Data Mining - Concepts and Techniques.

Computational and Mathematical Methods in Medicine

Cell-laden hydrogels for osteochondral and cartilage tissue engineering

3D bioprinting of functional tissue models for personalized drug screening and in vitro disease modeling

LncRNA MEG3 inhibited osteogenic differentiation of bone marrow mesenchymal stem cells from postmenopausal osteoporosis by targeting miR-133a-3p.

Background: In recent years, liver disorders have been continuously increased. Proper performance of data mining techniques in decision-making and forecasting caused to use them commonly in designing of automatic medical diagnostic systems. The main aim of this paper is to introduce a classifier for diagnosis of liver disease that not only has high precision but also is understandable and has been created without expert knowledge. Methods: In regards to this purpose, fuzzy association rules have been extracted from dataset according to fuzzy membership functions which determined by fuzzy C-means clustering method; while each time, extracting fuzzy association rules, one of the five quality measures including confidence, coverage, reliability, comprehensibility and interestingness is used and five fuzzy rule-bases extracted based on them. Then, five fuzzy inference systems are designed on the basis of obtained rule-bases and evaluated in order to choose the best model in terms of diagnostic accuracy. Results: The proposed diagnostic method was examined using data set of Indian liver patients available at UCI repository. Results showed that among considered quality measures, interestingness, reliability and truth outperformed respectively, and yielded precision, sensitivity, specificity and accuracy of more than 90%. Conclusion: In this paper, a classification method was developed to predict liver disease which in addition to high classification accuracy, it has been created without expert knowledge and provided an understandable explanation of data. This method is convenient, user friendly, efficient and requires no expertise.

http://mjiri.iums.ac.ir/article-1-4234-en.pdf

A novel method for fuzzy diagnostic system design.

Clinical decision support system to predict chronic kidney disease: A fuzzy expert system approach.

One of the most effective treatments for patients with obesity, albeit with some complications, is obesity surgery. The aim of this study was to develop a clinical decision support system (CDSS) to predict the early complications of one-anastomosis gastric bypass (OAGB) surgery. This study was conducted in Tehran, Iran on patients who underwent OAGB surgery in 2011–2014 in five hospitals. Initially, variables affecting the OAGB early complications were identified using the literature review. Patients’ data were extracted from an existing database of obesity surgery. Then, different artificial neural networks (ANNs) (multilayer perceptron (MLP) network) were developed and evaluated for prediction of 10-day, 1-month, and 3-month complications. Factors including age, BMI, smoking status, intra-operative complications, comorbidities, laboratory tests, sonography results, and endoscopy results were considered important factors for predicting early complications of OAGB. A CDSS was developed with these variables. The accuracy, specificity, and sensitivity of the 10-day prediction system in the test data were 98.4%, 98.6%, and 98.3%, respectively. These figures for 1-month system were 96%, 93%, and 98.4% and for the 3-month system were 89.3%, 86.6%, and 91.5%, respectively. Using the CDSS designed, we could accurately predict the early complications of OAGB surgery.

A Clinical Decision Support System for Predicting the Early Complications of One-Anastomosis Gastric Bypass Surgery.

Background Finding a valid diagnosis is mostly a prolonged process. Current advances in the sector of artificial intelligence have led to the appearance of expert systems that enrich the experiences and capabilities of doctors for making decisions for their patients. Objective The objective of this research was developing a fuzzy expert system for diagnosing Cystic Fibrosis (CF). Methods Defining the risk factors and then, designing the fuzzy expert system for diagnosis of CF were carried out in this cross-sectional study. To evaluate the performance of the proposed system, a dataset that corresponded to 70 patients with respiratory disease who were serially admitted to the CF Clinic in the Pediatric Respiratory Diseases Center, Masih Daneshvari Hospital in Tehran, Iran during August 2016 to January 2017 was considered. Whole procedures of system construction were implemented in a MATLAB environment. Results Results showed that the suggested system can be used as a strong diagnostic tool with 93.02% precision, 89.29% specificity, 95.24% sensitivity and 92.86% accuracy for diagnosing CF. There was also a good relationship between the user and the system through the appealing user interface. Conclusion The system is equipped with information, knowledge, and expertise from certified specialists; hence, as a training tool it can be useful for new physicians. It is worth mentioning that the accomplishment of this project depends on advocacy of decision making in CF diagnosis. Nevertheless, it is expected that the system will reduce the number of false positives and false negatives in unusual cases.

/pdf/a-fuzzy-rule-based-expert-system-for-diagnosing-cystic-2a8elv1zf0.pdf

A fuzzy rule-based expert system for diagnosing cystic fibrosis.

Background An ever growing for application of electronic health records (EHRs) has improved healthcare providers' communications, access to data for secondary use and promoted the quality of services. Patient's privacy has been changed to a great issue today since there are large loads of critical information in EHRs. Therefore, many privacy preservation techniques have been proposed and anonymization is a common one. Objectives This study aimed to investigate the effectiveness of anonymization in preserving patients' privacy. Methods The articles published in the 2005-2016 were included. Pubmed, Cochrane, IEEE and ScienceDirect were searched with a variety of related keywords. Finally, 18 articles were included. Results In the present study, the relevant anonymization issues were investigated in four categories: secondary use of anonymized data, re-identification risk, anonymization effect on information extraction and inadequacy of current methods for different document types. Conclusion The results revealed that though anonymization cannot reduce the risk of re-identification to zero, if implemented correctly, can manage to help preserve patient's privacy.

Azam Orooji

Papers

A novel method for fuzzy diagnostic system design.

Clinical decision support system to predict chronic kidney disease: A fuzzy expert system approach.

A Clinical Decision Support System for Predicting the Early Complications of One-Anastomosis Gastric Bypass Surgery.

A fuzzy rule-based expert system for diagnosing cystic fibrosis.

Effectiveness of Anonymization Methods in Preserving Patients' Privacy: A Systematic Literature Review.