Data Mining by Mehmed Kantardzic (good book recommendations TXT) 📕

recording these medical claims, with a large number of missing values contributed to the poorly labeled data set. Instead of the larger 500,000 point data set, the authors were “forced” to rebuild a subset of thesee data. This required manual labeling of the subset.

The manual labeling would require a much smaller set of data points to be used from the original 500,000. To cope with a smaller set of data points, the problem was split into four smaller problems, namely identifying fraudulent medical claims, affiliates, medical professionals, and employers. Individual data sets were constructed for each of these four subtasks ranging in size from 2838 samples in the medical claims task to 394 samples in the employer subtask. For each subtask a manual selection of features was performed. This involved selecting only one feature from highly correlated features, replacing categorical features with numerical features, and design new features that “summarize temporal behavior over an extended time span.” The original 125 features were paired down to between 12 and 25 features depending on the subtask. Additionally, the output of all other subtasks became inputs to each subtask, thus providing feedback to each subtask. Last, 2% of outliers were removed and features were normalized.

When modeling the data it was found that initially the accuracy of a single neural network on these data sets could vary by as much as 8.4%. Instead of a single neural network for a particular data set, a committee of neural networks was used. Each data set was also divided into a training set, a validation set, and a testing set to avoid overfitting the data. At this point it was also decided that each of the four models would be retrained monthly to keep up with the ever evolving process of fraud.

Neural networks and committees of neural networks output scores rather than an absolute fraud classification. It was necessary that a threshold be set for the output. The threshold was decided after accounting for personnel costs, false alarm costs, and the cost of not detecting a particular instance of fraud. All of these factors figured into an ROC curve to decide upon acceptable false and true positive rates. When the medical claims model using the input of the other three subtasks scored a medical claim above the chosen threshold, then a classification of fraud is given to that claim. The system was tested on a historical data set of 8819 employers that contains 418 instances of fraud. After this historical data set was split into training, validation, and test set, the results showed that the system identified 73.4% of the true fraudsters and had a false positive rate of 6.9%.

The completed system was then run each night giving each new medical claim a fraud probability. The claims are then reviewed being sorted by the given probabilities. There were previously very few documented cases of fraud. After implementation there were approximately 75 rejected claims per month. These newly found cases of fraud accounted for nearly 10% of the raw overall costs to the company. Additionally, the culture of fraud detection changed. A taxonomy of the types of fraud was created and further improvements were made on the manual revision process. The savings covered the operational costs and increased the quality of health coverage.

Overall this project was a big success. The authors spent a lot of time first understanding the problem and second analyzing the data in detail, before the data was modeled. The final models produced were analyzed in terms of real business costs. In the end the results showed that the costs of the project were justified and Banmedica S.A. greatly benefited from the final system.

4.9.2 Improving Cardiac Care

CVD leads to nearly 1 million deaths (or 38% of all deaths) in the United States per year. Additionally, in 2005 the estimated cost of CVD was $394 billion compared with an estimated $190 billion on all cancers combined. CVD is a real problem that appears to be growing in the number of lives claimed and the percent of the population that will be directly affected by this disease. Certainly we can gain a better understanding of this disease. There already exist guidelines for the care of patients with CVD that were created by panels of experts. With the current load on the medical system, doctors are able to only spend a short amount of time with each patient. With the large number of guidelines that exists, it is not reasonable to expect that doctors will follow every guideline on every patient. Ideally a system would aid a doctor in following the given guidelines without adding additional overheads.

This case study outlines the use and deployment of a system called REMIND, which is meant both to find patients at need within the system, and to enable a better tracking of when patients are being cared for according to guidelines. Currently two main types of records are kept for each patient, financial and clinical. The financial records are used for billing. These records use standardized codes (e.g., ICD-9) for doctor assessments and drugs prescribed. This standardization makes it straightforward for computer systems to extract information from these records and used by data-mining processes. However, it has been found that these codes are accurate only 60–80% of the time for various reasons. One reason is that when these codes are used for billing, although two conditions are nearly identical in symptoms and prescriptions, the amount of money that will be paid out by an insurer may be very different. The other form of records kept is clinical records. Clinical records are made up of unstructured text, and allow for the transfer of knowledge about a patient’s condition and treatments from one doctor to another. These records are much more accurate, but are not in a form that is easily used by automated computer systems.

It is not possible that with great demands on the time of doctors and nurses that additional data may be recorded specifically for this system. Instead, the REMIND