Comparing computer-interpretable guideline models: a case-study approach.
By Mor Peleg, S. Tu, J. Bury, P. Ciccarese, J. Fox, R. Greenes, Richard W. Hall, Peter D. Johnson, Neill Jones, Anand Kumar and 5 more. 2003
632 citations recorded by the source corpus.
Subfields: Health Information Management, Management Information Systems, Public Health, Environmental and Occupational Health
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In Health Information Management
In Management Information Systems
In Public Health, Environmental and Occupational Health
Citations in the corpus
- Distilling Knowledge from Deep Networks with Applications to Healthcare Domain (2015, neutral): “A good interpretable model is shown to result in faster adoptability among the clinical staff and results in better quality of patient care [35, 24].”
- IT support for healthcare processes - premises, challenges, perspectives (2007, neutral): “Recent surveys have compared these approaches [18,55,19].”
- Computer-interpretable clinical guidelines: A methodological review (2013, neutral): “They include document-centric models, decision trees and probabilistic models, and ‘‘Task-Network Models’’ (TNMs) [2], which represent guideline knowledge in hierarchical structures containing networks of clinical actions and decisions that unfold over time.”
- Interpretable Deep Models for ICU Outcome Prediction (2016, methodology): “An interpretable predictive model is shown to result in faster adoptability among clinical staff and better quality of patient care 12,13 .”
- GLIF3: a representation format for sharable computer-interpretable clinical practice guidelines (2004, background): “In an analysis of guideline modeling languages [27], it was noted that differences in application focus have given rise to a variety of features in the various modeling languages.”