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converted investigation and investigation group data into JSON (#1912)
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* converted investigation and investigation group data into JSON

* created json files for investigations and investigation_groups

* updated investigations.json

* fixed json data,command and migrations

* fixing migrations

* fixing lint issue

* update migrations

* update migrations

---------

Co-authored-by: Aakash Singh <mail@singhaakash.dev>
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@DraKen0009 @sainak
DraKen0009 and sainak committed May 15, 2024
1 parent 6ed4494 commit a5171a8
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39 changes: 39 additions & 0 deletions care/facility/migrations/0434_unique_investigations.py
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# Generated by Django 4.2.10 on 2024-03-30 09:46

from django.db import migrations, models
from django.db.models import F, Window
from django.db.models.functions import RowNumber


def fix_duplicate_investigation_names(apps, schema_editor):
PatientInvestigation = apps.get_model("facility", "PatientInvestigation")

window = Window(
expression=RowNumber(),
partition_by=[F("name")],
order_by=F("id").asc(),
)

investigations = PatientInvestigation.objects.annotate(row_number=window).filter(
row_number__gt=1
)

for investigation in investigations:
investigation.name = f"{investigation.name}_{investigation.row_number - 1}"

PatientInvestigation.objects.bulk_update(investigations, ["name"], batch_size=2000)


class Migration(migrations.Migration):
dependencies = [
("facility", "0433_alter_hospitaldoctors_area"),
]

operations = [
migrations.RunPython(fix_duplicate_investigation_names),
migrations.AlterField(
model_name="patientinvestigation",
name="name",
field=models.CharField(max_length=500, unique=True),
),
]
2 changes: 1 addition & 1 deletion care/facility/models/patient_investigation.py
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Expand Up @@ -17,7 +17,7 @@ def __str__(self) -> str:


class PatientInvestigation(BaseModel):
name = models.CharField(max_length=500, blank=False, null=False)
name = models.CharField(max_length=500, blank=False, null=False, unique=True)
groups = models.ManyToManyField(PatientInvestigationGroup)
unit = models.TextField(null=True, blank=True)
ideal_value = models.TextField(blank=True, null=True)
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245 changes: 71 additions & 174 deletions care/users/management/commands/populate_investigations.py
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import json

from django.core.management import BaseCommand
from django.db import transaction

from care.facility.models.patient_investigation import (
PatientInvestigation,
PatientInvestigationGroup,
)

