Computer-based models of medical decision making account for a large portion of clinical computing efforts. This article reviews representative examples from each of several major medical computing paradigms. These include 1) clinical algorithms, 2) clinical databanks that include analytic functions, 3) mathematical models of physical processes, 4) pattern recognition, 5) Bayesian statistics, 6) decision analysis, and 7) symbolic reasoning or artificial intelligence. Because the techniques used in the various systems cannot be examined exhaustively, the case studies in each category are used as a basis for studying general strengths and limitations. It is noted that no one method is best for all applications. However, emphasis is given to the limitations of early work that have made artificial intelligence techniques and knowledge engineering research particularly attractive. We stress that considerable basic research in medical computing remains to be done and that powerful new approaches may lie in the melding of two or more established techniques.

Knowledge engineering for medical decision making: A review of computer-based clinical decision aids

INTERNIST-1 and its successor, QUICK MEDICAL REFERENCE (QMR), are computer programs designed to provide health care professionals with diagnostic assistance in general internal medicine. Both programs rely on the INTERNIST-1 computerized knowledge base, which comprehensively describes 570 diseases in internal medicine. The philosophies behind the development of each program differ. Whereas INTERNIST-1 functions solely as a high-powered diagnostic consultant program, the QMR program acts more as an information tool, providing users with multiple ways of reviewing and manipulating the diagnostic information in the program's knowledge base. At the lowest level, the program can be viewed as an electronic textbook of medicine. In addition, the QMR program has the ability to assist users with generating hypotheses in complex patient cases. The QMR program has not been evaluated formally as an information tool for practicing physicians. A preliminary study indicates that QMR's case-analysis capabilities are of potential benefit in most patients in internal medicine admitted for diagnostic evaluation.

The INTERNIST-1/QUICK MEDICAL REFERENCE project--status report.

Database system performance is directly related to the efficiency of the system at storing data on primary storage (for example, disk) and moving it into CPU registers for processing. For this reason, there is a long history in the database community of research exploring physical storage alternatives, including sophisticated indexing, materialized views, and vertical and horizontal partitioning. In recent years, there has been renewed interest in so-called column-oriented systems, sometimes also called column-stores. Column-store systems completely vertically partition a database into a collection of individual columns that are stored separately. By storing each column separately on disk, these column-based systems enable queries to readjust the attributes they need, rather than having to read entire rows from disk and discard unneeded attributes once they are in memory. The Design and Implementation of Modern Column-Oriented Database Systems discusses modern column-stores, their architecture and evolution as well the benefits they can bring in data analytics. There is a specific focus on three influential research prototypes, MonetDB, MonetDB/X100, and C-Store. These systems have formed the basis for several well-known commercial column-store implementations. Their similarities and differences are described and they are discussed in terms of their specific architectural features for compression, late materialization, join processing, vectorization and adaptive indexing (database cracking). The Design and Implementation of Modern Column-Oriented Database Systems is an excellent reference on the topic for database researchers and practitioners.

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The Design and Implementation of Modern Column-Oriented Database Systems

The experience with 83 patients aged 60 or older from the Stanford Sleep-Wake Disorders Clinic is compared with that in 423 younger clinic patients seen during the same two-year period. Each patient received a medical, psychologic and polysomnographic evaluation. The final diagnoses were recorded according to the Diagnostic Classification System of the Association of Sleep Disorders Centers. The most common major diagnoses in the elderly group were sleep apnea syndrome (39 percent) and periodic movements-restless legs syndrome (18 percent). These syndromes showed a significantly greater prevalence in the older than in the younger patients (p less than .001), and were found in 68 percent of the elderly group. The elderly manifested more objective signs of sleep disturbance, including more wake time after sleep onset, and more frequent and longer awakenings; moreover, fewer of them experienced stage-4 sleep. The diagnostic findings seemed to indicate that complaints about sleep-wake functioning in many elderly patients may be a result of specific pathologic sleep disturbances.

Frank Germano

Papers

A modular self-describing clinical databank system.

A Data Base Organization to Support a Time-Oriented Medical Record.