TY  - JOUR
TI  - Improving Clinical Efficiency and Reducing Medical Errors through NLP-enabled Diagnosis of Health Conditions from Transcription Reports
AU  - Krish Maniar
AU  -  Shafin Haque
AU  -  Kabir Ramzan
JO  - International Journal of Scientific Research in Computer Science, Engineering and Information Technology
PB  - Technoscience Academy
DA  - 2022/06/30
PY  - 2022
DO  - https://doi.org/10.32628/CSEIT2283113
UR  - https://ijsrcseit.com/CSEIT2283113
VL  - 8
IS  - 3
SP  - 435	
EP  - 442
AB  - 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 CNN-LSTM 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.