Abstract Recommender systems are used to suggest items to users based on their interests. They have been used widely in various domains, including online stores, web advertisements, and social networks. As part of their process, recommender systems use a set of similarity measurements that would assist in finding interesting items. Although many similarity measurements have been proposed in the literature, they have not concentrated on actual user interests. This paper proposes a new efficient hybrid similarity measure for recommender systems based on user interests. This similarity measure is a combination of two novel base similarity measurements: the user interest–user interest similarity measure and the user interest–item similarity measure. This hybrid similarity measure improves the existing work in three aspects. First, it improves the current recommender systems by using actual user interests. Second, it provides a comprehensive evaluation of an efficient solution to the cold start problem. Third, this similarity measure works well even when no corated items exist between two users. Our experiments show that our proposed similarity measure is efficient in terms of accuracy, execution time, and applicability. Specifically, our proposed similarity measure achieves a mean absolute error (MAE) as low as 0.42, with 64% applicability and an execution time as low as 0.03 s, whereas the existing similarity measures from the literature achieve an MAE of 0.88 at their best; these results demonstrate the superiority of our proposed similarity measure in terms of accuracy, as well as having a high applicability percentage and a very short execution time.

In many real-world applications, it is important to mine causal relationships where an event or event pattern causes certain outcomes with low probability. Discovering this kind of causal relationships can help us prevent or correct negative outcomes caused by their antecedents. In this paper, we propose an innovative data mining framework and apply it to mine potential causal associations in electronic patient data sets where the drug-related events of interest occur infrequently. Specifically, we created a novel interestingness measure, exclusive causal-leverage, based on a computational, fuzzy recognition-primed decision (RPD) model that we previously developed. On the basis of this new measure, a data mining algorithm was developed to mine the causal relationship between drugs and their associated adverse drug reactions (ADRs). The algorithm was tested on real patient data retrieved from the Veterans Affairs Medical Center in Detroit, Michigan. The retrieved data included 16,206 patients (15,605 male, 601 female). The exclusive causal-leverage was employed to rank the potential causal associations between each of the three selected drugs (i.e., enalapril, pravastatin, and rosuvastatin) and 3,954 recorded symptoms, each of which corresponded to a potential ADR. The top 10 drug-symptom pairs for each drug were evaluated by the physicians on our project team. The numbers of symptoms considered as likely real ADRs for enalapril, pravastatin, and rosuvastatin were 8, 7, and 6, respectively. These preliminary results indicate the usefulness of our method in finding potential ADR signal pairs for further analysis (e.g., epidemiology study) and investigation (e.g., case review) by drug safety professionals.

Early detection of unknown adverse drug reactions (ADRs) in postmarketing surveillance saves lives and prevents harmful consequences. We propose a novel data mining approach to signaling potential ADRs from electronic health databases. More specifically, we introduce potential causal association rules (PCARs) to represent the potential causal relationship between a drug and ICD-9 (CDC. (2010). International Classification of Diseases, Ninth Revision (ICD-9). [Online]. Available: http://www.cdc.gov/nchs/icd/icd9.html) coded signs or symptoms representing potential ADRs. Due to the infrequent nature of ADRs, the existing frequency-based data mining methods cannot effectively discover PCARs. We introduce a new interestingness measure, potential causal leverage, to quantify the degree of association of a PCAR. This measure is based on the computational, experience-based fuzzy recognition-primed decision (RPD) model that we developed previously (Y. Ji, R. M. Massanari, J. Ager, J. Yen, R. E. Miller, and H. Ying, "A fuzzy logic-based computational recognition-primed decision model," Inf. Sci., vol. 177, pp. 4338-4353, 2007) on the basis of the well-known, psychology-originated qualitative RPD model (G. A. Klein, "A recognition-primed decision making model of rapid decision making," in Decision Making in Action: Models and Methods, 1993, pp. 138-147). The potential causal leverage assesses the strength of the association of a drug-symptom pair given a collection of patient cases. To test our data mining approach, we retrieved electronic medical data for 16,206 patients treated by one or more than eight drugs of our interest at the Veterans Affairs Medical Center in Detroit between 2007 and 2009. We selected enalapril as the target drug for this ADR signal generation study. We used our algorithm to preliminarily evaluate the associations between enalapril and all the ICD-9 codes associated with it. The experimental results indicate that our approach has a potential to better signal potential ADRs than risk ratio and leverage, two traditional frequency-based measures. Among the top 50 signal pairs (i.e., enalapril versus symptoms) ranked by the potential causal-leverage measure, the physicians on the project determined that eight of them probably represent true causal associations.

The current approach to postmarketing surveillance primarily relies on spontaneous reporting. It is a passive surveillance system and limited by gross underreporting (<;;10% reporting rate), latency, and inconsistent reporting. We propose a new interestingness measure, causal-leverage, to signal potential adverse drug reactions (ADRs) from electronic health databases which are readily available in most modern hospitals. This measure is based on an experience-based fuzzy recognition-primed decision (RPD) model that we developed previously which assesses the strength of association of a drug-ADR pair within each individual patient case. Using the causal-leverage measure, we develop a data mining algorithm to evaluate the associations between a given drug enalapril and all potential ADRs in a real-world electronic health database. The experimental results have shown that our approach can effectively shortlist some known ADRs. For example, the known ADR hyperkalemia caused by enalapril was ranked as top 1% among all the 3954 potential ADRs in our database.