Validating the INTERPRETOR Software Architecture for the Interpretation of Large and Noisy Data Sets

In this chapter, the authors validate INTERPRETOR software architecture as a dataflow model of com- putation for filtering, abstracting, and interpreting large and noisy datasets with two detailed empirical studies from the authors’ former research endeavours. Also discussed are five further recent and distinct systems that can be tailored or adapted to use the software architecture. The detailed case studies pre- sented are from two disparate domains that include intensive care unit data and building sensor data. By performing pattern mining on five further systems in the way the authors have suggested herein, they argue that INTERPRETOR software architecture has been validated

Do students studying Java perform better in short answer questions or computational questions? - A Case Study

There are numerous ways to assess students in a written Java test at University level. In this paper we try to determine whether students perform better in short answer type questions which test lower level cognitive skills or computational type questions which test higher level cognitive skills. Our case study is a Time Constrained Assessment for a level 5 module at Northampton University

Temporal Expert System Approach to the Interpretation of ICU Cardiovascular Data

Abstract Intensive Care depends on sophisticated life support technology. Effective management of devicesupported patients is complex, involving the interpretation of many variables, comparative evaluation of numerous therapeutic options, and control of various patient-management parameters. Raw data, when taken literally, can lead to the wrong interpretation of the patient state. We propose a system which processes raw data as it arrives for intelligent alarming and retrospectively for summarisation and state assessment, using a temporal expert system which incorporates both associational and model-based reasoning

Expertise and the interpretation of computerized physiological data: implications for the design of computerized monitoring in neonatal intensive care

This paper presents the outcomes from a cognitive engineering project addressing the design problems of computerized monitoring in neonatal intensive care. Cognitive engineering is viewed, in this project, as a symbiosis between cognitive science and design practice. A range of methodologies has been used: interviews with neonatal staff, ward observations and experimental techniques. The results of these investigations are reported, focusing specifically on the differences between junior and senior physicians in their interpretation of monitored physiological data. It was found that the senior doctors made better use of the different knowledge sources available than the junior doctors. The senior doctors were able to identify more relevant physiological patterns and generated more and better inferences than did their junior colleagues. Expertise differences are discussed in the context of previous psychological research in medical expertise. Finally, the paper discusses the potential utility of these outcomes to inform the design of computerized decision support in neonatal intensive care