论文标题
使用统计和神经语言建模的临床预测键盘
Clinical Predictive Keyboard using Statistical and Neural Language Modeling
论文作者
论文摘要
语言模型可用于预测在创作过程中，纠正拼写或加速写作（例如，在SMS或电子邮件中）。但是，语言模型仅在很小的范围内应用于在创作过程中协助医师（例如出院摘要或放射学报告）。但是，在基于计算机的医师的帮助下，加快患者出口的基于计算机的系统，还有助于减少医院的感染。我们采用统计和神经语言建模来预测临床文本的下一个单词，并在两个数据集中与放射学报告中的两个数据集中评估所有模型。我们表明，在放射学报告中，神经语言模型的准确性高达51.3％（正确预测的两个单词中的一个）。我们还表明，即使模型仅用于频繁单词，医师也可以节省宝贵的时间。
A language model can be used to predict the next word during authoring, to correct spelling or to accelerate writing (e.g., in sms or emails). Language models, however, have only been applied in a very small scale to assist physicians during authoring (e.g., discharge summaries or radiology reports). But along with the assistance to the physician, computer-based systems which expedite the patient's exit also assist in decreasing the hospital infections. We employed statistical and neural language modeling to predict the next word of a clinical text and assess all the models in terms of accuracy and keystroke discount in two datasets with radiology reports. We show that a neural language model can achieve as high as 51.3% accuracy in radiology reports (one out of two words predicted correctly). We also show that even when the models are employed only for frequent words, the physician can save valuable time.
