Mining association rules with improved semantics in medical databases
Section snippets
Application domain
Nowadays, data stored in medical databases are growing in an increasingly rapid way. Analyzing that data is crucial for medical decision making and management. It has been widely recognized that medical data analysis can lead to an enhancement of health care by improving the performance of patient management tasks [6], [7]. There are two main aspects that define the need for medical data analysis [6].
- •
Support of specific knowledge-based problem solving activities through the analysis of patients
Problem statement
There is an increasing interest in finding association rules among values of quantitative attributes in relational databases [11], as these kind of attributes are rather frequent. Quantitative attributes are those whose domain contain many precise values. Medical databases are used to store a big amount of quantitative attributes. But in common conversation and reasoning, humans employ rules relating imprecise terms rather than precise values. For instance, a physician will find more
Applied methods
We have employed several techniques in order to reach our goal.
- 1.
One of the best tools to represent linguistic imprecise terms with clear semantic content is the theory of fuzzy sets. By using this theory, the meaning of imprecise terms can be modeled by means of fuzzy sets in the appropriate domain. For example, a possible representation of imprecise terms related to the “Age”, by means of fuzzy sets, is shown in Fig. 1. Fig. 2 shows a set of imprecise terms for the “Hour”. The definition of the
Results
We have performed several experiments on large medical databases obtained from the University Hospital of Granada, specifically the relations URGENCY and SURGICAL OPERATIONS, containing 81,368 and 15,766 tuples, respectively. We show in [8] that fuzzy association rules allow us to (a) obtain rules with better semantics, and (b) obtain rules with enough support among quantitative attributes (otherwise, the high number of distinct and precise values makes the support of rules relating values of
Outlook
At this moment we are about to start the analysis of new medical databases obtained from several health services of Granada. From a theoretical point of view, we are concerned with the study of fuzzy hierarchies to find association rules with different granularity (precision) levels. Also, we are studying the unification of approximate dependencies (functional dependencies with a few exceptions) and fuzzy functional dependencies, in order to obtain “almost functional dependencies” described by
References (12)
- et al.
Fuzzy cardinality based evaluation of quantified sentences
Int. J. Approx. Reasoning
(2000) - et al.
A model of inexact resoning in medicine
Math. Biosci.
(1975) A computational approach to fuzzy quantifiers in natural languages
Comput. Math. Appl.
(1983)- Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in large databases. In: Proceedings of...
- et al.
Dynamic itemset counting and implication rules for market basket data
SIGMOD Record
(1997) - Delgado M, Sánchez D, Vila MA. Acquisition of fuzzy association rules from medical data. In: Barro S, Marı́n R,...
Cited by (62)
Identifying risk factors for adverse diseases using dynamic rare association rule mining
2018, Expert Systems with ApplicationsCitation Excerpt :Computational intelligence techniques have gained much importance of let in identification of risk factors for harmful diseases (Alizadehsani et al., 2013; Anooj, 2012; Nahar et al., 2013; Nahato et al., 2015). One of the commonly used techniques for disease diagnosis is association rule mining (Delgado, SáNchez, MartıN-Bautista, & Vila, 2001; Ordonez, 2006a; Ordonez et al., 2006). Association rule mining has several applications in medical domain including disease co-occurrence detection (Cao, Mamoulis, & Cheung, 2005), discovering adverse drug reactions (Wang et al., 2012), identifying risk factors for heart disease (Nahar et al., 2013) and public health surveillance (Mullins et al., 2006).
Fuzzy quantification: A state of the art
2014, Fuzzy Sets and SystemsCompass: A hybrid method for clinical and biobank data mining
2014, Journal of Biomedical InformaticsCitation Excerpt :The various types of existing AM methods typically address relatively simple dichotomous data sets containing only 1s and 0s. Applying AM to mine other types of data, such as clinical data, has been done in several, previous studies [6]. These approaches utilized the support measure to control the size and shape of the search space of associations.
A preclustering-based ensemble learning technique for acute appendicitis diagnoses
2013, Artificial Intelligence in MedicineCitation Excerpt :Alternatively, considering the close association with important patient symptoms and laboratory results, acute appendicitis diagnoses could be supported by a data-driven approach, which is particularly appealing considering the limited patient data and laboratory results available to healthcare organizations. The use of data mining seems promising in this context, because it could reduce the likelihood of misdiagnoses and avoid unnecessary surgical procedures or corrective therapeutic treatments [2–4,17,22,33]. Previous studies suggest the clinical value of data mining for clinical decision support [6,8,9,22,29]; however, a fundamental challenge remains in the form of the skewed outcome class distribution among the instances in a training sample.
Discovering associations between radiological features and COVID-19 patients' deterioration
2023, Health Science Reports