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Abstract
A number of approaches have been proposed in literature to collect and classify patient related information for purpose of better clinical diagnosis and thus safer treatment and administration of related activities. This type of data collection and classification benefits doctors and the corresponding hospitals. However, no effort is made, as to our knowledge, to classify accumulated data within insurance company databases to facilitate doctors as well as insurance companies for better analysis and cost-effective treatment of patients suffering from chronic (and expensive to treat) diseases such as related to oncology. In this study, a customized self-organized data classification model is applied to an insurance company database to build clusters based on age, patient condition, tests done, etc. These clusters provide integrated analysis to doctors in providing patient-specific, disease-specific, etc., and thus cost-effective treatment. On the other side, it saves on costs to be incurred on repeated tests to be done on the patient. An experimental setup is developed to train such a network, and testing results are presented. The practical constraints are also discussed.
Highlights
A number of improvements in hospitals have led to implementing automated ordering and dispensing systems [1] resulting in more time in patient care activities, performance improvement, savings, etc
Self-organizing maps differ from other artificial neural networks as they apply computationally convenient competitive learning approach using a neighborhood function, in order to preserve the topological properties of the input space
As a re-focus on objectives, an analysis platform to enable integrated and wider view on patients clinical information retrieved from insurance company database, was presented to facilitate doctors for in-depth medical analysis, and for insurance companies to benefit cost-effectiveness
Summary
A number of improvements in hospitals have led to implementing automated ordering and dispensing systems [1] resulting in more time in patient care activities, performance improvement, savings, etc. Patients’ records typically vary in a multitude of ways, some of which include diagnosis, severity of illness, medical complications, and the speed of recovery, resource consumption, lab tests, discharge destination, and social circumstances Such data is classified for various purposes, mostly within hospital domain. In another research [7], the authors propose an approach that combines rulebased features and knowledge-guided learning models for effective disease classification The classification of such data may be used for the purpose of analysis, planning, decision making, etc., and this area is active research subject in many disciplines, such as neural networks. Insurance investigation of patient cases, with special reference to oncology involves intensive, long term and phase wise collection of patient data This helps insurance companies to determine the type and cost of tests, diagnostics, and treatment conducted in the hospital per patient per physician per hospital.
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