Paul Kallukaran, IMS America [Division of Cognizant], Plymouth Meeting, PA USA
Jerry Kagan, IMS America [Division of Cognizant], Plymouth Meeting, PA USA
IMS America, a division of Cognizant Corporation, is the principal source of informationused in marketing and sales management by health care organizations throughout the UnitedStates. Of the various potential applications of neural networks, pattern recognition isconsidered one of major importance. This paper presents the results of using neuralnetworks to classify time-series data into several trend pattern classifications [e.g.,Increasing Trend, Decreasing Trend, Shift Up, Shift Down, Spike Up, Spike Down, and NoPattern], and the information generated from the classifier is used to detect variousmarketing related phenomena [e.g., Brand Switching, Brand Loyalty, and Product Trends]. The data used for the test consisted of prescription data for 12 months, for 600,000prescribers writing four drugs in the Anti-ulcer market. Using the neural network classifierand brand switching algorithm, the system was able to detect 2500 prescribers who werechanging their prescribing behavior. The model is a promising formula for analyzing times-series information from extremely large databases, and presenting the user with onlyinformation relevant for decision making. This data-mining system, designed as part of theIMS Xplorer product, uses SAS System components for data retrieval, datapreparation, graphical user interface, and data visualization. The IMS Xplorer productis a sales and marketing decision support system based on commercially-available, client-server technology created for pharmaceutical companies in their effort to fully utilize themission critical information found in the Xplorer data warehouse. KEY WORDS: Time-Series Data, Neural Networks, and Data Mining Introduction
cluster and discriminant analysistechniques.
During the past decade there has been asignificant increase in the use of artificial In fact, there is research to indicate that control, predictive modeling, data analysis, pattern recognition and signal processing traditional statistical methods in the areas (Nibset, Mclaughlin, and Mulgrew 1991).
of forecasting (Sharda and Patil 1992).
well established in applied fields, where neural networks are recognized as flexible and powerful tools for solving prediction alternatives to the more traditional time-series analysis (de Groot and Wuertz 1991), linear and nonlinear model fitting into various groups [e.g., IncreasingTrend, Decreasing Trend, Level Shift Up, Up]. The results from this neural network products and are also used at IMS to detect prescribers that have changed theirprescribing behaviors. Other statistical The Xponent database, in the last couple etc.] can then use the classifications from becomes extremely difficult to identifyphysicians changing their prescribing Database Background
behaviors. The neural-network modelprovides a method of analyzing times- series data and identifying physicians that are growing at an alarming rate. As these over time. The method provides a tool for effectiveness, data-mining techniques gain each prescriber, yields an annual gain of$52 million in sales. So, if you’re not 175,000 sites across the United States.
physicians, pharmacists, veterinarians,drugstores, hospitals, distributors, and Applications of the Data Mining
Technique for Targeting
including computer tape, microfilm,purchase invoices and surveys.
Xponent, the first true physician level impossible for human analysts to visually database for the health-care industry, is examine all of the time-series data and to classify time-series data into the various prescribing activity by using a customized projection factor for each prescriber.
collected over successive time intervals.
The data can be product volumes for a Xplorer is a customized decision support particular market or prescription data for model is applicable regardless of the data research analysts, economists, behavioral allow clients to access the data, conduct a scientists, security analysts, and others, broad range of analyses, and produce easy- study time series to gain an understanding of general market trends and to takesubsequent action based on the trends.
developed to classify time-series data into various trend patterns and to detect various performance. For our data-mining test, themodel was used to detect physicians that Implementation of a Data-
Mining Solution Using the SAS System
product B, C, or D over a 12 month period.
