modules are embedded in the designed CNN for identifying the key properties of the output feature map and finding important regions for improving the CNN performance.

The proposed model in this study estimates LAV in end-of-systole and end-of-diastole frames with the average R2 of 96.25% and 88.76%, respectively. Our experimental results show that using attention module in CNN architecture improves the performance of CNN for Left atrium chamber quantification with feature extraction focusing on identifying the key properties and discriminating regions.

The proposed method in this study can be used in computer assisted systems (CAD) for automatic chamber quantification with improving the accuracy and speed compared to manual Left atrium chamber quantification.

In today's challenging world, organizational growth is not possible without the efficient use of data. Process mining uses machine learning methods and business process management concepts to extract hidden knowledge about business processes from data stored in information systems. Process Discovery is the first step in process mining. The main goal of process discovery is to transform the event log into a process model. However, using process discovery methods will not be possible without appropriate data because any analysis based on low-quality data will lead to poor insights and bad decisions that will negatively affect the performance of the organization or business. This paper aims to provide a new conceptual framework for preprocessing data input into process discovery methods to improve the quality of the extracted model. The proposed conceptual framework has been developed using a qualitative research process based on grounded theory. For this purpose, 102 articles related to the domain of data quality in process mining were reviewed, and the most critical challenges of data quality in this field have been identified after filtering and integrating them from the literature, including: Are "noisy/infrequent events," "outlier events," "anomalous events," "missing values," "incorrect time format," "ambiguous timestamps," "synonymous activities" and "size and complexity." Then, the basic steps for data preprocessing and cleaning tasks are defined, which include the activities of "repair," "anomaly detection," "filtering," and "dimensional reduction." The final preprocessing framework then builds on data quality issues and identified activities. Four standardized datasets derived from real-world processes were used to assess the proposed framework's performance. Firstly, these data are raw, and secondly, four standard process discovery algorithms are applied after preprocessing by the introduced framework. The results showed that the preprocessing of the input data leads to the improvement of the model quality criteria extracted from the process discovery algorithms. Furthermore, to evaluate the validity of the proposed framework, its performance was compared with three preprocessing methods"sampling," "statistical preprocessing," and "prototype selection," which the results indicate better efficiency of the proposed approach. The results of this study can be used as guidelines by data and business analysts to identify and resolve data quality problems in process mining projects.

Accurate delineation of myocardial fibrosis in Late Gadolinium Enhancement Cardiac Magnetic Resonance (LGE-CMR) has a crucial role in the assessment and risk stratification of HCM patients. As this is time-consuming and requires expertise, automation can be essential in accelerating this process. This study aims to use Unet-based deep learning methods to automate the mentioned process.

This study used three consecutive Unet-based networks for Region of Interest (ROI) detection, myocardial segmentation, and fibrosis delineation. The study was conducted on LGE images of 41 images diagnosed with HCM, which were contoured by two experts.

This model reported a Dice similarity coefficient and accuracy of 89.74 and 98.22 in myocardial segmentation and 88.42 and 94.66 in fibrosis delineation, respectively, and could outperform the previous methods

The results confirm that using deep learning methods for delineating myocardial fibrosis not only can automate the process, but also helps improve the results and decrease the required time.

The health industry is a competitive and lucrative industry that has attracted many investors. Therefore, hospitals must create competitive advantages to stay in the competitive market. Patient satisfaction with the services provided in hospitals is one of the most basic competitive advantages of this industry. Therefore, identifying and analyzing the factors affecting the increase of patient satisfaction is an undeniable necessity that has been addressed in this study.

Because patient satisfaction characteristics used in hospitals may have a hidden relationship with each other, data mining approaches and tools to analyze patient satisfaction according to the questionnaire used We used the hospital. After preparing the data, the characteristics mentioned in the questionnaire for patients, classification models were applied to the collected and cleared data, and with the feature selection methods, effective characteristics Patients were identified and analyzed for satisfaction or dissatisfaction.

Based on the findings of the present study, it can be concluded that the factors of patient mentality of the physician's expertise and skill, appropriate and patient behavior of the physician and food quality (hoteling) respectively have a higher chance of increasing patient satisfaction with Establish services provided in the hospital.

