Developing a Portable Natural Language Processing Based Phenotyping System
Table 8: The classification results for major classes on all CUIs corresponding to the original records
['Intuitive', 'Intuitive P-Micro', 'Intuitive P-Macro', 'Intuitive R-Micro', 'Intuitive R-Macro', 'Intuitive F-Micro', 'Intuitive F-Macro']
Due to the limitation of machine learning methods on small samples, in this section, we perform the classification only on the major classes that have enough samples to train a machine learning model. For intuitive judgments, we only use the ‘Present’ and ‘Absent’ records in the training data to train the classification model. For textual judgments, we only consider the ‘Present’ and ‘Unmentioned’ records. In addition, by combining the rule-based approach and machine learning based approaches, we can achieve a comparable classification performance with the total rule-based approach, and more importantly, this method can be portable between different HER systems.by combining the rule-based approach and machine learning based approaches, we can achieve a comparable classification performance with the total rule-based approach, and more importantly, this method can be portable between different EHR systems. This is as expected due to the limitation of machine learning methods on small samples. Thus, in our portable phenotyping system, we can use the rule-based method for the minor class classification and use machine learning methods for the major class classification.