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- W289076991 abstract "We are developing an expert system to interpret self-recorded, time-ordered diabetes patient data. One essential task in this program is the detection of significant changes in insulin therapy. This detection problem is difficult because patients usually make small adjustments in their insulin therapy based on the value of recent blood glucose measurements. Thus, we need to recognize when a systematic change in insulin therapy has occurred against a background of constantly changing insulin injections.We describe three different methods we used to detect temporal changes in insulin therapy. With strictly domain-independent methods, we were unable to derive clinically meaningful intervals of insulin therapies. Using clinical heuristics to detect relevant changes, we could not discriminate small changes and could not adapt to variations seen in a wide range of patient records. By combining domain-dependent clinical heuristics to encode relevance and a domain-independent change-detection algorithm to control robustness we now are able to detect clinically meaningful changes in insulin therapy over many clinical settings." @default.
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- W289076991 date "1990-11-07" @default.
- W289076991 modified "2023-09-28" @default.
- W289076991 title "Knowledge-Based Systems. Methods for Developing and Evaluating Expert Systems: Automated Interpretation of Diabetes Patient Data: Detecting Temporal Changes in Insulin Therapy" @default.
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