A temporal analysis of QMR.

Section of Medical Informatics, B50A Lothrop Hall, 190 Lothrop Street, University of Pittsburgh, Pittsburgh, PA, USA.
Journal of the American Medical Informatics Association (Impact Factor: 3.5). 01/1996; 3(1):79-91. DOI: 10.1136/jamia.1996.96342651
Source: PubMed


To understand better the trade-offs of not incorporating explicit time in Quick Medical Reference (QMR), a diagnostic system in the domain of general internal medicine, along the dimensions of expressive power and diagnostic accuracy.
The study was conducted in two phases. Phase I was a descriptive analysis of the temporal abstractions incorporated in QMR's terms. Phase II was a pseudo-prospective controlled experiment, measuring the effect of history and physical examination temporal content on the diagnostic accuracy of QMR.
For each QMR finding that would fit our operational definition of temporal finding, several parameters describing the temporal nature of the finding were assessed, the most important ones being: temporal primitives, time units, temporal uncertainty, processes, and patterns. The history, physical examination, and initial laboratory results of 105 consecutive patients admitted to the Pittsburgh University Presbyterian Hospital were analyzed for temporal content and factors that could potentially influence diagnostic accuracy (these included: rareness of primary diagnosis, case length, uncertainty, spatial/causal information, and multiple diseases).
776 findings were identified as temporal. The authors developed an ontology describing the terms utilized by QMR developers to express temporal knowledge. The authors classified the temporal abstractions found in QMR in 116 temporal types, 11 temporal templates, and a temporal hierarchy. The odds of QMR's making a correct diagnosis in high temporal complexity cases is 0.7 the odds when the temporal complexity is lower, but this result is not statistically significant (95% confidence interval = 0.27-1.83).
QMR contains extensive implicit time modeling. These results support the conclusion that the abstracted encoding of time in the medical knowledge of QMR does not induce a diagnostic performance penalty.

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Available from: Bruce G Buchanan, Jan 24, 2014
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    ABSTRACT: The utilization of the appropriate level of temporal abstraction is an important aspect of time modeling. We discuss some aspects of the relation of temporal abstraction to important knowledge engineering parameters such as model correctness, ease of model specification, knowledge availability, query completeness, inference tractability, and semantic clarity. We propose that versatile and efficient time-modeling formalisms should encompass ways to represent and reason at more than one level of abstraction, and we discuss such a hybrid formalism. Although many research efforts have concentrated on the automation of specific temporal abstractions, much research needs to be done in understanding and developing provably optimal abstractions. We provide an initial framework for studying this problem in a manner that is independent of the particular problem domain and knowledge representation, and suggest several research challenges that appear worth pursuing.
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    ABSTRACT: Background: Disease and patient care processes often create characteristic mathematical and temporal patterns in time-stamped clinical events and observations, but existing medical record systems have a limited ability to recognize or visualize these patterns.System Design: This dissertation introduces the process-oriented approach to clinical data analysis and visualization. This approach aims to support specifying, detecting, and visualizing mathematical and temporal patterns in time-stamped patient data for a broad range of clinical tasks. It has two components: a pattern specification and detection strategy called PROTEMPA (Process-oriented Temporal Analysis); and a pattern visualization strategy called TPOD (Temporal Process-oriented Display).Evaluation: A study in the clinical research domain evaluated PROTEMPA's ability to identify and categorize patients based on diagnosis, disease severity, and disease progression by scanning for patterns in clinical laboratory results. A cognitive study in the patient care domain evaluated PROTEMPA and TPOD's ability to help physicians review cases and make decisions using case presentation software that displays laboratory results in either a TPOD-based display or a standard laboratory display.Results: PROTEMPA successfully identified laboratory data patterns in both domains. TPOD successfully visualized these patterns in the patient care domain. In the patient care study, subjects obtained more clinical concepts from the TPOD-based display, but TPOD had no effect on decision-making speed or quality. Subjects were split on which laboratory display they preferred, but expressed a desire to gain more familiarity with the TPOD-based display. Subjects reviewed data in the standard laboratory display for a variety of purposes, and interacted with the display in a complex fashion.Conclusions: The process-oriented approach successfully recognized and visualized data patterns for two distinct clinical tasks. In clinical research, this approach may provide significant advantages over existing methods of data retrieval. In patient care, comparative evaluation of novel data displays in context provides insights into physicians' preferences, the process of clinical decision-making by physicians, and display usability. TPOD's influence on concept acquisition is promising, but further research is needed regarding physicians' use of laboratory data for results review in order to determine how a process-oriented display might be deployed most beneficially.
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