Researcher view for exploring data

from enum import Enum

import pandas as pd
from jupyterhealth_client import Code, JupyterHealthClient

BLOOD_PRESSURE = Code.BLOOD_PRESSURE.value

pd.options.mode.chained_assignment = None

jh_client = JupyterHealthClient()

org_dict = {org['id']: org for org in jh_client.list_organizations()}
# children is not populated
for org in org_dict.values():
    parent_id = org['partOf']
    if parent_id is not None:
        parent = org_dict[parent_id]
        parent.setdefault("child_ids", []).append(org['id'])

def print_org(org, indent=''):
    print(f"{indent}[{org['id']}] {org['name']}")
    for org_id in org.get('child_ids', []):
        print_org(org_dict[org_id], indent=" " * len(indent) + " ⮑")


for org_id in org_dict[0]['child_ids']:
    print_org(org_dict[org_id])

print("All my studies:")
for study in jh_client.list_studies():
    print(f"  - [{study['id']}] {study['name']} org:{study['organization']['name']}")

# show all the patients with study data I have access to:
print("Patients with data I have access to:")

for patient in jh_client.list_patients():
    consents = jh_client.get_patient_consents(patient['id'])
    print(f"[{patient['id']}] {patient['nameFamily']}, {patient['nameGiven']}: {patient['telecomEmail']}")
    for study in consents['studies']:
        print(f"  - [{study['id']}] {study['name']}")
    for study in consents['studiesPendingConsent']:
        print(f"  - (not consented) [{study['id']}] {study['name']}")
    if not consents['studies'] and not consents['studiesPendingConsent']:
        print("  (no studies)") 

# pick patient id, study id from above
study_id = 30001
patient_id = 40028

df = jh_client.list_observations_df(patient_id=patient_id, study_id=study_id)

df.head()

# reduce data
bp = df.loc[df.resource_type == BLOOD_PRESSURE, ['systolic_blood_pressure_value', 'diastolic_blood_pressure_value', 'effective_time_frame_date_time']]
bp = bp.astype({"systolic_blood_pressure_value": int, "diastolic_blood_pressure_value": int})
bp

bp.plot(x="effective_time_frame_date_time", y=["diastolic_blood_pressure_value", "systolic_blood_pressure_value"])

from functools import partial


class Goal(Enum):
    """Enum for met/unmet

    These strings will be used for the legend.
    """

    met = "goal"
    unmet = "over"


class Category(Enum):
    normal = "normal"
    elevated = "elevated"
    hypertension = "hypertension"


def classify_bp(row):
    """Classify blood pressure"""
    # https://www.heart.org/en/health-topics/high-blood-pressure/understanding-blood-pressure-readings
    # note from : We can decide to have just normal, elevated and hypertension to begin with
    if (
        row.diastolic_blood_pressure_value < 80
        and row.systolic_blood_pressure_value < 120
    ):
        return Category.normal.value
    elif (
        row.diastolic_blood_pressure_value < 80
        and 120 <= row.systolic_blood_pressure_value < 130
    ):
        return Category.elevated.value
    else:
        return Category.hypertension.value


def bp_goal(patient_df, goal="140/90"):
    """True/False for blood pressure met goal"""
    sys_goal, dia_goal = (int(s) for s in goal.split("/"))
    if (patient_df.systolic_blood_pressure_value <= sys_goal) & (
        patient_df.diastolic_blood_pressure_value <= dia_goal
    ):
        return Goal.met.value
    else:
        return Goal.unmet.value


bp["category"] = bp.apply(classify_bp, axis=1)
goal = "110/70"

bp["goal"] = bp.apply(partial(bp_goal, goal="110/70"), axis=1)
bp

bp.category.value_counts(normalize=True)

bp.goal.value_counts(normalize=True)

def bp_goal_style(row):
    """highlight rows exceeding bp goal"""
    goal = Goal(row.goal)
    if goal == Goal.unmet:
        color = "#fdd"
    else:
        color = None
    
    return [f"background-color:{color}" if color else None] * len(row)


bp[-50:].style.hide().hide(["category", "goal"], axis="columns").apply(bp_goal_style, axis=1)