1.1 Medical databases
The status of databased information in current medical practice and research has been summarized by C.G. Chute of the Mayo Clinic in these words:
Medicine has emerged in the late twentieth-century as an information-intensive practice. Human memory, anecdote, and folklore can no longer train and equip medical practitioners to do their best possible job nor can they serve the interest of the patient.[1]The storage and retrieval of this data, originally consisting only of text available through local access to mainframe computers, has evolved into systems which allow use of significantly less expensive technology, new access methods, and the storage and retrieval of data of modalities other than text. In this background introduction, we briefly discuss the status of text-only medical databases and introduce the opportunities and problems presented by the emerging use of multimedia data in medical data collections.
1.1.1 The legacy: text-only databases
Collections of text-modality medical data have been widely used for clinical and research purposes
for decades. A prime example is the family of MEDLARS databases maintained by NLM for
providing access to bibliographic information and abstracts from the biomedical literature. Other
examples are the numerous databases of patient information maintained in individual hospitals,
and the large databases of medical data maintained by the Government for operation of the
Medicare program. The technology for use of these databases is not static; various researchers
are continuing to explore new ways to exploit existing collections of this data, to disseminate it
using new communications channels, and to seek new ways to extract important information.
At NLM, for example, the modem-based access to MEDLINE, which allows use of MEDLARS
databases, is now being augmented by Internet capability so that access will be possible through
Web browsers. Researchers at Washington University[2] have attacked the problem of unifying
information distributed across heterogeneous databases within a hospital for the purpose of
allowing a single, comprehensible view of the data. McDonald[3] has argued that databases can
definitely have an impact on clinical treatment; the Medicare database, for example, has shown
that the real risks of some kinds of surgery are much larger than indicated by clinical trial data,
and he identifies the need for better data analysis methods, drawing an analogy with the
discoveries astronomers have made with their own tools of spectral analysis and non-linear data
fitting methods.
An example of data accessible by MIRS is the National Health and Nutrition Examination Survey
(NHANES) data collected by the National Center for Health Statistics (NCHS). This data,
collected in surveys carried out at intervals separated by a few years, consists of many hundreds,
if not thousands, of observations for each survey participant. The second such survey, NHANES
II (1976-80), collected participant demographic data, as well as several categories of medical
information, including allergy skin testing data, anthropometric data, audiometric test data, health
history, hematology and biochemistry data, dietary information, and the results of a physician's
examination. This data has been archived and distributed by NCHS, in the form of 9-track data
tapes usually read by mainframe computers.
1.1.2 Multimedia databases
The most common multimedia databases currently in use are possibly the database systems used
by the nation's medical centers which have Picture Archiving and Communication Systems
(PACS); PACS archive and retrieve medical imagery--typically in huge volumes; as of 1992, the
UCLA PACS system was acquiring 2.0 Gigabytes of image data daily[4]. A problem at PACS
centers is unifying the patient information contained in the Hospital Information System (HIS)
database, the Radiology Information System (RIS) database, and the imagery contained in the
PACS. To our knowledge no unified approach has achieved general acceptance to the current
time. In fact, the problem of interfacing these various databases has been identified as a major
open task.[5]
A small number of medical databases supporting more than text data have emerged in recent
years. Included among these are the ARCADIA system, developed in Milan, for the access of
angiographic data and images[6], which uses the object-oriented ONTOS DBMS running on a Unix
platform, and Image Engine[7],[8], developed at the University of Pittsburgh, which allows retrieval
of various types of medical imagery on a MacIntosh computer.
1.1.3 The problem of access and use
The NHANES II text data discussed in Section 1.1.1 has been extensively used for research, in
spite of the fact that the distribution method has been limited to 9-track tapes: a MEDLINE
search in 1994 returned over 800 citations corresponding to studies using this data. Perhaps not
so well known is the fact that film imagery was also acquired in this survey: many participants
had both cervical and lumbar spine xrays taken. In contrast to the text data use, however,
NHANES films have been borrowed only nine times since 1974.
As a result of an effort to preserve and interpret the NHANES II xrays, the entire collection of
17,000 films has now been digitized, making the image data and the text data available in digital
form for the first time. An opportunity now exists for making both the images and the text
available as an integrated set.
The NHANES II collection is representative of the problem posed by sets of multimodality data
with unexploited informational potential because of the lack of a system for retrieving and
analyzing such data in an integrated manner on practical hardware platforms. We believe it is not
unique in that respect. The MIRS system has been created to remove the barriers which have
impeded the use of such collections.
2. MIRS GOALS
The goal of MIRS is to "Deliver the information": to streamline the accessibility of collections of
medical text and visual data which have been up to now inaccessible, or accessible only with
unintegrated tools. The emphasis of MIRS is medical information delivery--we conceive of MIRS
as being fundamentally an information vehicle--hence we place great importance on incorporating
all the necessary features, interfaces, and software hooks which will allow MIRS to deliver data
judged to be of significant medical informational content by specific targeted user groups. A
second general goal of MIRS is to be as generic as possible: MIRS should provide access to
many classes of medical data.
