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README.md
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- text: 'Impression: fever, chills, cough, chest pain, shortness of breath, N/V.'
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example_title: Example 2
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---
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# DX
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##
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This model is
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## Model Details
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---
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license: apache-2.0
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language:
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tags:
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- text: 'Impression: fever, chills, cough, chest pain, shortness of breath, N/V.'
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example_title: Example 2
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# ICD-10 DX Code Identification Model
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## Overview
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This model is designed for the identification of tokens related to ICD-10 DX codes in clinical documents. We focus on a subset of approximately 4,000+ codes,
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which are the most frequently used in clinical documentation. Please refer config.json file for target codes we used to train this model.
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## Model Details
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- **Type**: Named Entity Recognition (NER)
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- **Target**: ICD-10 DX Codes
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- **Code Subset**: 4,000+ most common codes
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## Dataset
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The dataset comprises clinical documents annotated for ICD-10 DX codes. We ensure a balanced representation of the selected codes to prevent model bias.
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the dataset is private one, used internally to trian the model.
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## Training
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Due to GPU memory constraints, training is conducted in epochs with periodic evaluations to monitor performance and mitigate overfitting.
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# Use a pipeline as a high-level helper
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from transformers import pipeline
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pipe = pipeline("token-classification", model="imperiumhf/imp_clinical_dxcode_ner_v2")
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## Evaluation
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Need to update metrics
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## Limitations and Considerations
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- Overfitting risk due to repeated training on the same dataset.
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- The balance between model complexity and the large number of classes.
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- Regular model evaluation for performance monitoring.
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## Contact
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## Acknowledgements
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All the rights over this model is reserved for Imperium software solutions pvt ltd.
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