Human language is complex and often equivocal. Unsurprisingly, even the most sophisticated natural language processing (NLP) algorithms inevitably make mistakes. The impact of these mistakes on the results of clinical research that uses NLP-generated data as one of the inputs is uncertain. In this talk, Alexander Turchin will discuss a recent study that has demonstrated that real world evidence analyses are resilient to a moderate error rate in NLP-generated data, supporting the use of NLP in clinical research.
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