Ayman Mansour

A Potential Causal Association Mining Algorithm for Screening Adverse Drug Reactions in Postmarketing Surveillance

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.

A Method for Mining Infrequent Causal Associations and Its Application in Finding Adverse Drug Reaction Signal Pairs

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.

A fuzzy recognition-primed decision model-based causal association mining algorithm for detecting adverse drug reactions in postmarketing surveillance

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 [1] 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.

Mining Infrequent Causal Associations in Electronic Health Databases

Discovering infrequent 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 datasets 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 exclusive causal-leverage was employed to rank the potential causal associations between each of the two selected drugs (i.e., enalapril and pravastatin) and 3,954 recorded symptoms, each of which corresponds to a potential ADR. The top 10 drug-symptom pairs for each drug were evaluated by our physicians on the project team. The results showed that the number of symptoms considered as real ADRs for enalapril and pravastatin was 8 and 7 out of 10, respectively.

Finding similar patients in a multi-agent environment

Finding similar patients is highly desirable in many clinical applications. In this paper, we address the issue of how to find similar patients in a multi-agent environment where software agents, located in different places, work collaboratively and proactively help one another to empower their human users to achieve a common healthcare goal. We show how the agents, equipped with fuzzy similarity rules developed by the physicians on the team, collaborate to find similar patients in each agents patient database. We describe the architecture, design and implementation of the system. Using the popular agent language JADE and clinical information on 1,000 patients treated at the Detroit Veterans Affairs Medical Center, we have implemented a five-agent system and generated some preliminary simulation results.

Identifying adverse drug reaction signal pairs by a multi-agent intelligent system with fuzzy decision model

Several thousands of drugs are currently available on the U.S. market. A complete understanding of the safe use of drugs is not possible at the time when drug is developed or marketed. At that time, the safety information is only obtained from a few thousand people in a typical pre-marketing clinical trial. Clinical trials are not capable of detecting rare adverse drug reactions (ADRs) because of limitations in sample size and trial duration. Early detection of unknown ADRs could save lives and prevent unnecessary hospitalizations. Current methods largely rely on spontaneous reports which suffer from serious underreporting, latency, and inconsistent reporting. Thus they are not ideal for rapidly identifying rare ADRs. In this paper we propose a team-based multi-agent intelligent system approach for proactively detecting potential ADR signal pairs (i.e., potential links between drugs and apparent adverse reactions). The basic idea is that intelligent agents are capable of collaborating with one another by sharing information and knowledge which will accelerate the process of detecting ADR signal pairs. Each agent is equipped with a fuzzy inference engine, which enables it to find the causal relationship between a drug and a potential ADR (i.e., a signal pair). To evaluate our approach, we designed a four-agent system and implemented it using JADE and FuzzyJess software packages. We choose four because it is representative enough while computing time is still reasonable. To assess the performance of the proposed system, we conducted a simulation experiment that involved over 10,000 patients treated by the drug Lisinopril at the Veterans Affairs Medical Center in Detroit between 2005 and 2008. The preliminary results indicate that the agents can successfully collaborate in finding signal pairs.

An exclusive causal-leverage measure for detecting adverse drug reactions from electronic medical records

Early detection of causal relationships between drugs and their associated adverse drug reactions (ADRs) can prevent harmful consequences or even deaths. Rare ADRs cannot be detected by pre-marketing clinical trials due to limitations in their size and duration. Existing postmarketing surveillance methods mainly rely on spontaneous reporting which is limited by severe underreporting (<10 percentage reporting rate), latency and inconsistency. In this paper, we propose to identify potential ADRs from electronic medical records which are accessible now in many hospitals. Specifically, we created a new interestingness measure, exclusive causal-leverage, based on a computational, fuzzy recognition-primed decision (RPD) model[1]. This measure extends our previous measure, called causal-leverage, and can more effectively reduce the effects of background noises in the data. On the basis of this new measure, a data mining algorithm was developed and 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). Experimental results showed that two known ADRs (i.e. hyperpotassemia and cough) associated with drug enalapril were ranked as 3 and 21, respectively, among all the 3,954 potential ADRs (ICD-9 codes) in our database.

A multi-agent system for detecting adverse drug reactions

Discovering unknown adverse drug reactions (ADRs) as early as possible is highly desirable. Current methods largely rely on passive spontaneous reports, which suffer from serious underreporting, latency, and inconsistent reporting. They are not ideal for early identification of ADRs [5]. In this paper, we propose a multi-agent system approach for ADR detection. A multi-agent system is formed by a community of agents that exchange information and proactively help one another to achieve the goals set by the system designer. We show how agents, equipped with decision rules developed by the physicians on the team, can collaborate to detect signal pairs of potential ADRs. Using the popular agent language JADE [8, 10] and clinical information on 1,000 patients treated at the Detroit Veterans Affairs Medical Center, we have constructed a small group of agents and generated preliminary simulated detection results.

A temporal interestingness measure for drug interaction signal detection in post-marketing surveillance.

Drug-drug interactions (DDIs) can result in serious consequences, including death. Existing methods for identifying potential DDIs in post-marketing surveillance primarily rely on the FDAs (Food and Drug Administration) spontaneous reporting system. However, this system suffers from severe underreporting, which makes it difficult to timely collect enough valid cases for statistical analysis. In this paper, we study how to signal potential DDIs using patient electronic health data. Specifically, we focus on discovery of potential DDIs by analyzing the temporal relationships between the concurrent use of two drugs of interest and the occurrences of various symptoms using novel temporal association mining techniques we developed. A new interestingness measure called functional temporal interest was proposed to assess the degrees of temporal association between two drugs of interest and each symptom. The measure was employed to screen potential DDIs from 21,405 electronic patient cases retrieved from the Veterans Affairs Medical Center in Detroit, Michigan. The preliminary results indicate the usefulness of our method in finding potential DDIs for further analysis (e.g., epidemiology study) and investigation (e.g., case review) by drug safety professionals.

Fuzzy Rule-Based Approach for Detecting Adverse Drug Reaction Signal Pairs

Detecting Adverse Drug Reactions (ADR) signal pairs is technically a complex problem. This is the case if we realistically assume that there does not exist a set of rules that are readily acceptable to all human experts (e.g., physicians, epidemiologists and pharmacists). The parameters used in identifying the signal pairs are really a vague, subjective measure rather than an objective measure. Furthermore, human experts often disagree one another owing to their knowledge and experiences and there is no “ground truth” to indicate which physician is right or wrong. Because of this and other limitations, current surveillance systems are not ideal for rapidly identifying rare unknown ADRs. A more effective system is needed as the electronic patient records become more and more easily accessible in various health organizations such as hospitals, medical centers and insurance companies. These data provide a new source of information that has great potentials to detect ADR signals much earlier. In this paper we have designed and developed a fuzzy inference engine for finding the causal relationship between a drug and an adverse reaction. The reasoning is based on a fuzzy inference system implemented using the freeware FuzzyJess. Fuzzy logic is used to represent, interpret, and compute vague and/or subjective information which is very common in medicine. The Detector is a fuzzy rulebased system. Using clinical information of more than 10,000 patients treated at the Detroit Veterans Affairs Medical Center, we have generated preliminary simulated detection results.