# Improving Clinical Efficiency and Reducing Medical Errors through NLP-enabled diagnosis of Health Conditions from Transcription Reports

arXiv: https://arxiv.org/abs/2206.13516 · HTML: https://mlsanity.com/paper/2206.13516/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 27 Jun 2022.

## Builds on (most cited first, 2 of 2)

- [A Combined CNN and LSTM Model for Arabic Sentiment Analysis](https://mlsanity.com/paper/1807.02911/) (2018)
- [Simple Recurrent Neural Networks is all we need for clinical events predictions using EHR data](https://mlsanity.com/paper/2110.00998/) (2021)

## Abstract (arXiv)

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.