Abstract
Medical knowledge databases are collections of information about specific medical problems, and they are primarily designed to help clinicians make appropriate decisions in the diagnosis and treatment of their patients. Knowledge discovery is the process of automatically searching knowledge bases and other large computer databases for potentially useful or previously unknown information by using techniques from statistics and information science. Gabrieli (1978) estimated that a total and comprehensive medical-knowledge database required by a physician for the practice of the specialty of internal medicine might consist of about 210 distinct facts, compounded with patterns and probabilistic semantic relationships; and when treating a patient would need to include data gathered in the collection of the patient’s past and present medical history; the data that originated in the physician’s memory of related knowledge and experience; and the physician’s decision as to of probable diagnoses and treatments related to the patient’s problems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agrawal R, Srikant R. Fast algorithms for mining association rules. Proc 20th Internatnl Conf on Very Large Databases. 1994:487–99.
Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in large databases. Proc ACM SIGMOD Internatnl Conf on Management of Data. 1993a:207–16.
Agrawal R, Imelienski T, Swami A. Database mining: a performance perspective. IEEE Trans Knowledge Data Eng. 1993b;5:914–25.
Agrawal R, Mehta M, Shafer J, et al. The quest data mining system. Proc Internatnl Conf Data Mining and Knowledge Discovery. 1996:244–9.
Baskin AB, Levy AH. MEDIKAS – an interactive knowledge acquisition system. Proc SCAMC. 1978:344–50.
Bayes T. An essay towards solving a problem in the doctrine of chances. MD Comput. 1991;8:157–71 (copied from Philosophical Trans Royal Soc London 1763).
Berman L, Cullen M, Miller P. Automated integration of external databases: a knowledge-based approach to enhancing the rule-based expert systems. Comput Biomed Res. 1993;26:230–41.
Benoit G, Andrews JE. Data discretization for novel resource discovery in large medical data sets. Proc AMIA Symp. 2000:61–5.
Berndt DJ, Hevner AR, Studnicki J. CATCH/IT: a data warehouse to support comp community health. Proc AMIA Symp. 1998:250–4.
Bernstein LM, Siegel ER, Goldstein CM. The hepatitis knowledge base. Ann Intern Med. 1980;93(Supp 1):165–222.
Bleich HL. Computer evaluation of acid-base disorders. J Clin Invest. 1969;48:1689–996.
Blum RL. Automating the study of clinical hypotheses on a time-oriented database: the RX project. Proc MEDINFO. 1980:456–60.
Blum RL. Automated induction of causal relationships from a time-oriented clinical database. Proc AMIA. 1982a:307–11.
Blum RL. Discovery and representation of causal relationships from a large time-oriented clinical database: the RX project. Chap 2: the time-oriented database. In: Lindberg DAB, Reichertz PL, Lindberg DAB, Reichertz PL, editors. Lecture notes in medical informatics. New York: Springer; 1982b. p. 38–57.
Blum RL. Discovery, confirmation, and incorporation of causal relationships from a large time-oriented clinical database: the RX project. Comput Biomed Res. 1982c;15:164–87.
Blum RL. Machine representation of clinical causal relationships. Proc MEDINFO. 1983:652–6.
Blum RL, Wiederhold G. Inferring knowledge from clinical data banks utilizing techniques from artificial intelligence. Proc SCAMC. 1978:303–7.
Blum RL, Wiederhold GCM. Studying hypotheses on a time-oriented clinical database: an overview of the RX project. Proc SCAMC. 1982:712–5.
Bohren BF, Hadzikadic M, Hanley EN. Extracting knowledge from large databases: an automated approach. Comput Biomed Res. 1995;28:191–210.
Brossette SE, Sprague AP, Jones WT, Moser SA. A data mining system for infection control surveillance. Methods Inform Med. 2000;39:303–10.
Clayton PD, Haug PJ, Pryor TA, Wigertz OB. Representing a medical knowledge base for multiple uses. Proc AAMSI. 1987:289–93.
Codd EF. A relational model of data for large shared data banks. Commun ACM. 1970;13:377–87.
Codd EF, Codd SB, Salley CT. Providing OLAP (On-line analytical processing) to user-analysts: an IT Mandate. San Jose: Codd & Date Inc; 1993.
Connolly TM, Begg CE. Database management systems: a practical approach to design, implementation, and management. 2nd ed. New York: Addison-Wesley; 1999.
Cooper LG, Giufridda G. Turning data mining into a management tool: new algorithms and empirical results. Manag Sci. 2000;46:249–64.
Doszkocs TE, Rapp BA, Schoolman HM. Automated information retrieval in science and technology. Science. 1980;208:25–30.
Downs SM, Wallace MY. Mining association rules from a pediatric primary care decision support system. Proc AMIA. 2000:200–4.
Evans S, Lemon SJ, Deters CA, et al. Automated detection of hereditary syndromes using data mining. Comput Biomed Res. 1997a;30:337–48.
