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+# Individual Task Examples
+
+In this folder, you can test individual tasks including ICD9 code classification, Naive Bayes summarization, and query extraction.
+
+Task Details:
+- [ICD9 code classification](https://github.com/Yale-LILY/EHRKit/tree/master/mimic_icd9_coding): predict ICD9 code using tfidf representations
+- [Naive Bayes Summarization](https://github.com/Yale-LILY/EHRKit/tree/master/summarization/pubmed_summarization): summarize text using a Naive Bayes model trained on the PubMed corpus
+- [Query Extraction](https://github.com/Yale-LILY/EHRKit/tree/master/QueryExtraction): search for specific queries in medical text using different methods
+
+## Virtual Environment
+Create virtual environment in parent directory using the following commands.
+```sh
+cd ..
+python3 -m venv ehrvir/
+source ehrvir/bin/activate
+```
+If you are using pip==18.1, comment out line 20 of requirements.txt ```en-core-web-sm``` and run 
+```sh
+pip install -r requirements.txt
+```
+Install ```en-core-web-sm``` by running
+```sh
+python -m spacy download en_core_web_sm
+```
+This is needed for the entity extraction task.
+
+## Preparation
+Create MIMIC data and output data directory using the following commands.
+```sh
+mkdir data
+mkdir data/mimic_data
+mkdir data/output_data
+```
+You will need to put all MIMIC data under ```data/mimic_data```, which is the folder containing all csv files downloaded from the orginal MIMIC dataset. If you are on tangra, you can the following command to copy everything from ```/lada2/lily/zl379/Year4/EHRTest/EHRKit/tutorials/data/mimic_data```:
+```sh 
+cp -r /lada2/lily/zl379/Year4/EHRTest/EHRKit/tutorials/data/mimic_data data/.
+```
+
+## Run mimic_classifier.py
+```sh
+cp mimic_classifier.py ../.
+cd ..
+python mimic_classifier.py
+```
+Note: you need to run ```mimic_classifier.py``` from the ROOT path of EHRKit.
+
+## Run a notebook
+You can test Naive Bayes summarization and query extraction using the notebooks naiveBayes.ipynb and query_extractor respectively. ICD0 code classification is also documented in mimic\_classifier.ipynb.
+
+To run a notebook, you need to first install ipykernel using ```python -m pip install ipykernel```. Set tthe kernel name to your virtual environment name using ```ipython kernel install --user --name=<name of virtual environment>```. Run ```jupyter notebook --no-browser . &``` and note the server port number. Open a local terminal and run ```ssh -o ServerAliveInterval=30  -L <any local port>:localhost:<server port number> <username>@tangra.cs.yale.edu```. Go to http://localhost:localportnumber and follow instructions in the notebook. 
+
+## Collated tasks
+Instructions to run other tasks are located in the [collated_tasks](https://github.com/Yale-LILY/EHRKit/tree/master/collated_tasks) directory.