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data may be recorded specifically for this system. Instead, the REMIND system combines data from all available systems. This includes extracting knowledge from the unstructured clinical records. The REMIND system combines all available sources of data present, then using redundancy in data the most likely state of the patient is found. For example to determine a patient is diabetic one may use any of the following pieces of data: billing code 250.xx for diabetes, a free text dictation identifying a diabetic diagnosis, a blood sugar value >300, a treatment of insulin or oral antidiabetic, or a common diabetic complication. The likelihood of the patient having diabetes increases as more relevant information is found. The REMIND system uses extracted information from all possible data sources, combined in a Bayesian network. The various outputs of the network are used along with temporal information to find the most probable sequence of states with a predefined disease progression modeled as a Markov model. The probabilities and structure of the Bayesian network are provided as domain knowledge provided beforehand by experts and tunable per deployment. The domain knowledge in the REMIND system is fairly simple as stated by the author of the system. Additionally, by using a large amount of redundancy the system performs well for a variety of probability settings and temporal settings for the disease progression. However, before a wide distribution of the REMIND system a careful tuning of all the parameters must take place.

One example deployment was to the South Carolina Heart Center where the goal was to identify among 61,027 patients those at risk of Sudden Cardiac Death (SCD). Patients who have previously suffered a myocardial infarction (MI; heart attack) are at the highest risk of SCD. In 1997 a study was performed on the efficacy of implantable cardioverter defribillators (ICDs). It was found that patients, with a prior MI and low ventricular function, had their 20-month mortality rate drop from 19.8% to 14.2%. This implantation is now a standard recommendation. Previous to the REMIND system one had two options to find who would require the implantation of an ICD. The first option is to manually review the records of all patients to identify those who were eligible for an ICD. This would be extremely time-consuming considering the large number of records. The other approach would be to evaluate the need for an ICD during regular checkups. However, not all patients come in for regular checkups and there would be a high chance that not every patient would be carefully considered for the need of an ICD. The REMIND system was given access to billing and demographics databases and transcribed free text including histories, physical reports, physician progress notes, and lab reports. From these data the REMIND system processed all records on a single laptop in 5 h and found 383 patients who qualified for an ICD.

To check the validity of the 383 found patients, 383 randomly chosen patients were mixed with the 383 found previously. Then 150 patients were chosen from the 766 patient samples. An electrophysiologist manually reviewed the 150 patients being blinded to the selection made by the REMIND system. The REMIND system concurred with the manual analysis in 94% (141/150) of the patients. The sensitivity was 99% (69/70) and the specificity was 90% (72/80). Thus it was shown that the REMIND system could fairly accurately identify at-risk patients in a large database. An expert was required to verify the results of the system. Additionally, all of the patients found would be reviewed by a physician before implantation would occur.

From the previous cases we see that a great deal of time was required from experts to prepare data for mining, and careful analysis of a model application needed to take place after deployment. Although applied data-mining techniques (neural and Bayesian networks) will be explained in the following chapters, the emphasis of these stories is on complexity of a data-mining process, and especially deployment