Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- CAC
+- ICD10
+pretty_name: ICD10 DX Code Description
+---
+# ICD10 Diagnosis Description Mapping Dataset
+
+## Overview
+
+This dataset is designed to assist in mapping ICD10 Diagnosis descriptions documented in clinical documents to the standard ICD10 Diagnosis descriptions by CMS (Centers for Medicare & Medicaid Services). The primary objective is to train a model that can map free-form disease text to ICD Codes.
+
+## Dataset Details
+
+The dataset consists of the following columns:
+
+- **AnnotationString**: This column contains the disease text as described in patient clinical documents.
+- **DXCode**: This column contains the standard DX code corresponding to the description in the AnnotationString column.
+- **ShortDesc**: This column contains the standard ICD Description in a short form corresponding to the DXCode.
+- **LongDesc**: This column contains the standard ICD Description in a long form corresponding to the DXCode.
+
+## Model Training Objective
+
+The goal of this dataset is to train the LLAMA2 7B Model to generate one of the following outputs based on the provided AnnotationString as input:
+
+### Case 1: Expecting DX Code as Output
+
+- **Instruction**: As an ICD Code helper, the model needs to generate an ICD 10 DX Code for the corresponding disease description.
+- **Input**: `<Annotation String>`
+- **Output**: `<DXCode>`
+
+### Case 2: Expecting ShortDesc as Output
+
+- **Instruction**: As an ICD Code helper, the model needs to generate the standard ICD 10 DX Code Short Description for the corresponding clinical text.
+- **Input**: `<Annotation String>`
+- **Output**: `<ShortDesc>`
+
+### Case 3: Expecting LongDesc as Output
+
+- **Instruction**: As an ICD Code helper, the model needs to generate the standard ICD 10 DX Code Long Description for the corresponding clinical text.
+- **Input**: `<Annotation String>`
+- **Output**: `<LongDesc>`
+
+## Usage
+
+To utilize this dataset effectively for training the LLAMA2 7B Model, you can follow these steps:
+
+1. Preprocess your input data to match the format specified in the "Instruction" for the desired case (1, 2, or 3).
+2. Use the preprocessed data as input to your model.
+3. The model will generate the corresponding output (DXCode, ShortDesc, or LongDesc) based on the case you specified.
+
+## Example
+
+Here's an example of how to use this dataset and the model:
+
+### Input
+
+```python
+# Instruction: As a ICD Code helper, you need to generate ICD 10 Dx Code for corresponding following Disease description
+# Input: "Patient presents with persistent cough and fever."
+```
+
+### Output (Case 1)
+
+```python
+# Output: "J44.9"
+```
+
+### Output (Case 2)
+
+```python
+# Output: "Chronic obstructive pulmonary disease, unspecified"
+```
+
+### Output (Case 3)
+
+```python
+# Output: "Chronic obstructive pulmonary disease, unspecified"
+```
+
+## Citation
+
+If you use this dataset or the pre-trained model in your research or applications, please consider citing the source or authors for proper attribution.
+
+---
+
+Feel free to modify and expand this readme file to include any additional information or instructions specific to your use case.