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S. Chakravarty, Yuval Shahar (2001)
Acquisition and Analysis of Repeating Patterns in Time-oriented Clinical DataMethods of Information in Medicine, 40
(2007)
Journal of the American Medical Informatics Association
M. Stacey, C. McGregor (2007)
Temporal abstraction in intelligent clinical data analysis: A surveyArtificial intelligence in medicine, 39 1
J. Martin, B. Rinehart, W. May, E. Magann, D. Terrone, P. Blake (1999)
The spectrum of severe preeclampsia: comparative analysis by HELLP (hemolysis, elevated liver enzyme levels, and low platelet count) syndrome classification.American journal of obstetrics and gynecology, 180 6 Pt 1
Drools-Home
Combi Carlo, F. Pinciroli, G. Pozzi (1995)
Managing Different Time Granularities of Clinical Information by an Interval-based Temporal Data ModelMethods of Information in Medicine, 34
Yuval Shahar (2000)
Dimension of Time in Illness: An Objective ViewAnnals of Internal Medicine, 132
Yuval Shahar, M. Musen (1996)
Knowledge-based temporal abstraction in clinical domainsArtificial intelligence in medicine, 8 3
J. Ridgeway, Darin Weyrich, T. Benedetti (2003)
Fetal Heart Rate Changes Associated With Uterine RuptureObstetrics & Gynecology, 103
Yuval Shahar, Cleve Cheng (1998)
Model-based visualization of temporal abstractionsProceedings. Fifth International Workshop on Temporal Representation and Reasoning (Cat. No.98EX157)
B. Sibai (1990)
The HELLP syndrome (hemolysis, elevated liver enzymes, and low platelets): much ado about nothing?American journal of obstetrics and gynecology, 162 2
M. Murray, F. Smith, Joanne Fox, E. Teal, J. Kesterson, Troy Stiffler, Roberta Ambuehl, Jane Wang, Maria Dibble, Dennis Benge, Leonard Betley, W. Tierney, C. McDonald (2003)
Case Report: Structure, Functions, and Activities of a Research Support Informatics SectionJournal of the American Medical Informatics Association : JAMIA, 10 4
The Protege Ontology Editor and Knowledge Acquisition System
R. Jenders, W. Sujansky, C. Broverman, Michael Chadwick (1997)
Towards improved knowledge sharing: assessment of the HL7 Reference Information Model to support medical logic module queriesProceedings : a conference of the American Medical Informatics Association. AMIA Fall Symposium
R. Elmasri, S. Navathe (1989)
Fundamentals of Database Systems
G. Carrault, M. Cordier, R. Quiniou, Feng Wang (2003)
Temporal abstraction and inductive logic programming for arrhythmia recognition from electrocardiogramsArtificial intelligence in medicine, 28 3
Chang-Shing Perng, D. Parker (2000)
Temporal Coupling Verification in Time Series DatabasesJournal of Intelligent Information Systems, 15
M. O'Connor, W. Grosso, S. Tu, M. Musen (2001)
RASTA: A Distributed Temporal Abstraction System to Facilitate Knowledge-Driven Monitoring of Clinical DatabasesStudies in health technology and informatics, 84 Pt 1
J. Weydert, Newell Nobbs, Ronald Feld, J. Kemp (2005)
A simple, focused, computerized query to detect overutilization of laboratory tests.Archives of pathology & laboratory medicine, 129 9
Amar Das, M. Musen (2002)
SYNCHRONUS: a reusable software module for temporal integrationProceedings. AMIA Symposium
M. O'Connor, R. Shankar, Amar Das (2006)
An Ontology-Driven Mediator for Querying Time-Oriented Biomedical Data19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06)
R. Bellazzi, C. Larizza, P. Magni, S. Montani, M. Stefanelli (2000)
Intelligent analysis of clinical time series: an application in the diabetes mellitus domainArtificial intelligence in medicine, 20 1
Javier Fonseca, F. Méndez, Claudia Cataño, F. Arias (2005)
Dexamethasone treatment does not improve the outcome of women with HELLP syndrome: a double-blind, placebo-controlled, randomized clinical trial.American journal of obstetrics and gynecology, 193 5
