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Intelligent_EHR_extract

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...
- the referring doctor

The system work as follow : 

<section align='center'>
    <img src='https://github.com/IKetchup/Intelligent_data_extraction_from_medical_reports/blob/main/images/schema.PNG?raw=true', height="400"/>
</section>

## Requirements

- Tesseract 5.0.0
...
    texts = texts + '\jump=================== New repport : ' + file + ' ===================\jump' + text
```
Visual output of ACABS segmentation: 

<section align='center'>
    <img src='https://github.com/IKetchup/Intelligent_data_extraction_from_medical_reports/blob/main/images/acabs_result_fancy.png?raw=true', height="500"/>
</section>

## Extraction of key information

After using ACABS to extract the text, the data need to be annotated (like [annotated_text.json](annotated_text.json)). In order to speed up the annotation use a software like  [ner-annotator](https://github.com/tecoholic/ner-annotator).
...
### Use a model

See [predictions.ipynb](predictions.ipynb)

<section align='center'>
    <img src='https://github.com/IKetchup/Intelligent_data_extraction_from_medical_reports/blob/main/images/pred.png?raw=true'/>
</section>

	a/README.md		b/README.md
	...		...
11	- the referring doctor	11	- the referring doctor
12		12
13	The system work as follow :	13	The system work as follow :
14		14
15	<section align='center'>	15	<section align='center'>
16	<img src='https://github.com/IKetchup/Intelligent_data_extraction_from_medical_reports/blob/main/images/schema.PNG', height="400"/>	16	<img src='https://github.com/IKetchup/Intelligent_data_extraction_from_medical_reports/blob/main/images/schema.PNG?raw=true', height="400"/>
17	</section>	17	</section>
18		18
19	## Requirements	19	## Requirements
20		20
21	- Tesseract 5.0.0	21	- Tesseract 5.0.0
	...		...
53	texts = texts + '\jump=================== New repport : ' + file + ' ===================\jump' + text	53	texts = texts + '\jump=================== New repport : ' + file + ' ===================\jump' + text
54	```	54	```
55	Visual output of ACABS segmentation:	55	Visual output of ACABS segmentation:
56		56
57	<section align='center'>	57	<section align='center'>
58	<img src='https://github.com/IKetchup/Intelligent_data_extraction_from_medical_reports/blob/main/images/acabs_result_fancy.png', height="500"/>	58	<img src='https://github.com/IKetchup/Intelligent_data_extraction_from_medical_reports/blob/main/images/acabs_result_fancy.png?raw=true', height="500"/>
59	</section>	59	</section>
60		60
61	## Extraction of key information	61	## Extraction of key information
62		62
63	After using ACABS to extract the text, the data need to be annotated (like [annotated_text.json](annotated_text.json)). In order to speed up the annotation use a software like [ner-annotator](https://github.com/tecoholic/ner-annotator).	63	After using ACABS to extract the text, the data need to be annotated (like [annotated_text.json](annotated_text.json)). In order to speed up the annotation use a software like [ner-annotator](https://github.com/tecoholic/ner-annotator).
	...		...
86	### Use a model	86	### Use a model
87		87
88	See [predictions.ipynb](predictions.ipynb)	88	See [predictions.ipynb](predictions.ipynb)
89		89
90	<section align='center'>	90	<section align='center'>
91	<img src='https://github.com/IKetchup/Intelligent_data_extraction_from_medical_reports/blob/main/images/pred.png'/>	91	<img src='https://github.com/IKetchup/Intelligent_data_extraction_from_medical_reports/blob/main/images/pred.png?raw=true'/>
92	</section>	92	</section>