From a5171a8a44379a7aa36fa944cf42727baa844ca3 Mon Sep 17 00:00:00 2001 From: Prafful Sharma <115104695+DraKen0009@users.noreply.github.com> Date: Wed, 15 May 2024 12:27:20 +0530 Subject: [PATCH] converted investigation and investigation group data into JSON (#1912) * 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 --- .../migrations/0434_unique_investigations.py | 39 + care/facility/models/patient_investigation.py | 2 +- .../commands/populate_investigations.py | 245 +-- data/investigation_groups.json | 30 + data/investigations.json | 1830 +++++++++++++++++ 5 files changed, 1971 insertions(+), 175 deletions(-) create mode 100644 care/facility/migrations/0434_unique_investigations.py create mode 100644 data/investigation_groups.json create mode 100644 data/investigations.json diff --git a/care/facility/migrations/0434_unique_investigations.py b/care/facility/migrations/0434_unique_investigations.py new file mode 100644 index 0000000000..adee9af156 --- /dev/null +++ b/care/facility/migrations/0434_unique_investigations.py @@ -0,0 +1,39 @@ +# 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), + ), + ] diff --git a/care/facility/models/patient_investigation.py b/care/facility/models/patient_investigation.py index 9e94e23153..e9fec6b3af 100644 --- a/care/facility/models/patient_investigation.py +++ b/care/facility/models/patient_investigation.py @@ -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) diff --git a/care/users/management/commands/populate_investigations.py b/care/users/management/commands/populate_investigations.py index 1603041f3b..21847ee190 100644 --- a/care/users/management/commands/populate_investigations.py +++ b/care/users/management/commands/populate_investigations.py @@ -1,156 +1,18 @@ +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): @@ -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", + ], + ) diff --git a/data/investigation_groups.json b/data/investigation_groups.json new file mode 100644 index 0000000000..210fa3da3d --- /dev/null +++ b/data/investigation_groups.json @@ -0,0 +1,30 @@ +[ + { + "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" + } +] diff --git a/data/investigations.json b/data/investigations.json new file mode 100644 index 0000000000..a33a48c6d5 --- /dev/null +++ b/data/investigations.json @@ -0,0 +1,1830 @@ +[ + { + "name": "Blood Group", + "type": "Choice", + "choices": "A-,A+,B+,B-,O+,O-,AB-,AB+", + "unit": null, + "ideal": null, + "min": null, + "max": null, + "category_id": [ + 1 + ] + }, + { + "name": "Total Count", + "type": "Float", + "choices": null, + "unit": "cell/cumm", + "ideal": "4500-11000 cells/cumm", + "min": 4500, + "max": 11000, + "category_id": [ + 1 + ] + }, + { + "name": "Neutrophil count", + "type": "Float", + "choices": null, + "unit": "cell/cumm", + "ideal": "4500-11000 cells/cumm", + "min": 4500, + "max": 11000, + "category_id": [ + 1 + ] + }, + { + "name": "Lymphocyte count Eosinophil count", + "type": "Float", + "choices": null, + "unit": "cell/cumm", + "ideal": "4500-11000 cells/cumm", + "min": 4500, + "max": 11000, + "category_id": [ + 1 + ] + }, + { + "name": "Eosinophil count", + "type": "Float", + "choices": null, + "unit": "cell/cumm", + "ideal": "4500-11000 cells/cumm", + "min": 4500, + "max": 11000, + "category_id": [ + 1 + ] + }, + { + "name": "Basophil