Welcome to patient_abm!

A library for generating synthetic electronic health records in FHIR v4 format using agent-based modeling to simulate patient pathways.

See the redacted final report from the March 2021 project development for an overview of the main components as well as suggested future developments - "REDACTED_C245 ABM Patient Pathways_Final Report_V3_28042021.cleaned.pdf"

Description

The simulation models a single patient interacting with environments (hospitals, GPs, etc) which can prompt updates to the patient's record.

Patients and Environments are modelled as agents. They are python class objects of type PatientAgent and EnvironmentAgent respectively, and are located in

src/patient_abm/agent

The simulation is configured by a configuration script, and the details of the patient-environment interactions must be implemented in the intelligence layer (see the relevant sections below for more details). We have provided templates for these elements.

Project Stucture

• The main code is found in the src and template folders of the repository (see Usage below for more information)
• The accompanying report is also available in the reports folder

Installation

pip installation

This repository has been tested using and

Use your terminal to cd into the directory containing this README (the project root directory) and run:

pip install .

Alternatively, if you want to develop and edit the library, then run

pip install -e ".[dev]"

Environment variables

In the project root directory, run:

export PATIENT_ABM_DIR="$(pwd)"

Running a simulation

After installing patient_abm, to run a patient pathway simulation, you must:

(1) Set up the simulation configuration script config.json

(2) Implement the intelligence layer, which:

• governs how the Patient and Environment agents interact;

• generates new Patient record entries;

• decides which Environment the patient should visit next and at what time;

• optionally applies custom updates the Patient and Environment agents.

In the folder template we provide templates for the config.json and the intelligence layer. The subfolder, also called template, contains empty template files, whereas the subfolder example contains example files.

You config.json and intelligence_dir can be located anywhere - they do not need to be inside this repo.

After completing this, in the terminal, run:

patient_abm simulation run --config_path </path/to/config.json>

to run the simulation. Angular brackets <...> here and in the following indicate places where the user needs to supply their own values, or where values are automatically generated by the simulation. For instance, if you want to run the config.json in template/example this this is the command

patient_abm simulation run --config_path template/example/config.json

Its outputs can be found in template/example/outputs.

This will load and validate the config.json, load the variables from the config, and then run the simulation one patient at a time, saving the outputs after each simulation.

The following folder structure and outputs are created in the save_dir defined in the config.json:

<simulation_id> /
    agents /
        patient_<patient_id>.tar
	    environment_<environment_0_id>.tar
	    environment_<environment_1_id>.tar
        ...
    fhir /
	    bundle.json
    main.log
    patient.log 

where a unique simulation_id is automatically generated for every patient in the config.json.

The configuration file config.json

The configuration file config.json contains all the information needed to initialize:

• All the simulation Patients
• All the simulation Environments (each Patient's 'universe'), along with the names of the interactions that the intelligence layer can apply when the Patient is present at an Environment
• Path to the intelligence layer directory, intelligence_dir
• Path to the save_dir directory in which the simulation outputs will be written
• Any other simulation parameter, such as stopping conditions, logging frequency, etc.

The config.json is a file with key-value pairs:

{
    key_0: <value_0>,
    key_1: <value_1>,
    ...
}

Below we provide the definition for each key and what the user is expected to provide as the corresponding value

patients

The key patients refers to data that should be used to initialize patient agent objects. You can enter its value in one of two ways:

• Write the patient data directly as a list of dictionaries. Each dictionary contains the patient class initialization arguments as key-value pairs.
• Give a path to a JSON (strongly preferred) or a CSV file that contains the same data as the list of dictionaries. The reason a JSON is preferred is because correct the datatypes are preserved, and is particularly important in the case where the Patient attribute is a nested object (such as the Patient's conditions attribute.)

Note that each patient must have the following required attributes:

• patient_id : Union[str, int]: Unique ID for the patient.
• gender : str: Patient gender, either "male" or "female". There are many other optional attributes, see the documentation for the PatientAgent class in patient_abm.agent.patient.

Two patient can have the same patient_id.

Even though multiple patients can be listed here, the simulation only runs for one patient at a time, they do not interact.

environments

The key environments refers to data that should be used to initialize Environment objects. You can enter its value in one of two ways:

• Write the environment data directly as a list of dictionaries. Each dictionary contains the patient class initialization arguments as key-value pairs.
• Give a path to a JSON (strongly preferred) or a CSV file that contains the same data as the list of dictionaries. The reason a JSON is preferred is because correct the datatypes are preserved, and is particularly important in the case where the Environment attribute is a nested object (such as the Environment interactions attribute.)

Note that each environment must have the following required attribute:

• environment_id : Union[str, int]: Unique ID for the environment. There are many other optional attributes, see the documentation for the EnvironmentAgent class in patient_abm.agent.environment.

Each environment in the list must have a unique environment_id.

