Clinical informatics in undergraduate teaching of health informatics.

We are reporting on a recent experience with Health Informatics (HI) teaching at undergraduate degree level to an audience of HI and Pharmacy students. The important insight is that effective teaching of clinical informatics must involve highly interactive, applied components in addition to the traditional theoretical material. This is in agreement with general literature underlining the importance of simulations and role playing in teaching and is well supported by our student evaluation results.

Unsupervised grammar induction and similarity retrieval in medical language processing using the Deterministic Dynamic Associative Memory (DDAM) model.

This paper is an overview of unsupervised grammar induction and similarity retrieval, two fundamental information processing functions of importance to medical language processing applications and to the construction of intelligent medical information systems. Existing literature with a focus on text segmentation tasks is reviewed. The review includes a comparison of existing approaches and reveals the longstanding interest in these traditionally distinct topics despite the significant computational challenges that characterizes them.

An economic evaluation of a telehealth network in British Columbia.

We carried out an economic evaluation of the northernmost five sites of the British Columbia telehealth network. The videoconferencing network links health-care facilities in 12 communities with Vancouver, for clinical consultations, administrative meetings and educational sessions. The economic evaluation was based on the netcost criterion (i.

Case-based medical informatics.

Background: The "applied" nature distinguishes applied sciences from theoretical sciences. To emphasize this distinction, we begin with a general, meta-level overview of the scientific endeavor. We introduce the knowledge spectrum and four interconnected modalities of knowledge.

Cluster analysis of Wisconsin Breast Cancer dataset using self-organizing maps.

This work deals with multidimensional data analysis, precisely cluster analysis applied to a very well known dataset, the Wisconsin Breast Cancer dataset. After the introduction of the topics of the paper the cluster analysis concept is shortly explained and different methods of cluster analysis are compared. Further, the Kohonen model of self-organizing maps is briefly described together with an example and with explanations of how the cluster analysis can be performed using the maps.

A new method for matching a document to potential users' information needs.

This paper explores approach to finding out how the information needs that a document can address can be captured. This is important in order to improve indexing strategies applied to document collections. We propose and implemented a cognitive science approach, the "jeopardy game" method of evaluation combined with "think aloud" analysis.