B. Hayes-Roth, S. Uckun, Jan Larsson, David Gaba, Juliana Barr, Jane Chien (1994)
Guardian: A Prototype Intelligent Agent for Intensive-Care Monitoring
E. Mulligen, T. Timmeis, J. Bemmel (1993)
User Evaluation of an Integrated Medical Workstation for Clinical Data AnalysisMethods of Information in Medicine, 32
Harry Karadimas, C. Chailloleau, F. Hemery, Julien Simonnet, É. Lepage (2002)
Application of Information Technology: Arden/J: An Architecture for MLM Execution on the Java PlatformJournal of the American Medical Informatics Association : JAMIA, 9 4
(2007)
Available at: http://jmatlink.sourceforge. net
Yuval Shahar (1996)
Dynamic temporal interpretation contexts for temporal abstractionAnnals of Mathematics and Artificial Intelligence, 22
R. Snodgrass, Michael Böhlen, Christian Jensen, A. Steiner (1997)
Transitioning Temporal Support in TSQL2 to SQL3
D. Nigrin, I. Kohane (2000)
Application of Information Technology: Temporal Expressiveness in Querying a Time-stamp - based Clinical DatabaseJournal of the American Medical Informatics Association : JAMIA, 7 2
J. Schubart, J. Einbinder (2000)
Evaluation of a data warehouse in an academic health sciences centerInternational journal of medical informatics, 60 3
I. Haimowitz, I. Kohane (1996)
Managing temporal worlds for medical trend diagnosisArtificial intelligence in medicine, 8 3
C. Forgy (1991)
Rete: a fast algorithm for the many pattern/many object pattern match problemExpert Systems
(2004)
Web-based Implementation of a Modular General Purpose Temporal Abstraction Framework for Pattern Identification in Clinical Laboratory Data
J. Fackler, I. Kohane (1994)
Monitor-driven data visualization: SmartDisplay.Proceedings. Symposium on Computer Applications in Medical Care
C. Safran, M. Bloomrosen, W. Hammond, S. Labkoff, S. Markel-Fox, P. Tang, D. Detmer (2007)
White Paper: Toward a National Framework for the Secondary Use of Health Data: An American Medical Informatics Association White PaperJournal of the American Medical Informatics Association : JAMIA, 14 1
M. O'Connor, S. Tu, M. Musen (2002)
The Chronus II temporal database mediatorProceedings. AMIA Symposium
M. Kahn, Lawrence Fagan, L. Sheiner (1991)
Combining Physiologic Models and Symbolic Methods to Interpret Time-Varying Patient Data*Methods of Information in Medicine, 30
James Allen (1983)
Maintaining knowledge about temporal intervalsCommun. ACM, 26
Integrating Mathematica and Java
J. Augusto (2005)
Temporal reasoning for decision support in medicineArtificial intelligence in medicine, 33 1
Charles Safran, Christopher Chute (1995)
Exploration and exploitation of clinical databases.International journal of bio-medical computing, 39 1
K. Adlassnig, Combi Carlo, Amar Das, E. Keravnou, G. Pozzi (2006)
Temporal representation and reasoning in medicine: Research directions and challengesArtificial intelligence in medicine, 38 2
G. Goos, J. Hartmanis, S. Sripada, J. Leeuwen, S. Jajodia (1998)
Temporal Databases: Research and Practice
S. Martins, Yuval Shahar, M. Galperin-Aizenberg, Herbert Kaizer, Dina Goren-Bar, Deborah McNaughton, L. Basso, M. Goldstein (2004)
Evaluation of KNAVE-II: a Tool for Intelligent Query and Exploration of Patient DataStudies in health technology and informatics, 107 Pt 1
Saori Kawasaki, T. Ho, T. Nguyen (2003)
Abstraction of Long-Term Changed Tests in Mining Hepatitis Data
C. Larizza, A. Moglia, M. Stefanelli (1992)
M-HTP: A system for monitoring heart transplant patientsArtif. Intell. Medicine, 4
R. Dechter, Itay Meiri, J. Pearl (1989)
Temporal Constraint Networks
W. Gall, G. Duftschmid, W. Dorda (2001)
Moving time window aggregates over patient historiesInternational journal of medical informatics, 63 3
