Extraction of relevant pathological terms from radiology reports is important for correct image label generation and disease population studies. In this letter we compare the performance of some known application program interface (APIs) for the task of thoracic abnormality extraction from radiology reports. We explored several medical domain specific annotation tools like Medical Text Indexer(MTI) with Non-MEDLINE and Mesh On Demand(MOD) options and generic Natural Language Understanding (NLU) API provided by the IBM cloud. Our results show that although MTI and MOD are intended for extracting medical terms, their performance is worst compared to generic extraction API like IBM NLU. Finally, we trained a DNN-based Named Entity Recognition (NER) model to extract the key concept words from radiology reports. Our model outperforms the medical specific and generic API performance by a large margin. Our results demonstrate the inadequacy of generic APIs for pathology extraction task and establishes the importance of domain specific model training for improved results. We hope that these results motivate the research community to release larger de-identified radiology reports corpus for building high accuracy machine learning models for the important task of pathology extraction.
Mostafiz and Ashraf REF trained a DNN-based Named Entity Recognition (NER) model to extract the key concept words from radiology reports, which results demonstrate the inadequacy of generic APIs for pathology extraction task and establishes the importance of domain specific model training for improved results.
Pathology Extraction from Chest X-Ray Radiology Reports: A Performance Study
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Computer Science