Incorporating repeating temporal association rules in Naïve Bayes classifiers for coronary heart disease diagnosis.

In this paper, we develop a Naïve Bayes classification model integrated with temporal association rules (TARs). A temporal pattern mining algorithm is used to detect TARs by identifying the most frequent temporal relationships among the derived basic temporal abstractions (TA). We develop and compare three classifiers that use as features the most frequent TARs as follows: (i) representing the most frequent TARs detected within the target class ('Disease = Present'), (ii) representing the most frequent TARs from both classes ('Disease = Present', 'Disease = Absent'), (iii) representing the most frequent TARs, after removing the ones that are low-risk predictors for the disease. These classifiers incorporate the horizontal support of TARs, which defines the number of times that a particular temporal pattern is found in some patient's record, as their features. All of the developed classifiers are applied for diagnosis of coronary heart disease (CHD) using a longitudinal dataset. We compare two ways of feature representation, using horizontal support or the mean duration of each TAR, on a single patient. The results obtained from this comparison show that the horizontal support representation outperforms the mean duration. The main effort of our research is to demonstrate that where long time periods are of significance in some medical domain, such as the CHD domain, the detection of the repeated occurrences of the most frequent TARs can yield better performances. We compared the classifier that uses the horizontal support representation and has the best performance with a Baseline Classifier which uses the binary representation of the most frequent TARs. The results obtained illustrate the comparatively high performance of the classifier representing the horizontal support, over the Baseline Classifier.

DBN-Extended: A Dynamic Bayesian Network Model Extended With Temporal Abstractions for Coronary Heart Disease Prognosis.

Dynamic Bayesian networks (DBNs) are temporal probabilistic graphical models that model temporal events and their causal and temporal dependencies. Temporal abstraction (TA) is a knowledge-based process that abstracts raw temporal data into higher level interval-based concepts. In this paper, we present an extended DBN model that integrates TA methods with DBNs applied for prognosis of the risk for coronary heart disease. More specifically, we demonstrate the derivation of TAs from data, which are used for building the network structure. We use machine learning algorithms to learn the parameters of the model through data. We apply the extended model to a longitudinal medical dataset and compare its performance to the performance of a DBN implemented without TAs. The results we obtain demonstrate the predictive accuracy of our model and the effectiveness of our proposed approach.

Temporal abstraction and temporal Bayesian networks in clinical domains: a survey.

OBJECTIVES: Temporal abstraction (TA) of clinical data aims to abstract and interpret clinical data into meaningful higher-level interval concepts. Abstracted concepts are used for diagnostic, prediction and therapy planning purposes. On the other hand, temporal Bayesian networks (TBNs) are temporal extensions of the known probabilistic graphical models, Bayesian networks. TBNs can represent temporal relationships between events and their state changes, or the evolution of a process, through time. This paper offers a survey on techniques/methods from these two areas that were used independently in many clinical domains (e.g. diabetes, hepatitis, cancer) for various clinical tasks (e.g. diagnosis, prognosis). A main objective of this survey, in addition to presenting the key aspects of TA and TBNs, is to point out important benefits from a potential integration of TA and TBNs in medical domains and tasks. The motivation for integrating these two areas is their complementary function: TA provides clinicians with high level views of data while TBNs serve as a knowledge representation and reasoning tool under uncertainty, which is inherent in all clinical tasks. METHODS: Key publications from these two areas of relevance to clinical systems, mainly circumscribed to the latest two decades, are reviewed and classified. TA techniques are compared on the basis of: (a) knowledge acquisition and representation for deriving TA concepts and (b) methodology for deriving basic and complex temporal abstractions. TBNs are compared on the basis of: (a) representation of time, (b) knowledge representation and acquisition, (c) inference methods and the computational demands of the network, and (d) their applications in medicine. RESULTS: The survey performs an extensive comparative analysis to illustrate the separate merits and limitations of various TA and TBN techniques used in clinical systems with the purpose of anticipating potential gains through an integration of the two techniques, thus leading to a unified methodology for clinical systems. The surveyed contributions are evaluated using frameworks of respective key features. In addition, for the evaluation of TBN methods, a unifying clinical domain (diabetes) is used. CONCLUSION: The main conclusion transpiring from this review is that techniques/methods from these two areas, that so far are being largely used independently of each other in clinical domains, could be effectively integrated in the context of medical decision-support systems. The anticipated key benefits of the perceived integration are: (a) during problem solving, the reasoning can be directed at different levels of temporal and/or conceptual abstractions since the nodes of the TBNs can be complex entities, temporally and structurally and (b) during model building, knowledge generated in the form of basic and/or complex abstractions, can be deployed in a TBN.