Evans S, Lemon SJ, Deters CA, et al. Using data mining to characterize DNA mutations by patient clinical features. Proc AMIA. 1997b:253–7.
Fayyad UM, Piatetsky-Shapiro G, Smyth P. From data mining to knowledge discovery in databases. AI Mag. 1996;17:37–54.
Fox MA. Linguistic implications of context dependency in ACIS. Proc MEDINFO. 1980:1285–9.
Frawley WJ, Piatetsky-Shapito G, Matheus CJ. Knowledge discovery in databases: an overview. AI Mag. 1992;13:57–70.
Gabrieli ER. Knowledge base structures in a medical information system. Proc 8th Ann Conf Soc Comp Med. 1978:1.2.9–11.
Hand DJ. Data mining statistics and more. Am Stat. 1998;52:112–8.
Hand DJ, Blunt G, Kelly MG, Adams NM. Data mining for fun and profit. Stat Sci. 2000;15:111–31.
Haughton D, Deichmann J, Eshghi A, et al. A review of software packages for data mining. Am Stat. 2003;57:290–309.
Holmes JH, Durbin DR, Winston FK. Discovery of predictive models in an injury surveillance database: an application of data mining in clinical research. Proc AMIA Symp. 2000:359–63.
Johnson SB. Extended SQL for manipulating clinical warehouse data. Proc AMIA Symp. 1999:819–23.
Ledley RS, Lusted LB. Reasoning foundations of medical diagnosis. Science. 1959;130:9–21.
Lee IN, Liao SC, Embrechts M. Data mining techniques applied to medical information. Med Inform Internet Med. 2000;25:81–102.
Lindberg DAB, Van Pelnan HJ, Couch RD. Patterns in clinical chemistry. Am J Clin Pathol. 1965;44:315–21.
Lindberg DAB, Takasugi S, DeLand EC. Analysis of blood chemical components distribution based on thermodynamic principle. Proc MEDIS ’78, Osaka; 1978. p. 109–12.
Lindberg DAB, Gaston LW, Kingsland LC, et al. A knowledge-based system for consultation about blood coagulation studies. In: Gabriele TG, editor. The human side of computers in medicine. Proc Soc for Computer Med; 10th Annual Conf., San Diego; 1980. p. 5.
Ludwig DW. INFERNET – a computer-based system for modeling medical knowledge and clinical inference. Proc SCAMC. 1981:243–9.
Nigrin DJ, Kohane IS. Data mining by clinicians. Proc AMIA Symp. 1998:957–61.
Nigrin DJ, Kohane IS. Scaling a data retrieval and mining application to the enterprise-wide level. Proc AMIA. 1999:901–5.
Nigrin DJ, Kohane IS. Temporal expressiveness in querying a time-stamp-based clinical database. J Am Med Inform Assoc. 2000;7:152–63.
Prather JC, Lobach DF, Goodwin LK, et al. Medical data mining: knowledge discovery in a clinical data warehouse. Proc AMIA Symp. 1997:101–5.
Shafer SL, Shafer A, Foxlee RH, Prust R. Aesculapius: the implementation of a knowledge base on a microcomputer. Proc MEDCOMP IEEE. 1982:413–9.
Srinivasan P, Rindflesch T. Exploring text mining from MEDLINE. Proc AMIA. 2002:722–6.
Starmer CF. Feedback stabilization of control policy selection in data/knowledge based systems. Proc SCAMC. 1984:586–91.
Sterling T, Gleser M, Haberman S, Pollack S. Robot data screening: a solution to multivariate type problems in the biological and social sciences. Commun ACM. 1966;9:529–32.
Szarfman A, Machado SG, O’Neil RT. Use of screening algorithms and computer systems to efficiently signal higher-than-expected combinations of drugs and events in the US FDA’s Spontaneous Reports Database. Drug Safety 2002:25:381–392.
Tenabe L, Scherf U, Smith LH, et al. MedMiner: an internet text-mining tool for biomedical information, with applications to gene expression profiling. Biotechniques. 1999;6:1210–4.
Wiederhold GC, Walker MG, Blum RL et al. Acquisition of medical knowledge from medical records. Proc Benutzergruppenseminar Med Sys; Munich; 1987. p. 8213–21.
Wilcox A, Hripcsak G. Knowledge discovery and data mining to assist natural language understanding. Proc AMIA. 1998:76–8.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag London Limited
About this chapter
Cite this chapter
Collen, M.F. (2012). Medical Knowledge Databases. In: Computer Medical Databases. Health Informatics. Springer, London. https://doi.org/10.1007/978-0-85729-962-8_8
Download citation
DOI: https://doi.org/10.1007/978-0-85729-962-8_8
Published:
Publisher Name: Springer, London
Print ISBN: 978-0-85729-961-1
Online ISBN: 978-0-85729-962-8
eBook Packages: MedicineMedicine (R0)