Add application file

app.py

@@ -0,0 +1,490 @@
+# Gradio application to predict ESI triag level based on observation
+# author: tbartolo
+import math
+import joblib
+import gradio as gr
+
+# START models
+symptom_code_map = joblib.load('resources/etc/symptom_code_map.joblib')
+age_min_max_scaler = joblib.load('resources/normalizers/AGE_min_max_scaler.joblib')
+bpdias_min_max_scaler = joblib.load('resources/normalizers/BPDIAS_min_max_scaler.joblib')
+bpsys_min_max_scaler = joblib.load('resources/normalizers/BPSYS_min_max_scaler.joblib')
+lov_min_max_scaler = joblib.load('resources/normalizers/LOV_min_max_scaler.joblib')
+painscale_min_max_scaler = joblib.load('resources/normalizers/PAINSCALE_min_max_scaler.joblib')
+respr_min_max_scaler = joblib.load('resources/normalizers/RESPR_min_max_scaler.joblib')
+tempf_discretizer_model = joblib.load('resources/normalizers/TEMPF_discretizer_model.joblib')
+waittime_min_max_scaler = joblib.load('resources/normalizers/WAITTIME_min_max_scaler.joblib')
+pulse_min_max_scaler = joblib.load('resources/normalizers/PULSE_min_max_scaler.joblib')
+popct_min_max_scaler = joblib.load('resources/normalizers/POPCT_min_max_scaler.joblib')
+gb_classifier_model = joblib.load('resources/algorithms/gb_classifier_model.joblib')
+# END models
+
+waittime_mean = 30.86
+lov_mean = 236.02
+age_mean = 37.71
+tempf_mean = 94.95
+pulse_mean = 89.86
+respr_mean = 19.89
+bpsys_mean = 119.74
+bpdias_mean = 69.47
+popct_mean = 94.52
+
+
+def master_fn(waittime, lov, age, newborn, private_residnce, sex, arrems, ambtransfer, pulse, respr, bpsys, bpdias,
+              popct, painscale, seen72, initial_visit, injury, injury72, etohab, alzhd, asthma, cancer, cebvd, ckd,
+              copd, chf, cad, deprn, diabtyp1, diabtyp2, diabtyp0, esrd, hpe, edhiv, hyplipid, htn, obesity, osa,
+              ostprsis, substab, racer, tempf, rfv1, rfv2, rfv3, rfv4, rfv5, trauma, overdose_poison, medical_surgical):
+    def waittime_normalizer(value):
+        if not value or value < 0:
+            value = waittime_mean
+
+        log10_value = math.log10(value)
+        return waittime_min_max_scaler.transform([[log10_value]])[0][0]
+
+    def lov_normalizer(value):
+        if not value or value <= 0:
+            value = lov_mean
+
+        log10_value = math.log10(value)
+        return lov_min_max_scaler.transform([[log10_value]])[0][0]
+
+    def age_normalizer(value):
+        if not value or value < 0:
+            value = age_mean
+
+        return age_min_max_scaler.transform([[value]])[0][0]
+
+    def newborn_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def private_residnce_normalizer(value):
+        if not value:
+            value = "yes"
+
+        return yes_no_to_binary(value)
+
+    def sex_normalizer(value):
+        if not value:
+            value = "male"
+
+        if value.lower() == "female":
+            return 1
+        elif value.lower() == "male":
+            return 0
+        else:
+            raise ValueError("Illegal argument: SEX must \"female\" or \"male\"")
+
+    def arrems_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def ambtransfer_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def pulse_normalizer(value):
+        if not value or value <= 0:
+            value = pulse_mean
+
+        return pulse_min_max_scaler.transform([[value]])[0][0]
+
+    def respr_normalizer(value):
+        if not value or value <= 0:
+            value = respr_mean
+
+        if value > 35:
+            value = 35  # clip
+
+        return respr_min_max_scaler.transform([[value]])[0][0]
+
+    def bpsys_normalizer(value):
+        if not value or value <= 0:
+            value = bpsys_mean
+
+        return bpsys_min_max_scaler.transform([[value]])[0][0]
+
+    def bpdias_normalizer(value):
+        if not value or value <= 0:
+            value = bpdias_mean
+
+        return bpdias_min_max_scaler.transform([[value]])[0][0]
+
+    def popct_normalizer(value):
+        if not value or value <= 0:
+            value = popct_mean
+
+        log10_value = math.log10(value)
+        return popct_min_max_scaler.transform([[log10_value]])[0][0]
+
+    def painscale_normalizer(value):
+        if not value:
+            value = 0
+
+        return painscale_min_max_scaler.transform([[value]])[0][0]
+
+    def seen72_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def initial_visit_normalizer(value):
