Dynamic Distance Learning Framework using Problem Based Assessment

Education is undergoing a major paradigm shift towards learning rather then teaching. Learning is no longer considered as a process transferring and distributing knowledge to the students. It is now viewed as a transformational process whereby students acquire facts, principles, and theories as conceptual tools for reasoning and problem solving in meaningful contexts. Internet is playing an important role for such student based learning. The objective of this paper is to develop an educational framework, using data mining technologies, with the help of Dynamic Web technologies (JSP and .net) that will be used by teachers to organize the course contents on the web according to existing infrastructure, experience, needs, and later on reorganizing it if necessary, depending upon the performance of students. The approach to organize the lecture content is based on the adaptive learning theory, incorporating Problem Based Learning (PBL) strategy. For on-line real time lectures and evaluations, we are using video conferencing tools (VCON Falcon IP, POLYCON view station FX) through IP, at a bandwidth of 1MB-DXX. Finally a good amount of emphasis is given on evaluation procedures that the tutor can adopt to evaluate the students, in order to provide them further course contents. Presently, the course syllabus and handouts on the web sites provided to the student are static in nature. Once distributed, these documents cannot be changed or modified, and also lack depth. When course materials are placed on the web, students can select a topic in the course outline and look at the description of a topic, and required reading assignments. Instructors can easily change schedules in these on-line documents and inform the students via e-mail. Students can also submit assignments, projects and take home exams electronically. A course home page is comprised of syllabus, assignments, projects and exams,readings and references, class presentation charts and student handouts. Students in a course are mostly assessed on the basis of the questions like why, how what, etc. In this way a student can be graded and ranked, which gives in turn provides the feedback to student for future improvements and challenges

Text Mining to Support Knowledge Discovery from Electronic Health Records

The use of electronic health records (EHRs) has grown rapidly in the last decade. The EHRs are no longer being used only for storing information for clinical purposes but the secondary use of the data in the healthcare research has increased rapidly as well. The data in EHRs are recorded in a structured manner as much as possible, however, many EHRs often also contain large amount of unstructured free‐text. The structured and unstructured clinical data presents several challenges to the researchers since the data are not primarily collected for research purposes. The issues related to structured data can be missing data, noise, and inconsistency. The unstructured free-text is even more challenging to use since they often have no fixed format and may vary from clinician to clinician and from database to database. Text and data mining techniques are increasingly being used to effectively and efficiently process large EHRs for research purposes. Most of the me

Erasmus MC at CLEF eHealth 2016: Concept recognition and coding in French texts

We participated in task 2 of the CLEF eHealth 2016 chal-lenge. Two subtasks were addressed: entity recognition and normalization in a corpus of French drug labels and Medline titles, and ICD-10 coding of French death certificates. For both subtasks we used a dictionary-based approach. For entity recognition and normalization, we used Peregrine, our open-source indexing engine, with a dictionary based on French terms in the Unified Medical Language System (UMLS) supplemented with English UMLS terms that were translated into French with automatic translators. For ICD-10 coding, we used the Solr text tagger, together with one of two ICD-10 terminologies derived from the task training ma-terial. To reduce the number of false-positive detections, we implemented several post-processing steps. On the challenge test set, our best system obtained F-scores of 0.702 and 0.651 fo

ContextD: An algorithm to identify contextual properties of medical terms in a dutch clinical corpus

Background: In order to extract meaningful information from electronic medical records, such as signs and symptoms, diagnoses, and treatments, it is important to take into account the contextual properties of the identified information: negation, temporality, and experiencer. Most work on automatic identification of these contextual properties has been done on English clinical text. This study presents ContextD, an adaptation of the English ConText algorithm to the Dutch language, and a Dutch clinical corpus. Results: The ContextD algorithm utilized 41 unique triggers to identify the contextual properties in the clinical corpus. For the negation property, the algorithm obtained an F-score from 87% to 93% for the different document types. For the experiencer property, the F-score was 99% to 100%. For the historical and hypothetical values of the temporality property, F-scores ranged from 26% to 54% and from 13% to 44%, respectively. Conclusions: The ContextD showed good performance in identifying negation and experiencer property values across all Dutch clinical document types. Accurate identification of the temporality property proved to be difficult and requires further work. The anonymized and annotated Dutch clinical corpus can serve as a useful resource for further algorithm development

Knowledge-based extraction of adverse drug events from biomedical text

Background: Many biomedical relation extraction systems are machine-learning based and have to be trained on large annotated corpora that are expensive and cumbersome to construct. We developed a knowledge-based relation extraction system that requires minimal training data, and applied the system for the extraction of adverse drug events from biomedical text. The system consists of a concept recognition module that identifies drugs and adverse effects in sentences, and a knowledg

Extraction of chemical-induced diseases using prior knowledge and textual information

We describe our approach to the chemical-disease relation (CDR) task in the BioCreative V challenge. The CDR task consists of two subtasks: Automatic disease-named entity recognition and normalization (DNER), and extraction of chemical-induced diseases (CIDs) from Medline abstracts. For the DNER subtask, we used our concept recognition tool Peregrine, in combination with several optimization steps. For the CID subtask, our system, which we named RELigator, was trained on a rich feature set, comprising features derived from a graph database containing prior knowledge about chemicals and diseases, and linguistic and statistical features derived from the abstracts in the CDR training corpus. We describe the systems that were developed and present evaluation results for both subtasks on the CDR test set. For DNER, our Peregrine system reached an F-score of 0.757. For CID, the system achieved an F-score of 0.526, which ranked second among 18 participating teams. Several post-challenge modifications of the systems resulted in substantially improved F-scores (0.828 for DNER and 0.602 for CID)

Evaluation of Resistance in Local Five Pakistani Chickpea Varieties against Callosobruchus Spps

During this research morphological data was recorded 1st before starting labe.  Work. In free choice test, the response of both species of Callosobruchus on candidate variety for oviposition was different. The adult emergence of both species of Callosobruchus on candidate varieties shows no significant difference. Both species of Callosobruchus in free choice test have no significant difference for percent   adult’s emergence on candidate varieties. Percent damage of both species of Callosobruchus on candidate varieties was different. Keywords: Chickpea, Cicer arietinum L, Phytic acid, legumes, beetle, weevi