Querying temporal clinical databases on granular trends

This paper focuses on the identification of temporal trends involving different granularities in clinical databases, where data are temporal in nature: for example, while follow-up visit data are usually stored at the granularity of working days, queries on these data could require to consider trends either at the granularity of months ("find patients who had an increase of systolic blood pressure within a single month") or at the granularity of weeks ("find patients who had steady states of diastolic blood pressure for more than 3 weeks"). Representing and reasoning properly on temporal clinical data at different granularities are important both to guarantee the efficacy and the quality of care processes and to detect emergency situations. Temporal sequences of data acquired during a care process provide a significant source of information not only to search for a particular value or an event at a specific time, but also to detect some clinically-relevant patterns for temporal data. We propose a general framework for the description and management of temporal trends by considering specific temporal features with respect to the chosen time granularity. Temporal aspects of data are considered within temporal relational databases, first formally by using a temporal extension of the relational calculus, and then by showing how to map these relational expressions to plain SQL queries. Throughout the paper we consider the clinical domain of hemodialysis, where several parameters are periodically sampled during every session.

[1]  Carlo Combi,et al.  Formal and conceptual modeling of spatio-temporal granularities , 2009, IDEAS '09.

[2]  Matteo Golfarelli Open Source BI Platforms: A Functional and Architectural Comparison , 2009, DaWaK.

[3]  Yuval Shahar,et al.  Distributed, intelligent, interactive visualization and exploration of time-oriented clinical data and their abstractions , 2006, Artif. Intell. Medicine.

[4]  Yuval Shahar,et al.  A Framework for Knowledge-Based Temporal Abstraction , 1997, Artif. Intell..

[5]  Richard T. Snodgrass,et al.  The TSQL2 Temporal Query Language , 1995 .

[6]  Gunter Saake,et al.  Logics for databases and information systems , 1998 .

[7]  Elpida T. Keravnou,et al.  Temporal representation and reasoning in medicine: Research directions and challenges , 2006, Artif. Intell. Medicine.

[8]  Yuval Shahar,et al.  Evaluation of an architecture for intelligent query and exploration of time-oriented clinical data , 2008, Artif. Intell. Medicine.

[9]  Silvia Miksch,et al.  Abstracting Steady Qualitative Descriptions over Time from Noisy, High-Frequency Data , 1999, AIMDM.

[10]  Jeffrey D. Ullman,et al.  Principles of Database and Knowledge-Base Systems, Volume II , 1988, Principles of computer science series.

[11]  Yuval Shahar,et al.  Temporal Information Systems in Medicine , 2010 .

[12]  Ramez Elmasri,et al.  An EER-Based Conceptual Model and Query Language for Time-Series Data , 1998, ER.

[13]  Christian S. Jensen,et al.  Temporal Query Languages , 2009, Encyclopedia of Database Systems.

[14]  Ling Liu,et al.  Encyclopedia of Database Systems , 2009, Encyclopedia of Database Systems.

[15]  Michael H. Böhlen Temporal Query Processing , 2009, Encyclopedia of Database Systems.

[16]  Jeffrey D. Uuman Principles of database and knowledge- base systems , 1989 .

[17]  Inderpal Singh Mumick,et al.  The Stanford Data Warehousing Project , 1995 .

[18]  Richard T. Snodgrass,et al.  Coalescing in Temporal Databases , 1996, VLDB.

[19]  Yuval Shahar,et al.  Integration of Temporal Reasoning and Temporal-Data Maintenance into a Reusable Database Mediator to Answer Abstract, Time-Oriented Queries: The Tzolkin System , 1999, Journal of Intelligent Information Systems.

[20]  Riccardo Bellazzi,et al.  Temporal data mining for the quality assessment of hemodialysis services , 2005, Artif. Intell. Medicine.

[21]  Jim Melton,et al.  SQL standardization: the next steps , 2000, SGMD.

[22]  Angelo Montanari,et al.  The t4sql temporal query language , 2007, CIKM '07.

