  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.