# TODO Move the Investigations and Investigation Groups into a proper JSON like python dict structure to allow easy updates and additions.
investigations = """name unit ideal min max type (Float/String/Choice) choices category_id
Blood Group Choice A-,A+,B+,B-,O+,O-,AB-,AB+ 1
Total Count cell/cumm 4500-11000 cells/cumm 4500 11000 Float 1
Neutrophil count cell/cumm 4500-11000 cells/cumm 4500 11000 Float 1
Lymphocyte count Eosinophil count cell/cumm 4500-11000 cells/cumm 4500 11000 Float 1
Eosinophil count cell/cumm 4500-11000 cells/cumm 4500 11000 Float 1
Basophil count cell/cumm 4500-11000 cells/cumm 4500 11000 Float 1
Monocyte count cell/cumm 4500-11000 cells/cumm 4500 11000 Float 1
neutrophil % 4500-11000 cells/cumm 4500 11000 Float 1
lymphocyte % 4500-11000 cells/cumm 4500 11000 Float 1
eosinophil % 4500-11000 cells/cumm 4500 11000 Float 1
basophile % 4500-11000 cells/cumm 4500 11000 Float 1
monocyte % 4500-11000 cells/cumm 4500 11000 Float 1
Hb gm% men 14-17 gm% , woman 12-16 gm% ,children 12-14 gm% 12 17 Float 1
PCV % Men 38-51 gm% , Woman 36-47% 36 51 Float 1
RBC count million/cumm 4.5-6.0 million/cumm 4.5 6 Float 1
RDW % 11.8 - 16.1% 11.8 16.1 Float 1
Platelets lakhs/cumm 1.5-4.5 lakhs/cumm 1.5 4.5 Float 1
MCV Fl 80-96 Fl 80 96 Float 1
MCH pg 27-33 pg 27 33 Float 1
MCHC g/dl 33.4-35.5 g/dl 33.4 35.5 Float 1
ESR mm/hr 0-20 mm/hr 0 20 Float 1
Peripheral blood smear String 1
Reticulocyte count % adults 0.5-1.5%, newborns 3-6% 0.5 6 Float 1
M P smear String 1
FBS mg/dl 70-110 mg/dl 70 110 Float 2
PPBS mg/dl < 140 mg/dl 0 140 Float 2
RBS mg/dl 80-120 mg/dl 80 120 Float 2
T. Cholestrol mg/dl 150-220 mg/dl 150 220 Float 2
LDL mg/dl < 130 mg/dl 0 130 Float 2
HDL mg/dl male 35-80 mg/dl, female 40-88 mg/dl 35 88 Float 2
Triglycerides mg/dl male 60-165 mg/dl, female 40-140 mg/dl 40 165 Float 2
VLDL mg/dl 2-30 mg/dl 2 30 Float 2
Urea mg/dl 10-50 mg/dl 10 50 Float 2
Uric Acid mg/dl male 3.5-7.2 mg/dl, female 2.6-6 mg/dl 2.6 7.2 Float 2
Creatinine mg/dl male 0.7-1.4 mg/dl, female 0.6-1.2 mg/dl 0.6 1.4 Float 2
CRP mg/l upto 6 mg/l 0 6 Float 2
Serum Sodium (Na+) mmol/l 135-155 mmol/l 135 155 Float 2
Serum Potassium (K+) mmol/l 3.5 - 5.5 mmol/l 3.5 5.5 Float 2
Serum Calcium mg/dl 8.8-10.2 mg/dl 8.8 10.2 Float 2
Serum Phosphorus mg/dl children 4-7 mg/dl, adult 2.5-4.5 mg/dl 2.5 7 Float 2
Serum Chloride mmol/l 96-109 mmol/l 96 109 Float 2
Serum Megnesium mg/dl 1.6-2.6 mg/dl 1.6 2.6 Float 2
Total Bilirubin mg/dl adult upto 1.2 mg/dl, infant 0.2-8 mg/dl 0.2 8 Float 2
Direct Bilirubin mg/dl upto 0.4 mg/dl 0 0.4 Float 2
SGOT IU/L upto 46 IU/L 0 46 Float 2
SGPT IU/L upto 49 IU/L 0 49 Float 2
ALP IU/L male 80-306 IU/L, female 64-306 IU/L 64 306 Float 2
Total Protein g/dl 6-8 g/dl 6 8 Float 2
Albumin g/dl 3.5-5.2 g/dl 3.5 5.2 Float 2
Globulin g/dl 1.5-2.5 g/dl 1.5 2.5 Float 2
PT sec 9.1-12.1 seconds 9.1 12.1 Float 2
INR sec 0.8-1.1 seconds 0.8 1.1 Float 2
APTT sec 25.4-38.4 seconds 25.4 38.4 Float 2
D-Dimer ug/l < 0.5 ug/l 0 0.5 Float 2
Fibrinogen mg/dl 200-400 mg/dl 200 400 Float 2
