Manuscript Number : CSEIT2283113
Improving Clinical Efficiency and Reducing Medical Errors through NLP-enabled Diagnosis of Health Conditions from Transcription Reports
Authors(3) :-Krish Maniar, Shafin Haque, Kabir Ramzan 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.
Krish Maniar Natural Language Processing, Medical Tran-scription Notes(s), Diagnostic Systems, Recurrent Neural Net-work, Long Short-Term Memory Network Publication Details Published in : Volume 8 | Issue 3 | May-June 2022 Article Preview
The Harker School in Saratoga, California, United States
Shafin Haque
Saratoga High School in Saratoga, California, United States
Kabir Ramzan
The Harker School in Saratoga, California, United States
Date of Publication : 2022-06-30
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 435-442
Manuscript Number : CSEIT2283113
Publisher : Technoscience Academy
Journal URL : https://res.ijsrcseit.com/CSEIT2283113
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