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A major obstacle to the development of Natural Language Processing (NLP)methods in the biomedical domain is data accessibility. This problem can beaddressed by generating medical data artificially. Most previous studies havefocused on the generation of short clinical text, and evaluation of the datautility has been limited. We propose a generic methodology to guide thegeneration of clinical text with key phrases. We use the artificial data asadditional training data in two key biomedical NLP tasks: text classificationand temporal relation extraction. We show that artificially generated trainingdata used in conjunction with real training data can lead to performance boostsfor data-greedy neural network algorithms. We also demonstrate the usefulnessof the generated data for NLP setups where it fully replaces real trainingdata.

Is artificial data useful for biomedical Natural Language Processing algorithms?