MedRecords

MedRecords is an opinionated wrapper around GraphRecords for medical data. It puts a graph record to work in a medical context and makes it the input medical analyses and products run on.

Installation

pip install medrecords

Building a Record

Every call returns a new record:

import medrecords as mr

medrecord = (
    mr.MedRecord()
    .add_nodes_in_group(
        [
            ("p1", {"gender": "F", "age": 71}),
            ("p2", {"gender": "M", "age": 64}),
            ("p3", {"gender": "F", "age": 38}),
        ],
        "Patient",
    )
    .add_nodes_in_group(
        [("I10", {"label": "Essential hypertension"})],
        "Diagnosis",
    )
    .add_edges_in_group(
        [("p1", "I10", {"year": 2019}), ("p2", "I10", {"year": 2021})],
        "Patient_Diagnosis",
    )
)

add_nodes and add_edges do the same without a group. Polars DataFrames work as a source, naming the index columns: add_nodes((frame, "patient_id")), add_edges((frame, "patient_id", "diagnosis_id")).

Reading a Record

medrecord.node_count()  # 4
medrecord.group_indices()  # ['Patient', 'Diagnosis', 'Patient_Diagnosis']
medrecord.group("Patient").nodes()  # ['p1', 'p2', 'p3']

patient = medrecord.node("p1")
patient.attributes()  # {'gender': 'F', 'age': 71}
patient.groups()  # ['Patient']

Querying

mr.nodes() and mr.edges() start an expression tied to no record. medrecord.nodes() binds one to a record, which makes it a series. A series runs when you call evaluate().

older = medrecord.nodes().filter(
    mr.nodes().in_group("Patient") & (mr.nodes().attribute("age") > 60)
)
list(older.evaluate())  # ['p1', 'p2']

diagnosed = (
    medrecord.edges().filter(mr.edges().in_group("Patient_Diagnosis")).source_node()
)
list(diagnosed.evaluate())  # ['p1', 'p2']

medrecord.nodes().filter(mr.nodes().in_group("Patient")).attribute("age").mean().evaluate()
# 57.666666666666664

medrecord.query(expression) binds a free expression the same way.

Evaluations

An analytic is a class with one compute method that returns one value. Analytics are placed in groups, and groups in an evaluation:

from medrecords.evaluation import Analytic, Evaluation, EvaluationGroup, read
from medrecords.evaluation.catalogue import AttributeCounts, AttributeMean, ElementCount

PATIENTS = mr.nodes().filter(mr.nodes().in_group("Patient"))


class OldestPatient(Analytic[mr.MedRecord, int]):
    """The age of the oldest patient."""

    def compute(self, medrecord: mr.MedRecord) -> int:
        return read(medrecord.query(PATIENTS).attribute("age").max().evaluate(), int)


evaluation = (
    Evaluation[mr.MedRecord]("Practice snapshot")
    .add_group(
        "Demographics",
        EvaluationGroup[mr.MedRecord]()
        .add_analytic("Patients", ElementCount(PATIENTS))
        .add_analytic("Mean age", AttributeMean("age", PATIENTS))
        .add_analytic("Sex", AttributeCounts("gender", PATIENTS)),
    )
    .add_analytic("Oldest", OldestPatient())
)

The second type argument of Analytic is the type of the value compute returns. The report is read by name:

report = evaluation.report(medrecord)

report.group("Demographics").analytic("Patients").result  # 3
report.group("Demographics").analytic("Mean age").result  # 57.666666666666664
report.analytic("Oldest").result  # 71

A result is a number or a string, or one of Table, Plot, Measurement, Assessment and Distribution. report.group("Demographics").analytic("Sex").result.to_polars() gives

┌───────┬───────┬──────────┐
│ value ┆ count ┆ share    │
╞═══════╪═══════╪══════════╡
│ F     ┆ 2     ┆ 0.666667 │
│ M     ┆ 1     ┆ 0.333333 │
└───────┴───────┴──────────┘

medrecords.evaluation.catalogue holds analytics for counts, attribute statistics, completeness, ranges and uniqueness. add_group_over and add_group_over_each run a group against something derived from the record, a cohort or one part per attribute value.

JSON and PDF are exports:

from medrecords.evaluation import Document, Report

report.to_json("report.json")
Report.from_json("report.json")

Document(report).to_pdf("report.pdf")

Documentation

• Documentation