+        if not value:
+            value = "yes"
+
+        return yes_no_to_binary(value)
+
+    def injury_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def injury72_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def etohab_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def alzhd_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def asthma_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def cancer_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def cebvd_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def ckd_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def copd_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def chf_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def cad_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def deprn_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def diabtyp2_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def diabtyp1_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def diabtyp0_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def esrd_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def hpe_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def edhiv_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def hyplipid_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def htn_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def obesity_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def osa_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def ostprsis_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def substab_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def racer_normalizer(value):
+        if not value:
+            value = "other"
+
+        if value.lower() == "white":
+            return [0, 1]
+        elif value.lower() == "black":
+            return [1, 0]
+        elif value.lower() == "other":
+            return [0, 0]
+        else:
+            raise ValueError("Illegal argument: RACER must \"white\" or \"black\" or \"other\"")
+
+    def tempf_normalizer(value):
+        if not value or value < 70:
+            value = tempf_mean
+
+        return tempf_discretizer_model.transform([[value * 10]]).flatten()
+
+    def rfv1_normalizer(value):
+        if not value:
+            value = 0
+
+        return rfv_to_mapping(value)
+
+    def rfv2_normalizer(value):
+        if not value:
+            value = 0
+
+        return rfv_to_mapping(value)
+
+    def rfv3_normalizer(value):
+        if not value:
+            value = 0
+
+        return rfv_to_mapping(value)
+
+    def rfv4_normalizer(value):
+        if not value:
+            value = 0
+
+        return rfv_to_mapping(value)
+
+    def rfv5_normalizer(value):
+        if not value:
+            value = 0
+
+        return rfv_to_mapping(value)
+
+    def trauma_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def overdose_poison_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def medical_surgical_normalizer(value):
+        if not value:
+            value = "no"
+
+        return yes_no_to_binary(value)
+
+    def yes_no_to_binary(value):
+        if value.lower() == "yes":
+            return 1
+        elif value.lower() == "no":
+            return 0
+        else:
+            raise ValueError("Illegal argument: Value must \"yes\" or \"no\"")
+
+    def rfv_to_mapping(value):
+        value = symptom_code_map.get(value * 10)
+
+        if value or value == 0:
+            return decimal_to_binary_array(value, 13)
+        else:
+            raise ValueError("Illegal argument: Value must be valid symptom code")
+
+    def decimal_to_binary_array(number, array_size):
+        binary_representation = bin(number)[2:]  # Convert to binary and remove '0b' prefix
+        binary_array = [int(bit) for bit in binary_representation.zfill(array_size)[-array_size:]]
+        return binary_array
+
+    observation = []
+
+    observation.append(waittime_normalizer(waittime))
+    observation.append(lov_normalizer(lov))
+    observation.append(age_normalizer(age))
+    observation.append(newborn_normalizer(newborn))
+    observation.append(private_residnce_normalizer(private_residnce))
+    observation.append(sex_normalizer(sex))
+    observation.append((arrems_normalizer(arrems)))
+    observation.append((ambtransfer_normalizer(ambtransfer)))
+    observation.append(pulse_normalizer(pulse))
+    observation.append(respr_normalizer(respr))
