Information theoretical approach to constitution and reduction of medical data

• Published in: International journal of medical informatics (Shannon, Ireland) Ireland), 1997-06, Vol.45 (1), p.65-74
• Main Authors: Zvarova, Jana, Studeny, Milan
• Format: Article
• Language: English
• Subjects: Algorithms; Conditional independence; Constitution of medical data; Data Interpretation, Statistical; Decision Making; Decision Support Systems, Management; Diagnosis; Diagnosis, Computer-Assisted; Forecasting; Humans; Information Theory; Measure of statistical dependence; Multiinformation; Probability; Reduction of medical data; Software; Stochastic Processes
• ISSN: 1386-5056; 1872-8243
• DOI: 10.1016/S1386-5056(97)00036-1

In medical decision problems it is very important to use the most relevant piece of information for decision making. We focus on a special case of diagnostic decision making when we can measure many symptoms and signs and we have to make diagnostic conclusions. We can state the problem as follows. We can measure symptoms and signs of a patient, denoted by s 1, s 2,…, s k , and we have to decide about a possible diagnosis d. We know that the symptoms and signs have different costs w 1, w 2,… w k when they are examined. Of course, each symptom, sign or their combination has a different predictive value for the diagnosis. Our task is to find out the combination of symptoms from given data with a sufficient informative value for diagnostic decision making. However, simultaneously we look for a combination of symptoms and signs with minimal costs among those carrying sufficient information. For that reason we will describe approaches based on information measures of statistical dependence and to show the idea of the program CORE (constitution and reduction of data) prepared for practical applications in medicine.