[23]  X.S. Wang,et al.  Discovering Frequent Event Patterns with Multiple Granularities in Time Sequences , 1998, IEEE Trans. Knowl. Data Eng..

[24]  Carlo Combi Temporal Object-Oriented Databases , 2009, Encyclopedia of Database Systems.

[25]  Carolyn McGregor,et al.  Temporal abstraction in intelligent clinical data analysis: A survey , 2007, Artif. Intell. Medicine.

[26]  Alberto Riva,et al.  Interpreting Longitudinal Data through Temporal Abstractions: An Application to Diabetic Patients Monitoring , 1997, IDA.

[27]  llsoo Ahn,et al.  Temporal Databases , 1986, Computer.

[28]  Y Shahar,et al.  Time-oriented Clinical Information Systems Time-oriented Clinical Information Systems * , 1997 .

[29]  Matteo Golfarelli,et al.  A Survey on Temporal Data Warehousing , 2009, Int. J. Data Warehous. Min..

[30]  Massimo Franceschet,et al.  Representing and Reasoning about Temporal Granularities , 2004, J. Log. Comput..

[31]  Richard T. Snodgrass,et al.  A taxonomy of time databases , 1985, SIGMOD Conference.

[32]  Alberto Riva,et al.  Temporal Abstractions for Diabetic Patients Management , 1997, AIME.

[33]  M A Musen,et al.  A Temporal Query System for Protocol-Directed Decision Support , 1994, Methods of Information in Medicine.

[34]  Curtis E. Dyreson,et al.  Temporal Granularity , 1995, The TSQL2 Temporal Query Language.

[35]  Jan Chomicki,et al.  Temporal Logic in Information Systems , 1998, Logics for Databases and Information Systems.

[36]  Jim Hunter,et al.  Deriving Trends in Historical and Real-Time Continuously Sampled Medical Data , 1999, Journal of Intelligent Information Systems.

[37]  Ashish Gupta,et al.  Materialized views: techniques, implementations, and applications , 1999 .

[38]  Serge Abiteboul,et al.  Foundations of Databases , 1994 .

[39]  C Combi,et al.  Temporal reasoning and temporal data maintenance in medicine: Issues and challenges , 1997, Comput. Biol. Medicine.

[40]  Carlo Zaniolo,et al.  Optimization of sequence queries in database systems , 2001, PODS '01.

[41]  Yuval Shahar,et al.  Knowledge-based temporal abstraction in clinical domains , 1996, Artif. Intell. Medicine.

[42]  Francesco Pinciroli,et al.  Applying object-oriented technologies in modeling and querying temporally oriented clinical databases dealing with temporal granularity and indeterminacy , 1997, IEEE Transactions on Information Technology in Biomedicine.

[43]  Jef Wijsen,et al.  Trends in Databases: Reasoning and Mining , 2001, IEEE Trans. Knowl. Data Eng..

[44]  Carlo Combi,et al.  Data mining with Temporal Abstractions: learning rules from time series , 2007, Data Mining and Knowledge Discovery.

[45]  Sushil Jajodia,et al.  Time Granularities in Databases, Data Mining, and Temporal Reasoning , 2000, Springer Berlin Heidelberg.

[46]  Surajit Chaudhuri,et al.  Maintenance of Materialized Views: Problems, Techniques, and Applications. , 1995 .

[47]  Jef Wijsen,et al.  Reasoning about Qualitative Trends in Databases , 1998, Inf. Syst..

[48]  Luca Chittaro,et al.  Abstraction on clinical data sequences: an object-oriented data model and a query language based on the event calculus , 1999, Artif. Intell. Medicine.

[49]  Carlo Combi,et al.  Temporal representation and reasoning in medicine , 2006, Artif. Intell. Medicine.

[50]  Robert H. Halstead,et al.  Parallel Symbolic Computing , 1986, Computer.

[51]  L. Ohno-Machado Journal of Biomedical Informatics , 2001 .