基于深度学习的电子病历挖掘分析.zip

# Simple EMR System This code can be used for Electronic Medical Record Research. ## Getting Started First, have a look in the screenshots directory to become familiar with the interface design. ### Prerequisites Python 3 or Docker Compose ### Installing #### Python 1. Clone repository 2. cd into project directory 3. enter "pip install -r requirements.txt" #### Docker 1. Clone repository #### Python安装注意事项 强烈建议您使用虚拟环境。 1.如果没有virtualenv，输入“pip install virtualenv” 2.输入“python -m venv __name_of_environment__” 3.输入“__name_of_environment__/Scripts/activate” 4.然后按照上面显示的Python安装说明进行操作 5.要终止虚拟环境，输入“deactivate” ### 部署 ＃＃＃＃ Python 1. cd 进入项目目录 2.输入“python manage.py runserver” 3. 打开浏览器访问http://127.0.0.1:8000/SEMRinterface/ 4.使用ctrl+c终止 #### 码头工人 1. cd 进入项目目录 2.输入“docker-compose up” 3. 打开浏览器访问http://127.0.0.1:8000/SEMRinterface/ 4.使用ctrl+c终止 ### Notes The SEMRinterface in meant to run in full screen mode on a 1920 x 1080 resolution monitor. Responsive html is not currently supported. ## Secondary Use ### Included data Two example studies are included in the Simple EMR System repository: demo_study and synthea_study. Demo_study includes three synthetic patient cases created by scrambling the contents of de-identified records from a larger set of patients from a hospital’s Cerner EMR system. Synthea_study includes 25 intensive care unit (ICU) encounters from the Synthea COVID-19 dataset (available at https://synthea.mitre.org/downloads). Included case data can be found at: 1. https://github.com/ajk77/SimpleEMRSystem/tree/master/resources/synthea_study 2. https://github.com/ajk77/SimpleEMRSystem/tree/master/resources/demo_study (update coming soon) ### Importing custom patient data To preprocess Synthea cases for display on the Simple EMR System: 1. Download the source data and extract it into the ‘resources’ folder. 2. Adjust parameters, such as the input and output directories, in the Synthea data loading script (‘loaddata_synthea.py’) and execute the script. 3. View details about each processed case in ‘list_case_dicts.json’. 4. Select cases to use in the study and add them to ‘case_details.json’ 5. assign them to users in ‘user_details.json’. 6. adjust display groups, medication routes, display names, and display locations in ‘variable_details.json,’ ‘med_details.json,’ and ‘data_layout.json.’ To load in data from other sources, you will need to create a custom 'loaddata' file. If your data source is a database, consider usings Django's Models.py (https://docs.djangoproject.com/en/3.1/topics/db/models/). You may also edit JSON file directly in order to create custom cases. ### Eye-tracking research Components related to eye-tracking are turned off in this version because accuracy across different environments can not be guaranteed. If you are interested in using SEMRinterface with a remote eye-tracking device, please see the following: * EyeBrowserPy (<https://github.com/ajk77/EyeBrowserPy>) * Leveraging Eye Tracking to Prioritize Relevant Medical Record Data: Comparative Machine Learning Study (<https://www.jmir.org/2020/4/e15876/>) * Eye-tracking for clinical decision support: A method to capture automatically what physicians are viewing in the EMR (<https://www.ncbi.nlm.nih.gov/pubmed/28815151>) ## Versioning Version 3.0. For the versions available, see https://github.com/ajk77/SimpleEMRSystem ## Authors * Andrew J King - Doctoral Candidate (at time of creation) * Website (https://www.andrewjking.com/) * Twitter (https://twitter.com/andrewsjourney) * Shyam Visweswaran - Principal Investigator * Website (http://www.thevislab.com/) * Twitter (https://twitter.com/Shyam_Vis) * Gregory F Cooper - Doctoral Advisor ## Citation King AJ, Calzoni L, Tajgardoon M, Cooper G, Clermont G, Hochheiser H, Visweswaran S. A simple electronic medical record system designed for research. JAMIA Open. 2021 July 31;4(3):ooab040. (<https://academic.oup.com/jamiaopen/article/4/3/ooab040/6332673>) ## Impact This interface has been used in the following studies: * King AJ, Cooper GF, Clermont G, Hochheiser H, Hauskrecht M, Sittig DF, Visweswaran S. Leveraging Eye Tracking to Prioritize Relevant Medical Record Data: Comparative Machine Learning Study. J Med Internet Res 2020;22(4):e15876. (<https://www.jmir.org/2020/4/e15876/>) * King AJ, Cooper GF, Clermont G, Hochheiser H, Hauskrecht M, Sittig DF, Visweswaran S. Using Machine Learning to Selectively Highlight Patient Information. J Biomed Inform. 2019 Dec 1;100:103327. (<https://www.sciencedirect.com/science/article/pii/S1532046419302461>) * King AJ, Cooper GF, Hochheiser H, Clermont G, Hauskrecht M, Visweswaran S. Using machine learning to predict the information seeking behavior of clinicians using an electronic medical record system. AMIA Annu Symp Proc. 2018 Nov 3-7; San Francisco, California p 673-682. [Distinguished Paper Nomination] (<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6371238/>) * King AJ, Hochheiser H, Visweswaran S, Clermont G, Cooper GF. Eye-tracking for clinical decision support: A method to capture automatically what physicians are viewing in the EMR. AMIA Joint Summits. 2017 Mar 27-30; San Francisco, California p 512-521. [Best Student Paper] (<https://www.ncbi.nlm.nih.gov/pubmed/28815151>) * King AJ, Cooper GF, Hochheiser H, Clermont G, Visweswaran S. Development and preliminary evaluation of a prototype of a learning electronic medical record system. AMIA Annu Symp Proc. 2015 Nov 14-18; San Francisco, California p.1967-1975. [Best Student Paper] (<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4765593/>) ## License This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>. ## Acknowledgments * Harry Hochheiser * Twitter (https://twitter.com/hshoch) * Gilles Clermont * Milos Hauskrecht