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The installation of electronic medical records transfers all free text to structured, drop-down boxes. When Cincinnati Children’s started using EPIC software paper forms were eliminated along with the free-text form fields. There is a great deal of knowledge in free-text and therefore a need to figure out how to extract it. We are attempting to overcome these obstacles by using natural language processing (NLP). Specifically, we are focused on developing and implementing neuro-cognitive algorithms that enable computers to understand the concepts and semantic relationships within clinical text. Already, we have developed a tool that anonymizes free-text and have used this tool to create a radiology corpus to support NLP research. Our next steps include further annotating the existing corpus, developing a second corpus, and using these corpora to train new, memory-based text processing algorithms.
Emotions are subjective as is their interpretation leading to a great amount of variations in how people interpret emotions. We investigated if machines can reach human-being competency in spotting emotions in text. We also wanted to upgrade the mainstream sentiment analysis from simple binary positive-negative classification to multilevel classification with 6 positive, 7 negative and two neutral emotions. We learned that ensemble classifiers can indeed reach human competency which is roughly 60% accuracy.
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