The data used for the test consists of 600,000 prescribers, writing four drugs in the Anti-ulcer market. The purpose of the tool had to have the ability to operate in a client/server environment, to have access Table One: SAS System Components Used in the Project
1. SAS/AF ®
All the graphical user screens , to select type of analysis,data elements and viewing the final results.
Connectivity to the UNIX server and interface to theOracle Database from Client. [Windows 95] 3. SAS/ BASE ®
All data preparation, transformation and statistics.
All the graphical reports for data visualization.
switching brands. The results of the test transformation and statistical functions,and to furnish the user with several were changing their prescribing behavior.
physician that was previously loyal to the drug A, and in the last 5 months switched to prescribing drug B. This reportprovides a useful tool for thepharmaceutical company’s sales-force touse when targeting the right prescribers for Table Two: List of Information Provided by the Client to Run the Model
Time Periods
Doctor Specialty
Payment Type
Distribution Channel
For a typical analysis, the user may specify 5.0 Conclusion
the examination and analysis of 600,000time series would take weeks of work. By After the criteria are selected for the data- person can use this model to target doctors analysis are shielded from the end user.
First, the analysis uses SAS to build SQL selling and to devise a specific message to database residing on the server, andtransforms and manipulates the data set to provide the correct input formats for the preprocessing capabilities, and offering the the prescribing of one medication over the visualization. SAS provides all the tools prescribing of another. The final results required to implement such a system.
from the analysis are saved in a SAS dataset. The complete analysis is performed To help the user to understand the factors various statistical techniques [Multinomial producing a final SAS data set about 2,500 classifications from the neural-networkmodel as a response variable. In the future, IMS plans to implement additional support system, to help the business user reports. The entire process is seamlessly within the mountains of captured data.
integrated with the SAS system, giving theuser complete flexibility in running theprocess without knowing any SAS.
Lapedes, A., and Farber, R. (1987),“Nonlinear Signal Processing Using Operations Research from Illinois Institute of Technology in Chicago. Sincegraduation, he has worked in the expertise in statistics, operations research, and artificial intelligence. Currently, he is Application,” Proceedings of the Eight Nelson, M., and Illingworth, W. T. (1991), A practical Guide to Neural Nets, New York, NY: Addison Wesley PublishingCompany, Inc.
Jerry Kagan is a Senior Programmer in theAdvanced Analytics Group at IMS and Neural Works Explorer, Pittsburgh SAS system for the past nine years,working primarily in the pharmaceutical development. Jerry has been an invitedand contributing speaker at SUGI and (1992), “Neural Networks As anAlternative to Statistical Modeling of Contacts
Radio Wave Propagation”, AmericanStatistical Association Winter Conference, Acknowledgments
Institute Inc. in the USA and othercountries. ® indicates USA registration deGroot, C., and Wuertz, D. (1991),“Analysis of Univariate Time series with registered trademarks of their respective Classical Examples,” Neurocomputing, 3,4 Figure One: Physician Targeting Report
Doctor Targeting Report
Company: Lily
Market: Anti-Depressant
September 1994 - August 1995
Product: Prozac
Doctor: John Smith
Market Share
Rx Volume
Speciality: OBSTETRICS
Number of Samples:
Number of Details:
Product Switched:
Reasons for Switch:
Less Drug Interactions, More Cost effective Market Share By Product/Doctor
Rx Volume By Product/Doctor
Rx Volume
Appendix A


BIJLAGE A: Formulier voor aanvraag tot terugbetaling van de specialiteit SEEBRI (§ 6690000 van hoofdstuk IV van het K.B. van 21 december 2001) I – Identificatie van de rechthebbende (naam, voornaam, inschrijvingsnummer bij de V.I.): II – Elementen te bevestigen door de geneesheer: Ik ondergetekende, dokter in de geneeskunde, attesteer dat de hierboven vermelde patiënt aan COPD van


Short Communications and Brief Case NotesSHORT COMMUNICATIONS AND BRIEF CASE NOTESthat the determination of specifi c IgE, and especially IgG, to Fatal Intraoperative Anaphylaxis After aprotinin should be evaluated in patients with prior contact Aprotinin Administration with this peptide. Patients with high antibody titers should be considered at risk, whereas the absence of aprotinin-s

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