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

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

ABSTRACT:

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 top10 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.

EXISTING SYSTEM:

Finding causal associations between two events or sets of events with relatively low frequency is very useful for various real-world applications. For example, a drug used at an appropriate dose may cause one or more adverse drug reactions (ADRs), although the probability is low. Discovering this kind of causal relationships can help us prevent or correct negative outcomes caused by its antecedents. In this system, we try to employ a knowledge-based approach to capture the degree of causality of an event pair within each sequence since the determination of causality is often ultimately application or domain dependent. We then develop an interestingness measure that incorporates the causalities across all the sequences in a database. Our study was motivated by the need of discovering ADR signals in postmarketing surveillance, even though the proposed framework can be applied to many different applications.

DISADVANTAGES OF EXISTING SYSTEM:

However, mining these relationships is challenging due to the difficulty of capturing causality among events and the infrequent nature of the events of interest in these applications. They can complicate a patient’s medical condition or contribute to increased morbidity, even death.  The current approach may require years to identify and withdraw problematic drugs from the market, and result in unnecessary mortality, morbidity, and cost of healthcare.

PROPOSED SYSTEM:

In this proposed system, we focus on mining infrequent causal associations.

1. We developed and incorporated an exclusion mechanism that can effectively reduce the undesirable effects caused by frequent events. Our new measure is named exclusive causal-leverage measure.

2. We proposed a data mining algorithm to mine ADR signal pairs from electronic patient database based on the new measure. The algorithm’s computational complexity is analyzed.

3. We compared our new exclusive causal-leverage measure with our previously proposed causal-leverage measure as well as two traditional measures in the literature: leverage and risk ratio.

4. To establish the superiority of our new measure, we did extensive experiments. In our previous work, we tested the effectiveness of the causal-leverage measure using a single drug in the experiment.

ADVANTAGES OF PROPOSED SYSTEM:

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.

SYSTEM ARCHITECTURE:

ALGORITHMS USED:

ü Algorithm 1. Searching for drugs and the support count for each drug

ü Algorithm 2. Pair (Candidate Rule) Generation and Evaluation

ü Algorithm 3. Procedure causal-leverage(X,Y,PIDs)

SYSTEM CONFIGURATION:-

HARDWARE CONFIGURATION:-

ü Processor             -        Pentium –IV

ü Speed                             -        1.1 Ghz

ü RAM                    -        256 MB(min)

ü Hard Disk            -        20 GB

ü Key Board            -        Standard Windows Keyboard

ü Mouse                  -        Two or Three Button Mouse

ü Monitor                -        SVGA

 

SOFTWARE CONFIGURATION:-

ü Operating System                     : Windows XP

ü Programming Language           : JAVA

ü Java Version                           : JDK 1.6 & above.

REFERENCE:

Yanqing Ji, Hao Ying, Fellow, IEEE, John Tran, Peter Dews, Ayman Mansour, and R. Michael Massanari-“ A Method for Mining Infrequent Causal Associations and Its Application in Finding Adverse Drug Reaction Signal Pairs”- IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 25, NO. 4, APRIL 2013.