Each environment's interactions attribute is a list of strings referring to functions in the intelligence layer with a specific structure. For example, if the intelligence layer directory looks like

<intelligence_dir> /
    interactions /
        general.py
	    gp.py
    intelligence.py

and there are functions in general.py called inpatient_encounter and outpatient_encounter, and two functions in gp.py called measure_bmi and diagnose_fever, then suppose we had a GP environments, its interactions list might be

interactions = [
    "general.inpatient_encounter",
    "gp.measure_bmi",
    "gp.diagnose_fever"
]

Note that default interactions located in src/patient_abm/intelligence/interactions/default get added to every environment as well. These are currently automatically added but in future could be amended.

intelligence_dir

The key intelligence_dir refers to the directory that contains the intelligence layer. Its value is the path string to that directory.

save_dir

The key save_dir refers to the directory in which the simulation outputs should be saved. Its value is the path string to that directory.

initial_environment_ids

The key initial_environment_ids refers to the initial Environment that each patient should visit, given by the Environment's environment_id. Its value is a dictionary, which can take several formats:

• {from_id: <environment_id>}, all Patients will start from the Environment with that <environment_id>.
• {from_id: [<environment_id_0>, <environment_id_1>, ...]}, the list of environment IDs must be as long as the number of Patients, each Patient will start from the Environment given in the corresponding position in the list.
• {from_probability: [<p_0>, <p_1>,...]}, the list of probabilities p_i must be as long as the number of Environments. The distribution is sampled for each patient.
• {from_probability: [<p_0>, <p_1>,...]}, the list of probabilities p_i must be as long as the number of Environments. The distribution is sampled for each patient.
• {from_json: '</path/to/ids.json>'}, a JSON file containing initial environment IDs, one for each patient.

stopping_condition

The key stopping_condition refers to the condition that should cause the simulation while loop to terminate. The simulation can always terminate early if a death interaction is applied. Its value is a dictionary, which can take several formats:

• {max_num_steps: <max_num_steps>}, the maximum number of steps (an integer) in the simulation.
• {max_real_time: {<unit>: <value>}}, maximum real time the simulation should run for. The subdictionary is {<unit>: <value>} is passed into python's datetime.timedelta function and so should respect the parameter values there.
• {max_patient_time: {<unit>: <value>}}, maximum patient time the simulation should run for. The subdictionary is {<unit>: <value>} is passed into python's datetime.timedelta function and so should respect the parameter values there.

hard_stop

The key hard_stop refers to a hard upper bound on the number of simulation steps. It is there to try and prevent the loop going to infinity for any reason. An integer value is expected.

log_every

The key log_every refers to the number of simulation steps that should execute between logging. Its value is an integer, i.e.. if it is n then logging will happen every n-th simulation step.

log_intermediate

If log_every > 1, then logging of simulation information between log_every steps may be lost. log_intermediate is a boolean which, if set to true, will ensure intermediate log information is collected but then actually writes to the logger in the log_every step. If false, only the information at every log_every-th simulation steps is written.

log_patient_record

A boolean value which, if set to true add the latest patient record entry to the logger. This should be used mainly for debugging. The full patient record is always stored in the saved patient agent tar file, and it can be recovered from there.

fhir_server_validate

At the end of the simulation, the patient record is converted into a FHIR Bundle resource and validated. Validation can be done using an "offline" method via the python fhir.resources library, or "online" by sending the bundle to the HAPI FHIR server (http://hapi.fhir.org/baseR4). If fhir_server_validate is true, the online method used.

patient_record_duplicate_action

When new patient entries are added to the patient record, a validation step is performed which checks whether the entry already exists, this is to prevent duplication. patient_record_duplicate_action decides the action to take if a duplicate is found. If it is set to "add", the new entry is added, whereas if if it is set to "skip" the entry won't be added.

Breast cancer pathway config

As an illustration for how a breast cancer pathway might look, we have provided a config.json for this in template/breast_cancer. This is simply an initial version of how this script could be configured for such a pathway, but this template and the intelligence layer can be configured to facilitate more complex dynamics.

The intelligence layer

The intelligence layer is a directory of python scripts. The location of the directory is given by the field intelligence_dir in the config.json. The structured of intelligence_dir is as follows:

<intelligence_dir> /
    interactions /
        <interactions_0>.py
	    <interactions_1>.py
        ...
    intelligence.py

The intelligence.py script must contain a function called intelligence. More information about the intelligence layer and how it should be structured are provided inside the respective files in template/template/<intelligence_dir>.

nox and tests

nox is used to check code is correctly formatted and runs the test suite. To use nox, cd the project root directory and run:

nox

Tests can also be run from this directory via

pytest tests

Notebooks

There are two demo notebooks in the notebooks folder.

patient-agent.ipynb

In this notebook we introduce the patient agent and its methods including:

• initializing with comorbidities
• adding properties to conditions, such as severity
• updating the patient record
• the patient record internal representation and converting to FHIR

simulation.ipynb

In this notebook we walk through how to run a simulation with a very simple intelligence layer and interactions. Please see above for more information about the simulation configuration script and the intelligence layer (we will not go into detail about the intelligence layer in the notebook). Here we will be using the files in template/example, and going through main processes that are called when patient_abm.simulation.run.simulate is executed (which is the function called by the CLI command patient_abm simulation run)

Roadmap

See the open issues for a list of proposed features (and known issues).

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

See CONTRIBUTING.md for detailed guidance.

License

Distributed under the MIT License. See LICENSE for more information.

Contact

To find out more about the Analytics Unit visit our project website or get in touch at [email protected].

Acknowledgements