Context-Sensitive Temporal Abstraction of Clinical Data

1997 ◽  
pp. 37-59
Author(s):  
Yuval Shahar
Keyword(s):  
Clinical Data ◽  
Temporal Abstraction ◽  
Context Sensitive

An Ontology-based Mediator of Clinical Information for Decision Support Systems

2008 ◽  
Vol 47 (06) ◽  
pp. 549-559 ◽  
Author(s):  
K. Ohe ◽  
Y. Kawazoe
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Data ◽  
Support System ◽  
Adverse Drug Events ◽  
Clinical Information ◽  
Temporal Information ◽  
Temporal Abstraction ◽  
Alert System ◽  
Data Repositories

Summary Objective: We have been developing a decision support system that uses electronic clinical data and provides alerts to clinicians. However, the inference rules for such a system are difficult to write in terms of representing domain concepts and temporal reasoning. To address this problem, we have developed an ontologybased mediator of clinical information for the decision support system. Methods: Our approach consists of three steps: 1) development of an ontology-based mediator that represents domain concepts and temporal information; 2) mapping of clinical data to corresponding concepts in the mediator; 3) temporal abstraction that creates high-level, interval-based concepts from time-stamped clinical data. As a result, we can write a concept-based rule expression that is available for use in domain concepts and interval-based temporal information. The proposed approach was applied to a prototype of clinical alert system, and the rules for adverse drug events were executed on data gathered over a 3-month period. Results: The system generated 615 alerts. 346 cases (56%) were considered appropriate and 269 cases (44%) were inappropriate. Of the false alerts, 192 cases were due to data inaccuracy and 77 cases were due to insufficiency of the temporal abstraction. Conclusion: Our approach enabled to represent a concept-based rule expression that was available for the prototype of a clinical alert system. We believe our approach will contribute to narrow the gaps of information model between domain concepts and clinical data repositories.

Acquisition and Analysis of Repeating Patterns in Time-oriented Clinical Data

2001 ◽  
Vol 40 (05) ◽  
pp. 410-420 ◽  
Author(s):  
S. Chakravarty ◽  
Y. Shahar
Keyword(s):  
Knowledge Acquisition ◽  
Clinical Data ◽  
Medical Center ◽  
Temporal Patterns ◽  
Experimental Information ◽  
Temporal Abstraction ◽  
Levels Of Abstraction ◽  
Acquisition Process ◽  
Clinical Databases ◽  
Almost All

Summary Objectives: (1) Creation of an expressive language for specification of temporal patterns in clinical domains, (2) Development of a graphical knowledge-acquisition tool allowing expert physicians to define meaningful domain-specific patterns, (3) Implementation of an interpreter capable of detecting such patterns in clinical databases, and (4) Evaluation of the tools in the domains of diabetes and oncology. Methods: We describe a constraint-based language, named CAPSUL, for specification of temporal patterns. We implemented a knowledge-acquisition tool and a temporal-pattern interpreter within Résumé, a larger temporal-abstraction architecture. We evaluated the knowledge-acquisition process with the help of domain experts. In collaboration with the Rush Presbyterian/St. Luke’s Medical Center, we analyzed data of bone-marrow transplantation patients. The expert compared the detected patterns to a manual inspection of the data, with the help of an experimental information-visualization tool we are developing in a related project. Results: The CAPSUL language was expressive enough during the knowledge-acquisition process to capture almost all of the patterns that the experts found useful. The patterns detected in the data by the pattern interpreter were all verified as correct. Completeness (whether all correct patterns were found) was difficult to assess, due to the size of the database. Conclusions: The CAPSUL language enables medical experts to express temporal patterns involving multiple levels of abstraction of clinical data. The ability to reuse both domain-patterns and abstract constraints seems highly useful. The Résumé interpreter, augmented by the CAPSUL semantics, finds the complex patterns within a clinical time-oriented database in a sound fashion.

Context-Sensitive Clinical Data Integration

2006 ◽  
pp. 387-398 ◽  
Author(s):  
James F. Terwilliger ◽  
Lois M. L. Delcambre ◽  
Judith Logan
Keyword(s):  
Data Integration ◽  
Clinical Data ◽  
Context Sensitive

Abstract # P-13: Characteristics of Korean T2DM Patients with Diabetic Retinopathy and Macular Edema: A Study Based on a Standardized Clinical Data

2016 ◽  
Vol 22 ◽  
pp. 19-20
Author(s):  
Sang Youl Rhee ◽  
Sejeong Park ◽  
Ki Young Kim ◽  
Suk Chon ◽  
Seung-Young Yu ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Macular Edema ◽  
Clinical Data

Neuropsychology in the Real World

2014 ◽  
Vol 25 (4) ◽  
pp. 233-238 ◽  
Author(s):  
Martin Peper ◽  
Simone N. Loeffler
Keyword(s):  
Neuropsychological Assessment ◽  
Real World ◽  
Ecological Validity ◽  
Real Life ◽  
Emotional States ◽  
Context Sensitive ◽  
Traditional Assessment ◽  
Life Data ◽  
Real Life Data ◽  
Assessment And Treatment

Current ambulatory technologies are highly relevant for neuropsychological assessment and treatment as they provide a gateway to real life data. Ambulatory assessment of cognitive complaints, skills and emotional states in natural contexts provides information that has a greater ecological validity than traditional assessment approaches. This issue presents an overview of current technological and methodological innovations, opportunities, problems and limitations of these methods designed for the context-sensitive measurement of cognitive, emotional and behavioral function. The usefulness of selected ambulatory approaches is demonstrated and their relevance for an ecologically valid neuropsychology is highlighted.

Lode for Clinical Data

1957 ◽  
Vol 2 (1) ◽  
pp. 14-15
Author(s):  
ALBERT BANDURA
Keyword(s):  
Clinical Data

Glare and age: Acquisition of a clinical data base for aircrew standards.

1990 ◽  
Author(s):  
Joseph M. Harrison ◽  
Peng Chen ◽  
Charles S. Ballentine ◽  
J. Terry Yates
Keyword(s):  
Data Base ◽  
Clinical Data
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