جستجوی مقالات مرتبط با کلیدواژه « data integration » در نشریات گروه « پزشکی »

In the field of management, the statistics and performance of the deputies and functions of the organization are always of great importance, which requires instant access to the latest status of the system under coverage and minimal forecast of the future situation, to provide quality services Also improve. All of this justifies the existence of an intelligent statistical system with decision-making capabilities.

In this study, we try to create an integrated web-based system in order to electronicize the processes of defining and recording statistical information along with the aggregation of scattered data in the system and finally extract reliable knowledge from this data using modern artificial intelligence methods. In order to make decisions in the fields of health, treatment, medical education as well as health management. The intelligent management system of North Khorasan University of Medical Sciences has been designed and implemented under the web with the technique of making Webserver software & Learning & data mining in three phases for 48 months in PHP language and MySQL database.

For this study, the statistical turnover structure of the university was classified into 1058 units. By classification based on the decision tree algorithm and reviewing the records of reports submitted to organizations at the same level and above, we reached 269833 units of data, 376 data types and 3885 data content. In the proposed method, the time cost of data transfer is less than one minute per item, and the required human resources due to the addition of multiple control sensors is 2640 people-hours per year, and significant time and financial savings were made.

According to the findings, it seems necessary to create a mechanized system for distribution, collection, control, analysis and reporting of statistical indicators that can have high accuracy and time and financial savings.

The major issue for the future of bioinformatics is the design of tools to determine the functions and all products of single-cell genes. This requires the integration of different biological disciplines as well as sophisticated mathematical and statistical tools. This study revealed that data mining techniques can be used to develop models for diagnosing high-risk or low-risk lifestyles for colorectal cancer.

In this retrospective study, a dataset consisting of information relevant to 84 patients and 225 healthy individuals with 25 attributes was collected. This information was on patients diagnosed from 2006 to the first quarter of 2014. The most widely used techniques in the medical informatics literature including support vector machine, Naive Bayes, decision tree, and k-nearest neighbor were used to develop the models.

The developed models are able to distinguish peoplechr('39')s lifestyles efficiently. A well-developed non-technical measure can properly determine the true value of individual predictions, whether true or false, at actual costs, and indicate a true measure of the cost savings in the health system by each model. Among the developed models, only two models were able to meet the criteria set for use in the real world.

The developed models should not only be technically evaluated, but should also be examined in terms of metrics accepted for the medical field as well as feasibility for real problem solving.

Data and information resources are scattered in health care institutions and health systems and integrated access to them for reporting and analysis is challenging. Application of data warehouse can be a solution to this issue. Thus, this study aimed to investigate data warehouse applications in the field of health, identify its challenges, and provide solutions.
This was a comprehensive and narrative review study. First, the keywords in the field of data warehouse were determined and the extent of the search was limited to the filed of health. Than, the relevant articles were selected from Google Scholar, PubMed, Scopus, Web of Knowledge, and ScienceDirect databases.
Findings: Today, most health care institutions face the challenge of collecting, organizing, and retrieving disparate and bulky data for research, quality improvement, outcome analysis, or best practices determination. The solution to most of these challenges is data warehouse. This technique may resolve many problems in data management and utilization of health system data.
The use of systems such as data warehouse is the main objective of the health system for the prevention of the onset or progression of diseases and improvement of public health. This paper was a first step in creating and using the National Health Data Warehouse, and thus, requires a national approach to address its challenges and solutions.