saeed shirazi

Data mining as an integral part of the knowledge discovery in database (KDD) has gained significant attention over the past few years. By and large, data mining is the process of finding interesting structures in a considerably voluminous amount of data. Owing to its methods and algorithms supporting variable types of data, the data mining approach has been applied in many scientific areas, including the healthcare industry.Regarding this matter, in this paper, we elaborate on the latest papers, including data mining techniques and algorithms in the healthcare field of research.

We present a data mining review based on the newest researches. Afterward, we categorize data mining papers in healthcare based on supervised and unsupervised learning paradigms as well as classifying them in terms of their applications in the healthcare domain.

In every healthcare application, we propose some summary points of the papers. At last, we delve into the absence and hence, the necessity of existing some novel methods in healthcare domains in this researches.

Recently, the use of data science techniques in healthcare has been increased remarkably. Community detection as one the important methods of data science is utilized in the health domain.

This paper detects disease areas based on combination of big data and graph mining methods on drug prescriptions. At first, network of prescription is designed, and Louvain algorithm is applied for community detection of 50000 Iranian prescriptions in 2014 gathered from the Iranian Health Insurance Organization. We use modularity metric for validation of the results and the experts’ opinion as the external validation of communities.

The outputs are consist of six communities. These communities are labeled based on experts’ opinion that present the disease fields.

The Louvain algorithm has the ability to detect the major communities of the prescription database with an acceptable accuracy. We have proven that these communities present the disease fields.

The existence of an intellectual structure for every field is essential for managers and scholars. Intellectual structures provide a comprehensive map of knowledge that can guide researchers and managers to have a better view of their fields. Besides, with high-speed and massive amounts of data and information generation, reading and surveying of all resources are severely tricky. Intellectual maps solve this problem and make a situation for control and monitoring this voluminous and high-speed generated data. Epidemiology is regarded as one of the exciting fields which many researchers focused on it. A study of the structure and criteria of different epidemiological fields has not been done yet. Indeed, there is no serious effort for knowledge discovery of hidden information on epidemiological texts.

In this paper, in order to survey this field, an intellectual structure is provided using co-word analysis. Utilizing co-word analysis discloses relationships and structure among research subjects and topics in a field.

Finally, four main clusters were determined, namely: genetic (with 30.53% of surveyed papers), illness (29.47%), modeling (23.16%), and prevention (16.84%).

According to epidemiology co-word network, epidemiology area has not been studied from enough different areas, especially from novel technologies

One of the statistical methods used to analyze the time-to-event medical data is survival analysis. In survival models, the response variable is time to the occurrence of an event. The main characteristic of survival data is the existence of censored data. When we have the distribution of survival time, we can use parametric methods. Among the important and popular distributions that can be used, we can mention the Weibull distribution. If the data derives from a heterogeneous population, simple parametric models (such as Weibull) would not fit the data appropriately. One of the methods which have been introduced to overcome this problem is the use of mixture models.

To assess the validity of the two-component Weibull mixture model, we use a simulation method on heterogeneous survival data. For this purpose, data with different sample sizes were produced in a batch of 1000. Then, the validity of the model is checked using root mean square error (RMSE) criterion

It is obtained that increasing the sample size would decrease the RMSE in the parameters. However the maximum observed RMSE in all the parameters was negligible.

The Bayesian Weibull mixture model was a proper fit for the heterogeneous survival data.

After decline of the Mughal Empire, many local governments emerged across Indian Subcontinent. Nawabs of Bhopal was one of these local states in central Indian subcontinent. Nawabs of Bhopal’s reign for more than one century ruled by women (Begums of Bhopal), is unique in Islamic history. Begums of Bhopal during their government carried out numerous reforms in various fields, and thus the golden age of Nawabs of Bhopal government appeared. By writing historical books and their travel literature, three of begums, i.e. Sikandar Begum, Shahjahan Begum, Sultan Jahan Begum, played an important role in writing the history of Nawabs of Bhopal. In this paper, the role of Begums of Bhopal in this field is studied and their works are introduced.

The Sufis had a great role in Islam promulgation in India Subcontinent when they migrated to India. Chishtiyya and Suhrawardiyya are considered as the first Sufi sects that entered into this land. Contemporary with the Delhi Sultans power peak in 7th and 8th centuries, they obtained great spiritual influence among the people from different strata. This caused the majority of Delhi Sultans to have close and respectfully relations with Sufis. They tried to attract them toward their courts to obtain legitimate and support for their reigns, from both political and governmental aspects. The masters of these two sects had different stances, whereas most of Chishtiyya masters refused entering into political and governmental affairs, Suhrawardiyya masters accepted some responsibilities such as “Sheikh- ul- Islam”. In this essay the relations between the mentioned Sufis sects and Delhi Sultans, and especially the most important changes of their relationships, are studied in details.