count", + "type": "Float", + "choices": null, + "unit": "cell/cumm", + "ideal": "4500-11000 cells/cumm", + "min": 4500, + "max": 11000, + "category_id": [ + 1 + ] + }, + { + "name": "Monocyte count", + "type": "Float", + "choices": null, + "unit": "cell/cumm", + "ideal": "4500-11000 cells/cumm", + "min": 4500, + "max": 11000, + "category_id": [ + 1 + ] + }, + { + "name": "neutrophil", + "type": "Float", + "choices": null, + "unit": "%", + "ideal": "40-60 %", + "min": 40, + "max": 60, + "category_id": [ + 1 + ] + }, + { + "name": "lymphocyte", + "type": "Float", + "choices": null, + "unit": "%", + "ideal": "20-4 %", + "min": 20, + "max": 4, + "category_id": [ + 1 + ] + }, + { + "name": "eosinophil", + "type": "Float", + "choices": null, + "unit": "%", + "ideal": "1-3 %", + "min": 1, + "max": 3, + "category_id": [ + 1 + ] + }, + { + "name": "basophile", + "type": "Float", + "choices": null, + "unit": "%", + "ideal": "0-1 %", + "min": 0, + "max": 1, + "category_id": [ + 1 + ] + }, + { + "name": "monocyte", + "type": "Float", + "choices": null, + "unit": "%", + "ideal": "4-8 %", + "min": 4, + "max": 8, + "category_id": [ + 1 + ] + }, + { + "name": "Hb", + "type": "Float", + "choices": null, + "unit": "gm%", + "ideal": "men 14-17 gm% , woman 12-16 gm% ,children 12-14 gm%", + "min": 12, + "max": 17, + "category_id": [ + 4 + ] + }, + { + "name": "PCV", + "type": "Float", + "choices": null, + "unit": "%", + "ideal": "Men 38-51 gm% , Woman 36-47%", + "min": 36, + "max": 51, + "category_id": [ + 1 + ] + }, + { + "name": "RBC count", + "type": "Float", + "choices": null, + "unit": "million/cumm", + "ideal": "4.5-6.0 million/cumm", + "min": 4.5, + "max": 6, + "category_id": [ + 4 + ] + }, + { + "name": "RDW", + "type": "Float", + "choices": null, + "unit": "%", + "ideal": "11.8 - 16.1%", + "min": 11.8, + "max": 16.1, + "category_id": [ + 1 + ] + }, + { + "name": "Platelets", + "type": "Float", + "choices": null, + "unit": "lakhs/cumm", + "ideal": "1.5-4.5 lakhs/cumm", + "min": 1.5, + "max": 4.5, + "category_id": [ + 4 + ] + }, + { + "name": "MCV", + "type": "Float", + "choices": null, + "unit": "Fl", + "ideal": "80-96 Fl", + "min": 80, + "max": 96, + "category_id": [ + 4 + ] + }, + { + "name": "MCH", + "type": "Float", + "choices": null, + "unit": "pg", + "ideal": "27-33 pg", + "min": 27, + "max": 33, + "category_id": [ + 4 + ] + }, + { + "name": "MCHC", + "type": "Float", + "choices": null, + "unit": "g/dl", + "ideal": "33.4-35.5 g/dl", + "min": 33.4, + "max": 35.5, + "category_id": [ + 4 + ] + }, + { + "name": "ESR", + "type": "Float", + "choices": null, + "unit": "mm/hr", + "ideal": "0-20 mm/hr", + "min": 0, + "max": 20, + "category_id": [ + 1 + ] + }, + { + "name": "Peripheral blood smear", + "type": "String", + "choices": null, + "unit": "", + "ideal": "", + "min": null, + "max": null, + "category_id": [ + 1 + ] + }, + { + "name": "Reticulocyte count", + "type": "Float", + "choices": null, + "unit": "%", + "ideal": "adults 0.5-1.5%, newborns 3-6%", + "min": 0.5, + "max": 6, + "category_id": [ + 1 + ] + }, + { + "name": "M P smear", + "type": "String", + "choices": null, + "unit": "", + "ideal": "", + "min": null, + "max": null, + "category_id": [ + 1 + ] + }, + { + "name": "FBS", + "type": "Float", + "choices": null, + "unit": "mg/dl", + "ideal": "70-110 mg/dl", + "min": 70, + "max": 110, + "category_id": [ + 2 + ] + }, + { + "name": "PPBS", + "type": "Float", + "choices": null, + "unit": "mg/dl", + "ideal": "< 140 mg/dl", + "min": 0, + "max": 140, + "category_id": [ + 2 + ] + }, + { + "name": "RBS", + "type": "Float", + "choices": null, + "unit": "mg/dl", + "ideal": "80-120 mg/dl", + "min": 80, + "max": 120, + "category_id": [ + 2 + ] + }, + { + "name": "T. Cholestrol", + "type": "Float", + "choices": null, + "unit": "mg/dl", + "ideal": "150-220 mg/dl", + "min": 150, + "max": 220, + "category_id": [ + 2 + ] + }, + { + "name": "LDL", + "type": "Float", + "choices": null, + "unit": "mg/dl", + "ideal": "< 130 mg/dl", + "min": 0, + "max": 130, + "category_id": [ + 2 + ] + }, + { + "name": "HDL", + "type": "Float", + "choices": null, + "unit": "mg/dl", + "ideal": "male 35-80 mg/dl, female 40-88 mg/dl", + "min": 35, + "max": 88, + "category_id": [ + 2 + ] + }, + { + "name": "Triglycerides", + "type": "Float", + "choices": null, + "unit": "mg/dl", + "ideal": "male 60-165 mg/dl, female 40-140 mg/dl", + "min": 40, + "max": 165, + "category_id": [ + 2 + ] + }, + { + "name": "VLDL", + "type": "Float", + "choices": null, + "unit": "mg/dl", + "ideal": "2-30 mg/dl", + "min": 2, + "max": 30, + "category_id": [ + 2 + ] + }, + { + "name": "Urea", + "type": "Float", + "choices": null, + "unit": "mg/dl", + "ideal": "10-50 mg/dl", + "min": 10, + "max": 50, + "category_id": [ + 2 + ] + }, + { + "name": "Uric Acid", + "type": "Float", + "choices": null, + "unit": "mg/dl", + "ideal": "male 3.5-7.2 mg/dl, female 2.6-6 mg/dl", + "min": 2.6, + "max": 7.2, + "category_id": [ + 2 + ] + }, + { + "name": "Creatinine", + "type": "Float", + "choices": null, + "unit": "mg/dl", + "ideal": "male 0.7-1.4 mg/dl, female 0.6-1.2 mg/dl", + "min": 0.6, + "max": 1.4, + "category_id": [ + 2 + ] + }, + { + "name": "CRP", + "type": "Float", + "choices": null, + "unit": "mg/l", + "ideal": "upto 6 mg/l", + "min": 0, + "max": 6, + "category_id": [ + 2 + ] + }, + { + "name": "Serum Sodium (Na+)", + "type": "Float", + "choices": null, + "unit": "mmol/l", + "ideal": "135-155 mmol/l", + "min": 135, + "max": 155, + "category_id": [ + 2 + ] + }, + { + "name": "Serum Potassium (K+)", + "type": "Float", + "choices": null, + "unit": "mmol/l", + "ideal": "3.5 - 5.5 mmol/l", + "min": 3.5, + "max": 5.5, + "category_id": [ + 2 + ] + }, + { + "name": "Serum Calcium", + "type": "Float", + "choices": null, + "unit": "mg/dl", + "ideal": "8.8-10.2 mg/dl", + "min": 8.8, + "max": 10.2, + "category_id": [ + 2 + ] + }, + { + "name": "Serum Phosphorus", + "type": "Float", + "choices": null, + "unit": "mg/dl", + "ideal": "children 4-7 mg/dl, adult 2.5-4.5 mg/dl", + "min": 2.5, + "max": 7, + "category_id": [ + 2 + ] + }, + { + "name": "Serum Chloride", + "type": "Float", + "choices": null, + "unit": "mmol/l", + "ideal": "96-109 mmol/l", + "min": 96, + "max": 109, + "category_id": [ + 2 + ] + }, + { + "name": "Serum Megnesium", + "type": "Float", + "choices": null, + "unit": "mg/dl", + "ideal": "1.6-2.6 mg/dl", + "min": 1.6, + "max": 2.6, + "category_id": [ + 2 + ] + }, + { + "name": "Total Bilirubin", + "type": "Float", + "choices": null, + "unit": "mg/dl", + "ideal": "adult upto 1.2 mg/dl, infant 0.2-8 mg/dl", + "min": 0.2, + "max": 8, + "category_id": [ + 6 + ] + }, + { + "name": "Direct Bilirubin", + "type": "Float", + "choices": null, + "unit": "mg/dl", + "ideal": 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