+    observation.append(bpsys_normalizer(bpsys))
+    observation.append(bpdias_normalizer(bpdias))
+    observation.append(popct_normalizer(popct))
+    observation.append(painscale_normalizer(painscale))
+    observation.append(seen72_normalizer(seen72))
+    observation.append(initial_visit_normalizer(initial_visit))
+    observation.append(injury_normalizer(injury))
+    observation.append(injury72_normalizer(injury72))
+    observation.append(etohab_normalizer(etohab))
+    observation.append(alzhd_normalizer(alzhd))
+    observation.append(asthma_normalizer(asthma))
+    observation.append(cancer_normalizer(cancer))
+    observation.append(cebvd_normalizer(cebvd))
+    observation.append(ckd_normalizer(ckd))
+    observation.append(copd_normalizer(copd))
+    observation.append(chf_normalizer(chf))
+    observation.append(cad_normalizer(cad))
+    observation.append(deprn_normalizer(deprn))
+    observation.append(diabtyp1_normalizer(diabtyp1))
+    observation.append(diabtyp2_normalizer(diabtyp2))
+    observation.append(diabtyp0_normalizer(diabtyp0))
+    observation.append(esrd_normalizer(esrd))
+    observation.append(hpe_normalizer(hpe))
+    observation.append(edhiv_normalizer(edhiv))
+    observation.append(hyplipid_normalizer(hyplipid))
+    observation.append(htn_normalizer(htn))
+    observation.append(obesity_normalizer(obesity))
+    observation.append(osa_normalizer(osa))
+    observation.append(ostprsis_normalizer(ostprsis))
+    observation.append(substab_normalizer(substab))
+
+    observation = (observation + racer_normalizer(racer) + tempf_normalizer(tempf).tolist() + rfv1_normalizer(rfv1)
+                   + rfv2_normalizer(rfv2)) + rfv3_normalizer(rfv3) + rfv4_normalizer(rfv4) + rfv5_normalizer(rfv5)
+
+    observation.append(trauma_normalizer(trauma))
+    observation.append(overdose_poison_normalizer(overdose_poison))
+    observation.append(medical_surgical_normalizer(medical_surgical))
+
+    return gb_classifier_model.predict([observation])[0]
+
+
+demo = gr.Interface(
+    master_fn,
+    [
+        "number",  # waittime
+        "number",  # lov
+        "number",  # age
+        gr.Radio(["yes", "no"]),  # newborn
+        gr.Radio(["yes", "no"]),  # private_residnce
+        gr.Radio(["male", "female"]),  # sex
+        gr.Radio(["yes", "no"]),  # arrems
+        gr.Radio(["yes", "no"]),  # ambtransfer
+        "number",  # pulse
+        "number",  # respr
+        "number",  # bpsys
+        "number",  # bpdias
+        "number",  # popct
+        "number",  # painscale
+        gr.Radio(["yes", "no"]),  # seen72
+        gr.Radio(["yes", "no"]),  # initial_visit
+        gr.Radio(["yes", "no"]),  # injury
+        gr.Radio(["yes", "no"]),  # injury72
+        gr.Radio(["yes", "no"]),  # etohab
+        gr.Radio(["yes", "no"]),  # alzhd
+        gr.Radio(["yes", "no"]),  # asthma
+        gr.Radio(["yes", "no"]),  # cancer
+        gr.Radio(["yes", "no"]),  # cebvd
+        gr.Radio(["yes", "no"]),  # ckd
+        gr.Radio(["yes", "no"]),  # copd
+        gr.Radio(["yes", "no"]),  # chf
+        gr.Radio(["yes", "no"]),  # cad
+        gr.Radio(["yes", "no"]),  # deprn
+        gr.Radio(["yes", "no"]),  # diabtyp1
+        gr.Radio(["yes", "no"]),  # diabtyp2
+        gr.Radio(["yes", "no"]),  # diabtyp0
+        gr.Radio(["yes", "no"]),  # esrd
+        gr.Radio(["yes", "no"]),  # hpe
+        gr.Radio(["yes", "no"]),  # edhiv
+        gr.Radio(["yes", "no"]),  # hyplipid
+        gr.Radio(["yes", "no"]),  # htn
+        gr.Radio(["yes", "no"]),  # obesity
+        gr.Radio(["yes", "no"]),  # osa
+        gr.Radio(["yes", "no"]),  # ostprsis
+        gr.Radio(["yes", "no"]),  # substab
+        gr.Radio(["white", "black", "other"]),  # racer
+        "number",  # tempf
+        "number",  # rfv1
+        "number",  # rfv2
+        "number",  # rfv3
+        "number",  # rfv4
+        "number",  # rfv5
+        gr.Radio(["yes", "no"]),  # trauma
+        gr.Radio(["yes", "no"]),  # overdose_poison
+        gr.Radio(["yes", "no"])  # medical_surgical
+    ],
+    "number",
+    title="ESI Triage Level Predictor",
+    description="Enter the patient attributes and symptoms to perform the prediction. Note this application is a POC "
+                "and by no means claims any sort of accuracy in its predictions.",
+)
+
+if __name__ == "__main__":
+    demo.launch(show_api=False)