Specific subgoals to achieve these general ends are as follows:
3.1 Overview
In this section we discuss how MIRS has been designed to address each of the subgoals listed in
Section 2.
MIRS is intended to allow for the easy addition of collections of medical data consisting of both
text and images, other than the NHANES II data set. A MIRS database must conform to these
rules:
For illustration, consider how the NHANES database fits into MIRS:
3.3 Generic nature of the MIRS query-building screen
The MIRS screen for constructing queries is called the Search Database screen. The
screen is logically divided into two parts: a top part where the user builds relational clauses (e.g.
"age > 45", "kyphosis = yes") and a bottom part, where the user combines these clauses with
Boolean connectors to build an SQL query which is then submitted. The top part of the screen
has list boxes containing the field names, relational operators, and field values appropriate for the
particular MIRS database being used. Also, each of these list boxes on the screen is labelled
appropriately for the particular MIRS database being used. If the user selects another MIRS
database (which is a MIRS option on the initial login screen), the labels on these boxes as well as
their contents change appropriately for the new database.
The ability of this interface to adapt to a new database on-the-fly is provided by three design
features:
Figure 4 represents the interactions between the ASCII script, the event-handling code,
and the dynamic display on the screen.
3.4 MIRS communication features for image communications
In order to increase the performance of MIRS in the delivery of image data, we have incorporated
into MIRS an experimental communications method, the "multisocket" file delivery method,
developed at NLM.[10] This method uses multiple TCP/IP channels to segment a file, transmit it,
and reassemble it at the receiving end. Because overall wait time for acknowledgments is reduced
in this method, higher transmission rates are achievable than when using conventional one-channel
TCP/IP methods, such as FTP. Internet tests we have performed with this method have achieved
speed-ups of 2.5:1 or more as compared with conventional FTP.[10] The multisocket method has
been implemented as an experimental "multisocket server" which is currently running on two
computers in our lab. An Application Program Interface (API) has been written for this server,
and it is through use of this API that MIRS uses the multisocket method.
The interaction of MIRS with the multisocket servers is as follows. When the user has formulated
and submitted an SQL query, the MIRS client (which may be situated remotely to NLM on the
Internet) sends the query to the MIRS server at NLM. The MIRS server responds by querying
the appropriate MIRS database for the text data which satisfies the query, along with the file
names of the images required to satisfy the query. This list of file names is returned to the MIRS
client. The MIRS client then contacts the multisocket server on the NLM machine which contains
the particular images named; at present, large multi-megabyte images are kept in magneto-optical
storage on a jukebox hosted by the liberty machine; small images are kept in magnetic
storage on a RAID system hosted by the archive machine. This is illustrated in Figure
5.
3.5 Generic nature of the MIRS query-results screen
Query results are presented on the MIRS Database Information screen. The top part of
this screen is a horizontally-scrollable, multiple-image display; the bottom part of the screen is a
vertically-scrollable text display of a single text record. Images may be quickly viewed by using a
slider bar. Text records may be viewed in sequence by using the next record/prev record
buttons. A control at the upper left allows the user to change the image display to show other
types of images associated with the text records. An image cursor is implemented as a colored
border which surrounds the image selected by the user as "current", by simply clicking the mouse
on that image. When an image is identified as "current", the text record display immediately
updates to provide the descriptive text information appropriate for that image. Similarly, when
the user uses the next record/prev record buttons to move through the text records, the
image cursor automatically moves to identify the image corresponding to the text record on
display.
This screen currently only has capability to display grayscale images in 128 shades of gray. Each
image which appears on the Database Information screen must be preformatted in a
MIRS-compatible format; at present, MIRS only recognizes the GIF file format for these images.
The image sizes MIRS will support on this screen are arbitrary, within bounds determined by the
screen spatial resolution, and by the fact that the screen space needs to be shared with the text
record display and graphical controls. Within these constraints, MIRS has the capability to
display "generic" gray-scale images in GIF format.
In addition MIRS provides a Full Resolution Display screen. This capability is for
showing a high-resolution version of a single image which appears on the Database
Information screen. This is applicable for images such as the cervical and lumbar spine xrays,
which have large spatial resolution (1463x1755 and 2048x2487 pixels, respectively), preventing
multiple images from being shown on the Database Information screen in full resolution.
Instead, Database Information shows lower resolution versions of such images; the user
may select any of these images and use a graphical control to bring up the Full Resolution
Display to show the full resolution version.
3.6 Examples of MIRS screens
Figure 6 shows an example of a MIRS Search Database screen for the NHANES
database. In this example, the user has created a query to search for all database records
corresponding to black females at least 45 years of age having the spinal condition of kyphosis.
