Predicting lab values for gastrointestinal bleeding patients in the intensive care unit: A comparative study on the impact of comorbidities and medications

Golnar K Mahani, Mohammad-Reza Pajoohan
Artificial Intelligence in Medicine 2019, 94: 79-87
Since a significant number of frequent laboratory blood tests are unnecessary and these tests may have complications, developing a system that could identify unnecessary tests is essential. In this paper, a value prediction approach is presented to predict the values of Calcium and Hematocrit laboratory blood tests for upper gastrointestinal bleeding patients and patients with unspecified hemorrhage in their gastrointestinal tract. The data have been extracted from the MIMIC-II database. By considering the issues of MIMIC-II in the process of data extraction and using expert knowledge, comprehensive preprocessing has been performed to validate the data. The first prediction system is developed using zero order Takagi-Sugeno fuzzy modeling and the sequential forward selection method. The results of this prediction system for target laboratory tests are promising. In the second proposed prediction system, patients are clustered using their comorbidity information before the final prediction phase. For each cluster, a medication feature is constructed and added to the data for the final feature selection. Although it was expected that clustering patients based on comorbidity data could improve the results of value prediction, the results were not improved in average. The reason for this could be the small number of abnormal laboratory test samples and their dispersion in clusters. These abnormal values would be more dispersed in the model with clustering phase, when they are scattered over different clusters.


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