Yuval Shahar (1997)
A Framework for Knowledge-Based Temporal AbstractionArtif. Intell., 90
W. Dorda, W. Gall, G. Duftschmid (2002)
Clinical Data Retrieval: 25 Years of Temporal Query Management at the University of Vienna Medical SchoolMethods of Information in Medicine, 41
B. Sibai, J. Barton (2005)
Dexamethasone to improve maternal outcome in women with hemolysis, elevated liver enzymes, and low platelets syndrome.American journal of obstetrics and gynecology, 193 5
(2007)
Available at: http:// jruby.codehaus.org. Accessed Mar 5
(2007)
Jepp—Java Embedded Python Available at: http:// jepp.sourceforge.net
(2007)
The Jython Project Available at: http://www.jython.org
Yuval Shahar, Dina Goren-Bar, David Boaz, Gil Tahan (2006)
Distributed, intelligent, interactive visualization and exploration of time-oriented clinical data and their abstractionsArtificial intelligence in medicine, 38 2
A. Bui, GREGORY WEINGER, SUSAN BARRETTA, J. Dionisio, H. Kangarloo (2002)
An XML Gateway to Patient Data for Medical Research ApplicationsAnnals of the New York Academy of Sciences, 980
(2007)
Java Toolkit: J/Link: Integrating Mathematica and Java. Available at: http://www.wolfram.com/ solutions/mathlink/jlink
C. Forgy (1982)
Rete: A Fast Algorithm for the Many Patterns/Many Objects Match ProblemArtif. Intell., 19
E. Sherman, G. Hripcsak, J. Starren, R. Jenders, P. Clayton (1995)
Using intermediate states to improve the ability of the Arden Syntax to implement care plans and reuse knowledge.Proceedings. Symposium on Computer Applications in Medical Care
John Nguyen, Yuval Shahar, S. Tu, Amar Das, M. Musen (1997)
A temporal database mediator for protocol-based decision supportProceedings : a conference of the American Medical Informatics Association. AMIA Fall Symposium
AbstractObjective: To specify and identify disease and patient care processes represented by temporal patterns in clinical events and observations, and retrieve patient populations containing those patterns from clinical data repositories, in support of clinical research, outcomes studies, and quality assurance.Design: A data processing method called PROTEMPA (Process-oriented Temporal Analysis) was developed for defining and detecting clinically relevant temporal and mathematical patterns in retrospective data. PROTEMPA provides for portability across data sources, “pluggable” data processing environments, and the creation of libraries of pattern definitions and data processing algorithms.Measurements: A proof-of-concept implementation of PROTEMPA in Java was evaluated against standard SQL queries for its ability to identify patients from a large clinical data repository who show the features of HELLP syndrome, and categorize those patients by disease severity and progression based on time sequence characteristics in their clinical laboratory test results. Results were verified by manual case review.Results: The proof-of-concept implementation was more accurate than SQL in identifying patients with HELLP and correctly assigned severity and disease progression categories, which was not possible using SQL only.Conclusions: PROTEMPA supports the identification and categorization of patients with complex disease based on the characteristics of and relationships between time sequences in multiple data types. Identifying patient populations who share these types of patterns may be useful when patient features of interest do not have standard codes, are poorly-expressed in coding schemes, may be inaccurately or incompletely coded, or are not represented explicitly as data values.
Journal of the American Medical Informatics Association – Oxford University Press
Published: Sep 1, 2007
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