GCT mg/dl < 140 mg/dl 0 140 Float 2
GTT mg/dl 140-200 mg/dl 140 200 Float 2
GGT U/L 11-50 U/L 11 50 Float 2
HbA1C % 4-5.6 % 4 5.6 Float 2
Serum Copper mcg/dl 85-180 mcg/dl 85 180 Float 2
Serum Lead mcg/dl upto 10 mcg/dl 0 10 Float 2
Iron mcg/dl 60-170 mcg/dl 60 170 Float 2
TIBC mcg/dl 250-450 mcg/dl 250 450 Float 2
Transferin Saturation % 15-50 % 15 50 Float 2
IL6 pg/ml 0-16.4 pg/ml 0 16.4 Float 2
Lactate mmol/l 0.5-1 mmol/l 0.5 1 Float 2
Ceruloplasmin mg/dl 14-40 mg/dl 14 40 Float 2
ACP U/L 0.13-0.63 U/L 0.13 0.63 Float 2
Protein C IU dl 1 65-135 IU dl 1 65 135 Float 2
Protein S % 70-140 % 70 140 Float 2
G6PD U/g Hb neonate 10.15-14.71 U/g Hb, adult 6.75-11.95 U/g Hb Float 2
ACCP EU/ml < 20 EU/ml 0 20 Float 2
Ferritin ng/ml 20-250 ng/ml 20 250 Float 2
LDH U/L 140-280 U/L 140 280 Float 2
Amylase U/L 60-180 U/L 60 180 Float 2
Lipase U/L 0-160 U/L 0 160 Float 2
Ammonia ug/dL 15-45 ug/dL 15 45 Float 2
CKMB IU/L 5-25 IU/L 5 25 Float 2
CK NAC U/L male < 171 U/L, female < 145 U/L Float 2
24hrs Urine Protein mg/dl <10 mg/dl 0 10 Float 2
24hrs Urine Uric Acid mg/24hr 250-750 mg/24hr 250 750 Float 2
24 hrs Urine Oxalate mg/L <15 mg/L 0 15 Float 2
Urine Microalbumin mg < 30 mg 0 30 Float 2
Urine Sodium mEq/day 40-220 mEq/day 40 220 Float 2
PCT ng/ml < 0.15 ng/ml 0 0.15 Float 2
T3 ng/dl 80-220 ng/dl 80 220 Float 2
T4 ug/L 5-12 ug/L 5 12 Float 2
TSH mIU/L 0.5-5 mIU/L 0.5 5 Float 2
FT3 ng/dl 60-180 ng/dl 60 180 Float 2
FT4 ng/dl 0.7-1.9 ng/dl 0.7 1.9 Float 2
Estradiol pg/ml premenopausal women 30-400 pg/ml, post-menopausal women 0-30 pg/ml, men 10-50 pg/ml Float 2
Growth Hormone ng/ml male 0.4-10 ng/ml, female 1-14 ng/ml 0.4 14 Float 2
Cortisol mcg/dl 5-25 ng/ml 5 25 Float 2
PTH pg/ml 14-65 pg/ml 14 65 Float 2
Prolactine ng/ml male <20 ng/ml, female <25 ng/ml, pregnant women <300 ng/ml Float 2
Pro BNP pg/ml < 300 pg/ml 0 300 Float 2
Vitamine D3 ng/ml 20-40 ng/ml 20 40 Float 2
Vitamine B12 pg/ml 160-950 pg/ml 160 950 Float 2
FSH IU/L before puberty 0-4 IU/L, during puberty 0.36-10 IU/L, 0 10 Float 2
LH IU/L before menopause 5-25 IU/L, after menopause 14.2-52.3 IU/L 5 52.3 Float 2
PSA ng/ml <4 ng/ml 0 4 Float 2
ACTH pg/ml 10-60 pg/ml 10 60 Float 2
CEA ng/ml 0-2.5 ng/ml 0 2.5 Float 2
AFP ng/ml 10-20 ng/ml 10 20 Float 2
CA125 U/ml < 46 U/ml 0 46 Float 2
CA19.9 U/ml 0-37 U/ml 0 37 Float 2
Testosterone ng/dl 270-1070 ng/dl 270 1070 Float 2
Progestrone ng/ml female pre ovulation, menupausal women, men <1 ng/ml, mid-cycle 5-20 ng/ml Float 2
Serum IgG g/L 6-16 g/L 6 16 Float 2
Serum IgE UL/ml 150-1000 UL/ml 150 1000 Float 2
Serum IgM g/L 0.4-2.5 g/L 0.4 2.5 Float 2
Serum IgA G/L 0.8-3 g/L 0.8 3 Float 2
Colour String 3
Appearence String 3
Ph String 3
Specific Gravity String 3
Nitrite String 3
Urobilinogen String 3
Bile Salt String 3
Bile Pigment String 3
Acetone String 3
Albumin String 3
Sugar String 3
Puscells String 3
Epithetical Cells String 3
RBC String 3
Cast String 3
Crystal String 3
others String 3
UPT String 3
Stool OB String 3
Stool Microscopy String 3"""


