Misdiagnosis rates are one of the leading causes medical errors in hospitals, affecting over 12 million adults across US. To address high rate misdiagnosis, this study utilizes 4 NLP-based algorithms to determine appropriate health condition based on an unstructured transcription report. From Logistic Regression, Random Forest, LSTM, and CNNLSTM models, CNN-LSTM model performed best with accura...
