Data mining for improved cardiac care

Abstract

Cardiovascular Disease (CVD) is the single largest killer in the world. Although, several CVD treatment guidelines have been developed to improve quality of care and reduce healthcare costs, for a number of reasons, adherence to these guidelines remains poor. Further, due to the extremely poor quality of data in medical patient records, most of today's healthcare IT systems cannot provide significant support to improve the quality of CVD care (particularly in chronic CVD situations which contribute to the majority of costs).We present REMIND, a Probabilistic framework for Reliable Extraction and Meaningful Inference from Nonstructured Data. REMIND integrates the structured and unstructured clinical data in patient records to automatically create high-quality structured clinical data. There are two principal factors that enable REMIND to overcome the barriers associated with inference from medical records. First, patient data is highly redundant -- exploiting this redundancy allows us to deal with the inherent errors in the data. Second, REMIND performs inference based on external medical domain knowledge to combine data from multiple sources and to enforce consistency between different medical conclusions drawn from the data -- via a probabilistic reasoning framework that overcomes the incomplete, inconsistent, and incorrect nature of data in medical patient records.This high-quality structuring allows existing patient records to be mined to support guideline compliance and to improve patient care. However, once REMIND is configured for an institution's data repository, many other important clinical applications are also enabled, including: quality assurance; therapy selection for individual patients; automated patient identification for clinical trials; data extraction for research studies; and to relate financial and clinical factors. REMIND provides value across the continuum of healthcare, ranging from small physician practice databases to the most complex hospital IT systems, from acute cardiac care to chronic CVD management, and to experimental research studies. REMIND is currently deployed across multiple disease areas over a total of 5,000,000 patients across the US.

Predict the Diagnosis of Heart Disease Patients Using Classification Mining Techniques

“…Numerous works [3,2] related to heart disease diagnosis using data mining techniques have motivated this study. The dataset, algorithms, methods used by the authors and the observed results along with the future work are studied for each paper.…”

Data Mining System and Applications: A Review

“…The medical data is complex and difficult to analyze. A REMIND (Reliable Extraction and Meaningful Inference from Non-structured Data) system [21] integrates the structured and unstructured clinical data in patient records to automatically create high quality structured clinical data. The high quality of structuring allows existing patient records to be mined to support guidelines compliance and to improve patient care.…”

“…The high quality of structuring allows existing patient records to be mined to support guidelines compliance and to improve patient care. [21] Data mining in distance learning automatically generate useful information to enhance the learning process based on the vast amount of data generated by the tutors and student's interactions with web based distance-learning environment. [18] The Data Mining Applications transfers the data into information and feedback to the e-learning environment.…”

Classification of Imbalanced Data Using a Modified Fuzzy-Neighbor Weighted Approach

“…Raeder mentioned it mentioned it specifically again in [5]. Examples of real world imbalanced datasets are Medical Diagnosis [6,7], Oil-spill Detection [8], Credit Card Fraud Detection, Network Intrusion, Text Categorization, Helicopter Gearbox Fault Monitoring [9] etc.…”