investigation_groups = """Id Name
1 Haematology
2 Biochemistry test
3 Urine Test"""


def none_or_float(val):
if len(val.strip()) != 0:
return float(val)
return None
with open("data/investigations.json", "r") as investigations_data:
investigations = json.load(investigations_data)

with open("data/investigation_groups.json") as investigation_groups_data:
investigation_groups = json.load(investigation_groups_data)


class Command(BaseCommand):
Expand All @@ -161,34 +23,69 @@ class Command(BaseCommand):
help = "Seed Data for Investigations"

def handle(self, *args, **options):
investigation_group_data = investigation_groups.split("\n")[1:]
investigation_group_dict = {}
for investigation_group in investigation_group_data:
current_investigation_group = investigation_group.split("\t")
current_obj = PatientInvestigationGroup.objects.filter(
name=current_investigation_group[1]
).first()
if not current_obj:
current_obj = PatientInvestigationGroup(
name=current_investigation_group[1]
)
current_obj.save()
investigation_group_dict[current_investigation_group[0]] = current_obj
investigation_data = investigations.split("\n")[1:]
for investigation in investigation_data:
current_investigation = investigation.split("\t")

investigation_groups_to_create = [
PatientInvestigationGroup(name=group.get("name"))
for group in investigation_groups
if group.get("id") not in investigation_group_dict
]
created_groups = PatientInvestigationGroup.objects.bulk_create(
investigation_groups_to_create
)
investigation_group_dict.update({group.id: group for group in created_groups})

existing_objs = PatientInvestigation.objects.filter(
name__in=[investigation["name"] for investigation in investigations]
)

bulk_create_data = []
bulk_update_data = []

for investigation in investigations:
data = {
"name": current_investigation[0],
"unit": current_investigation[1],
"ideal_value": current_investigation[2],
"min_value": none_or_float(current_investigation[3]),
"max_value": none_or_float(current_investigation[4]),
"investigation_type": current_investigation[5],
"choices": current_investigation[6],
"name": investigation["name"],
"unit": investigation.get("unit", ""),
"ideal_value": investigation.get("ideal_value", ""),
"min_value": None
if investigation.get("min_value") is None
else float(investigation.get("min_value")),
"max_value": None
if investigation.get("max_value") is None
else float(investigation.get("max_value")),
"investigation_type": investigation["type"],
"choices": investigation.get("choices", ""),
}
current_obj = PatientInvestigation.objects.filter(**data).first()
if not current_obj:
current_obj = PatientInvestigation(**data)
current_obj.save()
current_obj.groups.add(investigation_group_dict[current_investigation[7]])
current_obj.save()

existing_obj = existing_objs.filter(name=data["name"]).first()
if existing_obj:
bulk_update_data.append(existing_obj)
else:
new_obj = PatientInvestigation(**data)
bulk_create_data.append(new_obj)

group_ids = investigation.get("category_ids", [])
groups_to_add = [
investigation_group_dict[category_id] for category_id in group_ids
]
if existing_obj:
existing_obj.groups.set(groups_to_add)
else:
data["groups"] = groups_to_add

with transaction.atomic():
if bulk_create_data:
PatientInvestigation.objects.bulk_create(bulk_create_data)

if bulk_update_data:
PatientInvestigation.objects.bulk_update(
bulk_update_data,
fields=[
"unit",
"ideal_value",
"min_value",
"max_value",
"investigation_type",
"choices",
],
)
30 changes: 30 additions & 0 deletions data/investigation_groups.json
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[
{
"id": "1",
"name": "Haematology"
},
{
"id": "2",
"name": "Biochemistry test"
},
{
"id": "3",
"name": "Urine Test"
},
{
"id": "4",
"name": "CBC"
},
{
"id": "5",
"name": "Differential white blood cell count"
},
{
"id": "6",
"name": "Liver Function Test"
},
{
"id": "7",
"name": "Kidney Function test"
}
]

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