Comparing the approach used in this study with other studies showed that due to the hidden effects of variables on each other and the relatively large number of variables studied, one of the best options for analyzing patient satisfaction questionnaire data, Use of data mining tools and approaches

The high prevalence of cardiovascular diseases has caused many health problems in countries. Cardiac Rehabilitation Programs (CRPs) is a complementary therapy for Percutaneous Coronary Intervention (PCI) patients. However, PCI patients hardly attend CRPs. This study aims to decipher the reasons why PCI patients rarely participate in CRPs after PCI. The parameters affecting the attendance of the patients at CRPs were identified by using the previous studies and opinions of experts. A questionnaire was designed based on the identified parameters and distributed among PCI patients who were referred to Tehran Heart Center Hospital. According to data mining approach, 184 samples were collected and classified with three algorithms (Decision Trees, k-Nearest Neighbor (kNN), and Naïve Bayes). The obtained results by decision trees were superior with the average accuracy of 82%, while kNN and Naïve Bayes obtained 81.2% and 78%, respectively. Results showed that lack of physician’s advice was the most significant reason for non-participation of PCI patients in CRPs (P< .0001). Other factors were family and friends’ encouragement, paying expenses by insurance, awareness of the benefits of the CRPs, and comorbidity, respectively. Results of the best model can enhance the quality of services, promote health and prevent additional costs for patients.

Surgical suits allocate a large amount of expenses to hospitals; on the other hand, they constitute a huge part of hospital revenues. Patient flow optimization in a surgical suite by omitting or reducing bottlenecks which cause loss of time is one of the key solutions in minimizing the patients’ length of stay[1] (LOS) in the system, lowering the expenses, increasing efficiency, and also enhancing patients’ satisfaction. In this paper, an analytical model based on simulation aiming at patient flow optimization in the surgical suite has been proposed. To achieve such a goal, first, modeling of patients' workflow was created by using discrete-event simulation. Afterward, improvement scenarios were applied in the simulated model of surgical suites. Among defined scenarios, the combination scenario consisting of the omission of the waiting time between the patients’ entrance to the surgical suite and beginning of the admission procedure, being on time for the first operation, and adding a resource to the resources of the transportation and recovery room, was chosen as the best scenario. The results of the simulation indicate that performing this scenario can decrease patients’ LOS in such a system to 22.15%.

Background of the Study: Patient discharge process starts from the point of the initial order of the physician order and continues to the discharge time of a patient and the release of the bed that was allocated to him/her. Lengthening the patient discharge process is regarded as a negative factor in the management of beds; this lengthy process leads to delay in accepting new patients, increases the waiting time for the patients who demand an empty bed (especially in emergencies), imposes extra costs on the hospital, and creates some other problems. Therefore, discharge process pattern extraction and analysis can be helpful to shorten this process, accelerate the process of admission, reduce the costs of hospital, etc.
In the present study, first, the fuzzy model of the hospital's discharge process and the hospital's workflow pattern have been drawn according to the most frequent patterns in the data and based on the expert's opinions. Afterwards, the dotted charts of the different sectors have been extracted and analyzed using the process mining tools.
Findings: After analyzing the dotted charts, the delayed segments were specified on the pattern of the workflow and finally, some suggestions have been offered through separating the four areas of the human resources, the system, the environment.
The discharge process of a hospital is associated with almost all its sectors and to improve it, a huge part of the organization is involved.

Performance is a multi-dimensional and dynamic concept. During the past 2 decades, considerable studies were performed in developing the hospital performance concept. To know literature key concepts on hospital performance, the knowledge visualization based on co-word analysis and social network analysis has been used.
Documents were identified through “PubMed” searching from1945 to 2014 and 2350 papers entered the study after omitting unrelated articles, the duplicates, and articles without abstract. After pre-processing and preparing articles, the key words were extracted and terms were weighted by TF-IDF weighting schema. Support as an interestingness measure, which considers the co-occurrence of the extracted keywords and "hospital performance" phrase was calculated. Keywords having high support with "hospital performance" are selected. Term-term matrix of these selected keywords is calculated and the graph is extracted.
The most high frequency words after “Hospital Performance” were “mortality” and “efficiency”. The major knowledge structure of hospital performance literature during these years shows that the keyword “mortality” had the highest support with hospital performance followed by “quality of care”, “quality improvement”, “discharge”, “length of stay” and “clinical outcome”. The strongest relationship is seen between “electronic medical record” and “readmission rate”.
Some dimensions of hospital performance are more important such as “efficiency”, “effectiveness”, “quality” and “safety” and some indicators are more highlighted such as “mortality”, “length of stay”, “readmission rate” and “patient satisfaction”. In the last decade, some concepts became more significant in hospital performance literature such as “mortality”, “quality of care” and “quality improvement”.

