论文标题
通过转录报告对健康状况的诊断来提高临床效率并减少医疗错误
Improving Clinical Efficiency and Reducing Medical Errors through NLP-enabled diagnosis of Health Conditions from Transcription Reports
论文作者
论文摘要
误诊率是医院医疗错误的主要原因之一，影响了美国超过1200万成年人。为了解决高诊断率的高率，本研究利用4种基于NLP的算法根据非结构化转录报告来确定适当的健康状况。从逻辑回归，随机森林，LSTM和CNNLSTM模型中，CNN-LSTM模型的表现最佳，精度为97.89％。我们将此模型打包到了经过身份验证的网络平台中，以向临床医生提供可访问的援助。总体而言，通过标准化医疗保健诊断和结构转录报告，我们的NLP平台极大地提高了全球医院的临床效率和准确性。
Misdiagnosis rates are one of the leading causes of medical errors in hospitals, affecting over 12 million adults across the US. To address the high rate of misdiagnosis, this study utilizes 4 NLP-based algorithms to determine the appropriate health condition based on an unstructured transcription report. From the Logistic Regression, Random Forest, LSTM, and CNNLSTM models, the CNN-LSTM model performed the best with an accuracy of 97.89%. We packaged this model into a authenticated web platform for accessible assistance to clinicians. Overall, by standardizing health care diagnosis and structuring transcription reports, our NLP platform drastically improves the clinical efficiency and accuracy of hospitals worldwide.
