100 IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 1, NO. 2, JUNE 1997 Applying Object-Oriented Technologies in Modeling and Querying Temporally Oriented Clinical Databases Dealing with Temporal Granularity and Indeterminacy Carlo Combi, Member, IEEE, Giorgio Cucchi, and Francesco Pinciroli Abstract—The need of managing temporal information given at different levels of granularity or with indeterminacy is common to many application areas. Among them, we focus on clinical data management. Different time granularities and indeterminacy are also needed in querying temporal databases. In this paper, we describe GCH-OSQL (Granular Clinical History-Object Struc- tured Query Language), an object-oriented temporally-oriented extension of SQL. GCH-OSQL is based on an object-oriented temporal data model, GCH-OODM. GCH-OODM allows storage of clinical information at different and mixed granularities or with temporal indeterminacy. GCH-OSQL deals with the valid time of clinical information. The temporal extension of the SE- LECT construct includes the addition of the TIME-SLICE and MOVING WINDOW clauses, and the capability to reference the temporal dimension of objects in the WHERE and SELECT clauses. Using object-oriented technologies, a system prototype for GCH-OSQL and GCH-OODM has been implemented and applied to data management of follow-up patients after coronary angioplasty intervention. Index Terms— Database technology, object-oriented technol- ogy, medical informatics, temporal databases. I. INTRODUCTION D IFFERENT fields of computer science deal with temporal data management and representation: artificial intelli- gence, software engineering, and databases are some of those fields, in which topics related to the management of the temporal dimension of data are dealt with [1], [28], [38], [49]. In database field, topics related to temporal databases gained increasing attention during the last years [13], [15], [25], [30], [38], [49]. Among main research directions, we can Manuscript received February 4, 1997; revised July 3, 1997. This work was supported in part by contributions from MURST Italian National Project for Medical Informatics, the Department of Biomedical Engineering of the Politecnico di Milano, and CNR’s Centro Studi per la Teoria dei Sistemi, Department of Mathematics and Computer Science of the University of Udine. C. Combi is with the Dipartimento di Matematica e Informatica, Universita’ degli Studi di Udine, 33100 Udine, Italy (e-mail: combi@dimi.uniud.it). G. Cucchi was with the Dipartimento di Bioingegneria, Politecnico di Milano, Milano, Italy. He is now with Sinapsi srl, Rome, Italy. F. Pinciroli is with the Dipartimento de Bioengegneria, Politecnico di Milano, Milano, Italy. He is also with the Centro di Ingegneria Biomedica del CNR, Milano, Italy. Publisher Item Identifier S 1089-7771(97)07233-6. distinguish the study of temporal data models and the defini- tion of suitable temporal query languages [49]. The proposed temporal data models usually consist in extensions of already existing data models, like the relational model, the entity- relationship model, the object-oriented models [31], [44], [49], [54]; similarly the query languages for temporal databases are based on extensions of the most known query languages [31], [36], [39], [43], [48]. Medical Informatics pays particular attention to the problem of managing and representing the temporal aspect of clinical information [11], [14], [22], [23], [27], [41], [50]. Time is important for clinical medicine both in defining the diagnosis, in identifying the therapy, and in defining the prognosis [11], [21], [41]: these decision making actions hold during a time and are dealt with in respect to information that is temporally characterized. In order to assess a diagnosis, in fact, the physician finds out the clinical history of the patient, composed usually by previous pathologies, therapies, and symptoms, the patient narrates; this information completes data collected directly from the patient, i.e., the blood pressure, heart rate, identified by their temporal location [40]. In supporting by computer the storage and the retrieval of this information, we need to deal with the temporal dimension of clinical data, besides with the complex structure of natural language-related data. The considered temporal dimension is usually the valid time, i.e., the time during which the information is true in the modeled reality [49]. A. Modeling and Querying Temporal Clinical Data Some important issues have to be considered in temporally- oriented clinical data. • The temporal dimension is expressed by using sometimes the concept of interval, for facts having a span of time, and sometimes the concept of instant for events holding at a time point [11], [14], [22], [53]. This temporal dimension, moreover, can be expressed in different and heterogeneous way: the used time axis, for example, has different time units (“in 1989 the patient had myocardial infarction,” “at 1:00 p.m. the patient had an episode of atrial fibrillation: it lasted 48 minutes”); in other cases the temporal location is expressed with some vague- 1089–7771/97$10.00  1997 IEEE