An application that helps health practitioners monitor the vitals of their patients. The application need to connect to a FHIR server that stores electronic medical records (EMRs).
**Features in the App **
- Options to select multiple vitals
- Graph visualisation to monitor vitals
- Multiple patient monitoring system
- Dynamice selection of the frequency of refreshing the data
- Summarised view of the patient details with the vitals
- Different view if the vital reading is not in normal range.
- Selection of the normal range for the vital
UML for the App
Design Rational of the Application included the resaon for the design choices, design patterns and design principles.
- First iteration Click here 2.Second iteration Click here
Additional Feature Python program applying machine learning model using random forest decision tree to predict what causes high levels of total cholestrol in a patient with the accuracy of more than 0.8. The data generated using the backend java program and stored in the .csv file.
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Open the Project using the pom.xml file from top level of the project file.
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Make sure Java SDK has been added to the file, otherwise check it from the project structure, the file option top left hand side. In the Project structure choose project.
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If Driver class dosen't show the running option then choose Module from the project structure and select the 'src' as sources.
Setup of the Machine Learning program:
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Installation of the of required modules.
- pip install -r requirements.txt for the python2
- pip3 install -r requirements.txt for the python3 or
- python -m pip install -r requirements.txt
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Two data set are available that are fetched from the server, both of the files don't have NULL position.
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Running the MLDrive from the java project(src/MachineLearning) generate the data sets that are fetched from the server.
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Result of the accuracy would show at the console.