Background and Kidney failure reduces the kidney function and in long term it leads to chronic kidney disease. One of the main complications of this disease is irreversible damage to the kidneys (end-stage kidney disease) and hemodialysis is the main method used to treat advanced kidney failure. The main problem associated with hemodialysis is treating anemia caused by lack of erythropoietin secretion in kidney which is usually treated by synthetic erythropoietin. On the other hand, choosing the right dosage of erythropoietin is important because it is expensive and could have some complications. This research aimed at predicting the dosage of erythropoietin and identifying affecting factors. Data was collected from a dialysis center in Tehran and data mining methods were used. The input variables were measured in the past 6 months of treating patients with erythropoietin. The sequential data was then converted to the bag of features (BOF) format. Then support vector machines and random forest were applied on the BOF to predict the erythropoietin dosage. The amount of medication in previous months was found to be an important factor in determining the appropriate dosage of erythropoietin for the next month. In optimal condition, random forest and SVM could predict the erythropoietin dosage with an average accuracy of 90% and 79%, respectively. This study identified the factors influencing the treatment and control of anemia in hemodialysis patients. These results could be of great benefit in prescribing the proper dosage of erythropoietin, and reducing the treatment cost and duration. Moreover, it helps to prevent the complications caused by excessive use of erythropoietin such as increase in hemoglobin level.

Myocardial Infarction (MI) is the most common cause of death in developed and developing countries. Based on World Health Organization prediction, MI will be the leading cause of death in the world up to 2020. The main goal of acute myocardial infarction therapy is reperfusion which is performed by two treatment approaches, although these two methods use for unique complication treatment but they are different in treatment cost and quality; Since the costs of health care is growing, this study aimed at identifying affected differentiated costs among patients with acute MI with the purpose of providing a framework to compare two treatment methods. In presented model in current study, with the purpose of select affecting cost attributes utilized hybrid of Genetic algorithm and decision tree approach as a new method and compare its result using random method. Study result presented classifies error reduction up to 0.2 compare with random method error. Then, based on selected attributes, presented a framework comparing two treatment methods. Also, in the majority of direct cost and total cost, Primary Angioplasty was cost effective more than Thrombolytic Therapy, but in indirect cost, we achieved some results dissimilar to physician's expected results such as kidney failure. Suggested approach in this research can be a guidance to physicians and insurances institutes in acute myocardial infarction treatment policies.

Laparoscopy or minimally-invasive surgery is a surgical technique in which the surgical operations are performed via a few small incisions. This kind of surgery has fewer complications over open surgery. Finding methods for shortening the time of laparoscopic surgeries can improve operating room efficiency. An approach to shortening the time of laparoscopic surgeries is identifying the interruptions in these surgeries and preventing from their occurrence or reducing the potential of occurrence of the identified interrupt causes. In this paper, the interrupt causes of laparoscopic surgeries are prioritized based on the identified causal relations between interrupt causes. Research population is the laparoscopic surgeries performed in Hasheminezhad kidney center in May-June 2013. For this purpose, 25 laparoscopic surgeries are observed in this hospital in this time interval. Causal relations among interrupt causes are identified from the gathered data. The main causes are identified and ranked based on Fuzzy TOPSIS method. For prioritizing the interrupt causes, frequency of occurrence, average length, severity degree, the potential of reducing interrupt occurrence and the potential of preventing interrupt occurrence are considered.Experimental results show that the most important interrupt causes in laparoscopic surgeries are staff shortage or multi-tasking staff, foggy lens, unavailable surgical instruments, dirty lens and finally low-experienced staff. Moreover, sensitivity analysis on criteria weighting show that the mentioned interrupt causes are the five most-important interrupt causes in more than 80% of the evaluated scenarios.Preventing the occurrence of the most-important interrupt causes can improve the surgical time. If it is not possible, reducing the average length of interrupts caused by the identified main causes can be considered for improvement of the operating room efficiency.