Several list boxes have been created on the screen in response to user actions: in the
DEMOGRAPHIC FEATURES part of the screen, the user has selected "Sex" from the
Feature list; this caused the list box titled Value to appear to the right; from this
Value list, the user has selected "Female"; likewise, in the EXAMINATION FEATURES,
the user has selected "Physician's Exam" from the list box titled Tape; this caused the
Topic list box to appear; selecting an item from the Topic list caused the
Subtopic list box to appear, and so forth.
As the user makes selections from this lists, relations (such as "Age > 45") appear in the Query
Construction Area at the bottom left. Finally, the user joins the relations with Boolean
connectors to form the query; in this case, four relations have been joined with AND. The final
query appears in the Constructed Query box.
Figure 7 shows an example of a MIRS Database Information screen for the
NHANES database. This screen is the output of a query submitted by the user. In this case the
query was a search for all database records corresponding to white females at least 50 years of
age. Cervical x-ray images are shown in the upper part of the screen in a horizontally-scrollable
display, while the text corresponding to the "current image" (highlighted with an image cursor
which frames the image) appears in the lower part of the screen. The text may be scrolled
vertically to show additional textual data.
3.7 MIRS client platform
The current MIRS client is designed to run on a Sun Sparc 10 class workstation. The initial
target user community for MIRS is the research community in the medical school/university
environment. Additional MIRS access may be provided in the future on a wider class of
platforms, or by using platform-independent means such as the World Wide Web.
4. CONCLUSION
MIRS is a prototype client/server system with the goal of providing a solution to the problem of
extracting medical information from remote collections of medical text and image data. MIRS
provides a generic method for data organization, retrieval, presentation, and analysis which is
intended to apply to a wide class of medical datasets. MIRS capabilities to retrieve and present
data have been implemented for a set of NHANES II survey data, and work is planned to add
capabilities for data analysis, saving of results, and image processing, as well as the addition of
other databases to MIRS.
5. ACKNOWLEDGMENTS
We wish to acknowledge the efforts and dedication of Leif Neve, Patty Bailey, and Yun Cai, of
Century Computing, Inc. in developing the MIRS system.
6. REFERENCES
1. C.G. Chute, "Clinical data retrieval and analysis: I've seen a case like that before," Annals
of the New York Academy of Sciences , (670), pp. 133-140, 1992.
2. K.A. Marrs, S.A. Steib, C.A. Abrams, and M.G. Kahn, "Unifying heterogeneous distributed
clinical data in a relational database," Proceedings of SCAMC 1993, pp. 644-648.
3. C.J. McDonald and S.L. Hui. " The analysis of humongous databases: problems and
promises," Statistics in Medicine, Vol. 10, pp. 511-518, 1991.
4. A.W.K. Wong and H.K. Huang, "Subsystem throughputs of a clinical picture archiving and
communications system," Proceedings of SPIE Medical Imaging VI, Newport Beach, CA.
1992.
5. H.K. Huang, R.L. Arenson, S.-L. Lou, A.W.K. Wong, K.P. Andriole, T.M. Bazzill, and D.
Avrin, "Multimedia in the radiology environment: current concept," Computerized Medical
Imaging and Graphics, Vol. 18, No. 1, pp. 1-10, 1994.
6. F. Pinciroli, C. Combi and G. Pozzi. "ARCADIA: A system for the integration of
angiocardiographic data and images by an object-oriented DBMS," Computers and
Biomedical Research, Vol. 28, pp. 5-23, 1995.
7. H.J. Lowe, B.G. Buchanan, G.F. Cooper, and J.K. Vries. "Building a medical multimedia
database system to integrate clinical information: an application of high-performance computing
and communications technology," Bulletin of the Medical Libraries Association, Vol. 83,
No. 1, pp. 57-64, January 1995.
8. H.J. Lowe. " Image Engine: an object-oriented multimedia database for storing, retrieving and
sharing medical images and text," Proceedings of SCAMC 1993, pp. 839-843.
9. M. Stonebraker. " Object-relational DBMS: the next wave," Illustra Information Technologies,
Inc, Oakland, CA, 1995.
10. L.R. Long, L.E. Berman, G.R. Thoma. "An Application-Level Technique for Transmission of
Large Images on the Internet,"Proceedings of SPIE: Multimedia Computing and Networking
1995, Vol. 2417, San Jose, CA, pp. 116-129, February 6-8, 1995.
3. MIRS DESIGN
3.2 Generic nature of the MIRS databases
In the implementation of a MIRS database, the non-hierarchical data becomes one relational
database table; each hierarchy tree becomes one table; and data which points to image files is
collected in one table. Figure 1 gives a picture of a generic MIRS database. This is the
general structure to which any collection of text/image data has to conform to become a database
accessible to the MIRS client.
Figure 2 shows the NHANES II database realization of the generic MIRS database
structure.
Figure 3.
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