Medical decision making: probabilistic medical reasoning
Medical informatics: computer applications in health care
Neural expert system using fuzzy teaching input and its application to medical diagnosis
Information Sciences—Applications: An International Journal
Extending naïve Bayes classifiers using long itemsets
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Inductive Learning for Case-Based Diagnosis with Multiple Faults
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Masquerade Detection Using Truncated Command Lines
DSN '02 Proceedings of the 2002 International Conference on Dependable Systems and Networks
Extension of the HEPAR II Model to Multiple-Disorder Diagnosis
Proceedings of the IIS'2000 Symposium on Intelligent Information Systems
Functional Evaluation of SETH: An Expert System In Clinical Toxicology
AIME '95 Proceedings of the 5th Conference on Artificial Intelligence in Medicine in Europe: Artificial Intelligence Medicine
A Comparison Study on Algorithms for Incremental Update of Frequent Sequences
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
Principles of human-computer collaboration for knowledge discovery in science
Artificial Intelligence
Autonomous decision-making: a data mining approach
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
Uniqueness of medical data mining
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Mining association rules with improved semantics in medical databases
Artificial Intelligence in Medicine
A knowledge-based clinical toxicology consultant for diagnosing multiple exposures
Artificial Intelligence in Medicine
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Objective: Every year, toxic exposures kill 1200 Americans. To aid in the timely diagnosis and treatment of such exposures, this research investigates the feasibility of a knowledge-based system capable of generating differential diagnoses for human exposures involving unknown toxins. Methods: Data mining techniques automatically extract prior probabilities and likelihood ratios from a database managed by the Florida Poison Information Center. Using observed clinical effects, the trained system produces a ranked list of plausible toxic exposures. The resulting system was evaluated using 30,152 single exposure cases. In addition, the effects of two filters for refining diagnosis based on a minimum number of exposure cases and a minimum number of clinical effects were also explored. Results: The system achieved accuracies (calculated as the percentage of exposures correctly identified in top 10% of trained diagnoses) as high as 79.8% when diagnosing by substance and 78.9% when diagnosing by the major and minor categories of toxins. Conclusions: The results of this research are modest, yet promising. At this time, no similar systems are currently in use in the United States and it is hoped that these studies will yield an effective medical decision support system for clinical toxicology.