Myocardial Infarction happens when blood stops flowing properly to part of the heart and the heart muscle is injured due to not receiving enough oxygen. Percutaneous coronary intervention (PCI)، is a non-surgical procedure used to treat the Myocardial Infarction. When PCI is success، average life expectancy of patient is increased and patient returns to normal life. Every year، part of PCI knowledge، as scientific articles، are provided to medical communities. Extraction of implicit knowledge in papers facilitated access to PCI knowledge، PCI Knowledge map shows a comprehensive picture of PCI. Knowledge map is a tool for presenting complex process and relation between concepts. This paper base on text mining and using Co-occurrence Analysis method، extracted causal relationships between PCI concepts from abstract of 20 years paper and illustrating these relationships، then Validated PCI map by experts، Importance of relationships between concepts is determined Using fuzzy cognitive map. in this paper FCM is trained with AHL and DHL algorithms، result indicated PCI has scientific Structure. PCI Fuzzy Knowledge map can be used in training، process of PCI changes، and determines the most important factors that influence the success of PCI.

Identification of surgical instruments in laparoscopic video images has several biomedical applications. While several methods have been proposed for accurate detection of surgical instruments, the accuracy of these methods is still challenged high complexity of the laparoscopic video images. This paper introduces a Surgical Instrument Detection Framework (SIDF) for accurate identification of surgical instruments in complex laparoscopic video frames.

Based on the Generalized Near-Set Theory, a novel image segmentation algorithm, termed Generalized Near-Set Theory-based Image Segmentation Algorithm (GNSTISA) was developed. According to SIDF, first GNSTISA is executed to segment the laparoscopic images. Next, the segments generated by GNSTISA are filtered based on their color and texture. The remaining segments would then indicate surgical instruments.

Using the laparoscopic videos of varicocele surgeries obtained from Hasheminezhad Kidney Center, the performance of GNSTISA was compared with previous image segmentation methods. The results showed that GNSTISA outperforms the earlier algorithms in term of accurate segmentation of laparoscopic images. Moreover, the accuracy of SIDF in identifying the surgical instruments was found superior to that of other methods.

SIDF eliminates the limitations of previous image segmentation methods, and can be used for precise identification of surgical instrument detection.

Laparoscopy or minimally invasive surgery is a surgical procedure in which laparoscope and other surgical instruments are inserted inside body via a few small incisions. Laparoscope is used to look inside the patient''s body and records displayed images. Temporal segmentation of laparoscopic videos has many applications like detecting laparoscopic anomalies and interrupts. It is prerequisite of laparoscopic action recognition for tagging laparoscopic video clips, training to the surgeons and fast retrieval of tagged laparoscopic video clips. Temporal segmentation of videos is is done with the aim of generating homogeneous segments. In this paper, a novel approach for minimally-invasive video segmentation (MIVS) is proposed. In MIVS, several data sets are extracted from laparoscopic videos for increasing the confidence and reducing error of estimation. Each extracted data set is segmented individually with Genetic Algorithm several times after outlier removal. Each time, a different cost function is used as objective function of GA. The correlation coefficient is measured between objective values of individuals of each GA execution and their associated performance measures including detection rate, recognition rate and accuracy. Cost functions having negative correlation with all mentioned performance measures are selected as cost function of the next step segmentation which segments several data sets simultaneously exploiting Multi-objective GA. MIVS is tested on laparoscopic videos of Varicocelle and UPJO surgeries collected from HASHEMINEZHAD Kidney Center. Experimental results show that MIVS can segment laparoscopic videos with accuracy of 94.89%. MIVS outperforms previous presented segmentation methods in segmenting minimally-invasive surgical videos.

Surgical cancelation is a significant source of time and resource waste, patientsafety risk, and stress for patients and their families. In this study, a risk management-based approach is developedto prioritize factors contributing to surgical cancellation. Factors leading to surgical cancellation were comprehensively classified based on literature review.A Fuzzy Failure Mode and Effect Analysis were developed for identifying the relative importance of the potentialsurgical cancellation factors. Validity of the results was examined by obtaining experts’ opinions. Our analysis identified inadequacy of recovery beds, inadequacy of ICU beds, high-risk surgery, andhigh blood pressure and diabetes as the most important factors contributing to surgical cancelation. According to our results, the Fuzzy Failure Mode and Effect Analysis can successfully rank thefactors contributing to surgical cancellation. Our results encourage further use of the risk management theory andtools combined with fuzzy set theory to support and facilitate the clinical decision-making process.