Rule Extraction, Fuzzy ARTMAP, and Medical Databases

This paper shows how knowledge, in the form of fuzzy rules, can be derived from a self-organizing supervised learning neural network called fuzzy ARTMAP. Rule extraction proceeds in two stages: pruning removes those recognition nodes whose confidence index falls below a selected threshold; and quantization of continuous learned weights allows the final system state to be translated into a usable set of rules. Simulations on a medical prediction problem, the Pima Indian Diabetes (PID) database, illustrate the method. In the simulations, pruned networks about 1/3 the size of the original actually show improved performance. Quantization yields comprehensible rules with only slight degradation in test set prediction performance.British Petroleum (89-A-1204); Defense Advanced Research Projects Agency (AFOSR-90-0083, ONR-N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (90-0083); Institute of Systems Science (National University of Singapore

Mathematical techniques for the protection of patient's privacy in medical databases

In modern society, keeping the balance between privacy and public access to information is becoming a widespread problem more and more often. Valid data is crucial for many kinds of research, but the public good should not be achieved at the expense of individuals. While creating a central database of patients, the CSIOZ wishes to provide statistical information for selected institutions. However, there are some plans to extend the access by providing the statistics to researchers or even to citizens. This might pose a significant risk of disclosure of some private, sensitive information about individuals. This report proposes some methods to prevent data leaks. One category of suggestions is based on the idea of modifying statistics, so that they would maintain importance for statisticians and at the same time guarantee the protection of patient's privacy. Another group of proposed mechanisms, though sometimes difficult to implement, enables one to obtain precise statistics, while restricting such queries which might reveal sensitive information

Heath Network Clinicians Use and Need for Clinical Information Sources: Results of a Survey

The Dana Medical Library, part of the University of Libraries system, with the support of the University of Vermont Health Network, assessed the needs of UVM Health Network clinicians for evidence-based clinical resources such as: medical databases, journal articles, topic summaries, or electronic/print textbooks. Results are compiled and discussed

Applying Data Mining Techniques to Medical Databases

The data mining techniques such as Neural Network, Naïve Bayes, and Association rules are at present not well explored on medical databases. In this paper, we present and analyze our experimental results on thrombosis medical database by employing data mining tool of XLMiner and using different data mining techniques such as Naive Bayes and Neural Network for classification, Association rules, and Neural Network and K-Nearest Neighbors for prediction. As seen from experiments, some results are common across various mining techniques while others are unique

The effect of missing values using genetic programming on evolvable diagnosis

Medical databases usually contain missing values due the policy of reducing stress and harm to the patient. In practice missing values has been a problem mainly due to the necessity to evaluate mathematical equations obtained by genetic programming. The solution to this problem is to use fill in methods to estimate the missing values. This paper analyses three fill in methods: (1) attribute means, (2) conditional means, and (3) random number generation. The methods are evaluated using sensitivity, specificity, and entropy to explain the exchange in knowledge of the results. The results are illustrated based on the breast cancer database. Conditional means produced the best fill in experimental results

Comparison of distance metrics for hierarchical data in medical databases

Distance metrics are broadly used in different research areas and applications, such as bio- informatics, data mining and many other fields. However, there are some metrics, like pq-gram and Edit Distance used specifically for data with a hierarchical structure. Other metrics used for non- hierarchical data are the geometric and Hamming metrics. We have applied these metrics to The Health Improvement Network (THIN) database which has some hierarchical data. The THIN data has to be converted into a tree-like structure for the first group of metrics. For the second group of metrics, the data are converted into a frequency table or matrix, then for all metrics, all distances are found and normalised. Based on this particular data set, our research question: which of these metrics is useful for THIN data? This paper compares the metrics, particularly the pqgram metric on finding the similarities of patients’ data. It also investigates the similar patients who have the same close distances as well as the metrics suitability for clustering the whole patient population. Our results show that the two groups of metrics perform differently as they represent different structures of the data. Nevertheless, all the metrics could represent some similar data of patients as well as discriminate sufficiently well in clustering the patient population using k-means clustering algorithm