CardioML: integrating personal cardiac information for ubiquous diagnosis and analysis

The latest medical diagnosis devices enable the performance of e-diagnosis making the access to these services easier, faster and available in remote areas. However this imposes new communications and data interchange challenges. In this paper a new XML based format for storing cardiac signals and related information is presented. The proposed structure encompasses data acquisition devices, patient information, data description, pathological diagnosis and waveform annotation. When compared with similar purpose formats several advantages arise. Besides the full integrated data model it may also be noted the available geographical references for e-diagnosis, the multi stream data description, the ability to handle several simultaneous devices, the possibility of independent waveform annotation and a HL7 compliant structure for common contents. These features represent an enhanced integration with existent systems and an improved flexibility for cardiac data representation.

Seqins: a sequencing tool for educational resources

The teaching-learning process is increasingly focused on the combination of the paradigms “learning by viewing” and “learning by doing.” In this context, educational resources, either expository or evaluative, play a pivotal role. Both types of resources are interdependent and their sequencing would create a richer educational experience to the end user. However, there is a lack of tools that support sequencing essentially due to the fact that existing specifications are complex. The Seqins is a sequencing tool of digital resources that has a fairly simple sequencing model. The tool communicates through the IMS LTI specification with a plethora of e-learning systems such as learning management systems, repositories, authoring and evaluation systems. In order to validate Seqins we integrate it in an e-learning Ensemble framework instance for the computer programming learning.

Sequencing educational resources with Seqins

Existing adaptive educational hypermedia systems have been using learning resources sequencing approaches in order to enrich the learning experience. In this context, educational resources, either expository or evaluative, play a central role. However, there is a lack of tools that support sequencing essentially due to the fact that existing specifications are complex. This paper presents Seqins as a sequencing tool of digital educational resources. Seqins includes a simple and flexible sequencing model that will foster heterogeneous students to learn at different rhythms. The tool communicates through the IMS Learning Tools Interoperability specification with a plethora of e-learning systems such as learning management systems, repositories, authoring and automatic evaluation systems. In order to validate Seqins we integrate it in an e-learning Ensemble framework instance for the computer programming learning domain.

Using data mining techniques to support breast cancer diagnosis

More than ever, there is an increase of the number of decision support methods and computer aided diagnostic systems applied to various areas of medicine. In breast cancer research, many works have been done in order to reduce false-positives when used as a double reading method. In this study, we aimed to present a set of data mining techniques that were applied to approach a decision support system in the area of breast cancer diagnosis. This method is geared to assist clinical practice in identifying mammographic findings such as microcalcifications, masses and even normal tissues, in order to avoid misdiagnosis. In this work a reliable database was used, with 410 images from about 115 patients, containing previous reviews performed by radiologists as microcalcifications, masses and also normal tissue findings. Throughout this work, two feature extraction techniques were used: the gray level co-occurrence matrix and the gray level run length matrix. For classification purposes, we considered various scenarios according to different distinct patterns of injuries and several classifiers in order to distinguish the best performance in each case described. The many classifiers used were Naïve Bayes, Support Vector Machines, k-nearest Neighbors and Decision Trees (J48 and Random Forests). The results in distinguishing mammographic findings revealed great percentages of PPV and very good accuracy values. Furthermore, it also presented other related results of classification of breast density and BI-RADS® scale. The best predictive method found for all tested groups was the Random Forest classifier, and the best performance has been achieved through the distinction of microcalcifications. The conclusions based on the several tested scenarios represent a new perspective in breast cancer diagnosis using data mining techniques.