Corrigendum to ``Academic data derived from a university e-government analytic platform: An educational data mining approach'' [Data in Brief, Volume 49 (2023) /109357].

[This corrects the article DOI: 10.1016/j.dib.2023.109357.].

Academic data derived from a university e-government analytic platform: An educational data mining approach.

The article describes the academic data, which derived from a University E-government analytic platform, which supports the facilitation of blended learning in a Greek University during and after the COVID19 outbreak [1,2]. University e-government services refer to a set of information systems that facilitate the functionalities of the University and enable the management of the underlying information [3,4]. These educational, research and managerial services, also called U-EGOV, follow the four stages of e-government (Presence, Interaction, Transaction, Transformation) [5]. In the presented approach, the data was aggregated from the university services with an automated process and includes all the individual U-EGOV services, that is the synchronous and asynchronous educational platforms, the teleconferencing tool, etc. The dataset created contains information about the courses, the assignments, the grades, the examinations, as well as other significant academic elements of the synchronous and the asynchronous education that takes place in the University. The analysis spans from the spring semester of the academic year 2019-2020, the winter semester of the academic year 2020-2021 to the spring semester of 2020-2021 (three semesters in total). The sample consists of 4800 records and after the preprocessing 4765 records (statistics of courses attended by students) which include 1661 unique students within the university in twenty (20) courses. We have followed an educational data mining approach on the collected data by utilizing an automated data aggregation mechanism to gather data for the selected courses, in order to enhance the learning process and the quality of services. The dataset can be reused: i) as a reference point to measure the quality of the academic outputs and its progress through the years and ii) as a basis for similar analysis in other Higher Educational Institutions (HEIs).

Automatic diagnosis classification of patient discharge letters.

CAIRN (Computer Assisted Medical Information Resource Navigation) is a prototyping System that allows flexible medical data storage and retrieval supporting medical informatics research. In this paper methods that automate the selection of ICD-9 diagnosis (International Classification of Diseases and Diagnoses, 9th Revision) are investigated. We present the Text Data Mining module extension of CAIRN and its application in order to organize in a systematic way uncontrolled terms, to propose relationships between uncontrolled terms and finally aid the diagnosis classification.