A pattern-based knowledge editing system for building clinical Decision Support Systems

A clinical decision support tool to assist with the interpretation of the 12-lead electrocardiogram

This article reports the design and testing of a novel interactive method, abbreviated to ANALYSE (systemAtic aNALYsiS of Electrocardiography) to assist interpretation of 12-lead electrocardiogram. 15 participants interpreted a total of 150 12-lead electrocardiogram recordings randomly using a standard and this novel (ANALYSE) reporting format. The overall aggregated mean score attained using the standard format was 53% (range = 38-82%, standard deviation = 12). Conversely, the overall aggregated mean score attained using ANALYSE format was 75% (range = 55%-93%, standard deviation = 9). A total of 14/15 participants consistently scored higher when interpreting electrocardiograms using the ANALYSE format (range = 10% -45%). A significant difference between the aggregated marks scored using the ANALYSE format and the standard format was calculated (Wilcoxon Z Score = -3.2374 (df = 14), p < 0.01). This study demonstrates the clinical utility of a novel method (ANALYSE) to assist the learning of electrocardiogram interpretation and its association with enhanced diagnostic performance in novices.

Issues Associated With the Use of Semantic Web Technology in Knowledge Acquisition for Clinical Decision Support Systems: Systematic Review of the Literature

Background

Knowledge-based clinical decision support system (KB-CDSS) can be used to help practitioners make diagnostic decisions. KB-CDSS may use clinical knowledge obtained from a wide variety of sources to make decisions. However, knowledge acquisition is one of the well-known bottlenecks in KB-CDSSs, partly because of the enormous growth in health-related knowledge available and the difficulty in assessing the quality of this knowledge as well as identifying the “best” knowledge to use. This bottleneck not only means that lower-quality knowledge is being used, but also that KB-CDSSs are difficult to develop for areas where expert knowledge may be limited or unavailable. Recent methods have been developed by utilizing Semantic Web (SW) technologies in order to automatically discover relevant knowledge from knowledge sources.

Objective

The two main objectives of this study were to (1) identify and categorize knowledge acquisition issues that have been addressed through using SW technologies and (2) highlight the role of SW for acquiring knowledge used in the KB-CDSS.

Methods

We conducted a systematic review of the recent work related to knowledge acquisition MeM for clinical decision support systems published in scientific journals. In this regard, we used the keyword search technique to extract relevant papers.

Results

The retrieved papers were categorized based on two main issues: (1) format and data heterogeneity and (2) lack of semantic analysis. Most existing approaches will be discussed under these categories. A total of 27 papers were reviewed in this study.

Conclusions

The potential for using SW technology in KB-CDSS has only been considered to a minor extent so far despite its promise. This review identifies some questions and issues regarding use of SW technology for extracting relevant knowledge for a KB-CDSS.

Aggregate features in multisample classification problems

This paper evaluates the classification of multisample problems, such as electromyographic (EMG) data, by making aggregate features available to a per-sample classifier. It is found that the accuracy of this approach is superior to that of traditional methods such as majority vote for this problem. The classification improvements of this method, in conjunction with a confidence measure expressing the per-sample probability of classification failure (i.e., a hazard function) is described and measured. Results are expected to be of interest in clinical decision support system development.