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"indicator_features": [],\n "scaler_mean": [\n 0.144,\n 0.592,\n 0.352,\n -1.9264272718900757,\n 0.25428691480041,\n -0.12866111127091243,\n 0.3160687894046944,\n 0.17560685599438977,\n 0.07457050108855663,\n 0.09705051883019633\n ],\n "scaler_scale": [\n 0.351089732119867,\n 0.4914631217090454,\n 0.4775939698111775,\n 8.062467396785618,\n 5.3648309482553245,\n 2.305385205762454,\n 2.0027369434051057,\n 7.015626085932286,\n 2.06711970671945,\n 6.827505392398019\n ],\n "coefficients": [\n 0.5998429990683761,\n 1.0963173371470474,\n 0.3665690795386455,\n 0.8979249492913688,\n -0.09116739912543087,\n 0.12131397805293258,\n 0.21162589301511409,\n 0.5647436885260596,\n 0.6123748996206798,\n 0.4735228772820654\n ],\n "intercept": -0.8399166743021372\n }\n }\n}') # ============================================================ # Portable inference engine # ============================================================ def sigmoid(z): if z >= 0: return 1.0 / ( 1.0 + math.exp(-z) ) ez = math.exp(z) return ez / ( 1.0 + ez ) def normalize_value(feature, value): if feature == "sex": if value == "Female": return 1.0 return 0.0 if value is None: return None if isinstance( value, bool ): return float( value ) try: return float( value ) except Exception: return None def predict_single(model, raw_values): features = model["features"] values = [ normalize_value( feature, value ) for feature, value in zip( features, raw_values ) ] missing = [ value is None or ( isinstance( value, float ) and math.isnan( value ) ) for value in values ] imputed = [] for i, value in enumerate( values ): if missing[i]: imputed.append( model[ "imputer_statistics" ][i] ) else: imputed.append( float( value ) ) transformed = list( imputed ) for index in model[ "indicator_features" ]: transformed.append( 1.0 if missing[index] else 0.0 ) scaled = [] for value, mean, scale in zip( transformed, model["scaler_mean"], model["scaler_scale"], ): if scale == 0: scaled.append( 0.0 ) else: scaled.append( ( value - mean ) / scale ) z = model[ "intercept" ] for coefficient, value in zip( model["coefficients"], scaled ): z += ( coefficient * value ) return sigmoid( z ) def predict_bundle( bundle_name, values ): bundle = MODEL_DATA[ bundle_name ] if bundle[ "type" ] == "ensemble": probabilities = [ predict_single( member, values ) for member in bundle[ "members" ] ] probability = ( sum( probabilities ) / len( probabilities ) ) else: probability = predict_single( bundle[ "model" ], values ) return probability # ============================================================ # Labels # ============================================================ def clinical_zone( probability, bundle_name ): bundle = MODEL_DATA[ bundle_name ] if probability >= bundle[ "red_cutoff" ]: return ( "Red — higher model probability" ) if probability >= bundle[ "green_cutoff" ]: return ( "Yellow — intermediate model probability" ) return ( "Green — lower model probability" ) def wearable_zone( probability ): threshold = MODEL_DATA[ "acute_hr_biomarker" ][ "threshold" ] if probability >= threshold: return ( "Higher-probability model output" ) return ( "Lower-probability model output" ) # ============================================================ # Feature labels and UI # ============================================================ SPECIAL_LABELS = { "sex": "Sex", "bmi": "Body mass index (BMI)", "pulmonary": "Pulmonary condition", "mode 1 SEE DCT 2": "Mode 1 SEE DCT 2", "mode 2 SEE DCT 4": "Mode 2 SEE DCT 4", "mode 2 SEE DCT 6": "Mode 2 SEE DCT 6", "Signal DCT 2": "Signal DCT 2", "Signal DCT 26": "Signal DCT 26", "Signal DCT 31": "Signal DCT 31", "Signal DCT 35": "Signal DCT 35", } def pretty_label( feature ): if feature in SPECIAL_LABELS: return SPECIAL_LABELS[ feature ] text = ( feature .replace( "post_acute__", "Post-acute " ) .replace( "acute__", "Acute " ) .replace( "_", " " ) ) return text[ 0 ].upper() + text[ 1: ] HR_FEATURES = { "mode 1 SEE DCT 2", "mode 2 SEE DCT 4", "mode 2 SEE DCT 6", "Signal DCT 2", "Signal DCT 26", "Signal DCT 31", "Signal DCT 35", } def make_input( feature ): label = pretty_label( feature ) if feature == "sex": return gr.Dropdown( choices=[ "Male", "Female" ], value="Male", label=label, ) if feature == "bmi": return gr.Number( value=25.0, label=label, ) if feature in HR_FEATURES: return gr.Number( value=0.0, label=label, ) return gr.Checkbox( value=False, label=label, ) # ============================================================ # Prediction callbacks # ============================================================ def predict_early( *values ): p = predict_bundle( "early_risk", values ) return ( round( p, 4 ), clinical_zone( p, "early_risk" ), ) def predict_pasc( *values ): p = predict_bundle( "pasc_check_lite", values ) return ( round( p, 4 ), clinical_zone( p, "pasc_check_lite" ), ) def predict_wearable( *values ): p = predict_bundle( "acute_hr_biomarker", values ) return ( round( p, 4 ), wearable_zone( p ), ) # ============================================================ # Interface # ============================================================ NOTICE = """ **Research prototype** Internally evaluated using the Stanford cohort. This application is not intended for diagnosis, treatment decisions, or autonomous clinical use. """ with gr.Blocks( title="Long COVID Research Prototype" ) as demo: gr.Markdown( """ # Long COVID Clinical + Wearable Prototype Three complementary research models for Long COVID risk stratification and identification. """ ) gr.Markdown( NOTICE ) # -------------------------------------------------------- # Model A # -------------------------------------------------------- with gr.Tab( "Early Risk" ): gr.Markdown( """ ### Model A — Early Risk Prospective risk stratification using information available during the first four weeks. """ ) early_inputs = [ make_input( feature ) for feature in MODEL_DATA[ "early_risk" ][ "features" ] ] early_button = gr.Button( "Estimate probability", variant="primary", ) early_probability = gr.Number( label="Predicted probability", precision=4, ) early_zone = gr.Textbox( label="Research risk zone", ) early_button.click( fn=predict_early, inputs=early_inputs, outputs=[ early_probability, early_zone, ], ) # -------------------------------------------------------- # Model B # -------------------------------------------------------- with gr.Tab( "PASC Check Lite" ): gr.Markdown( """ ### Model B — PASC Check Lite Post-acute decision-support model for identifying a pattern compatible with Long COVID / PASC. """ ) pasc_inputs = [ make_input( feature ) for feature in MODEL_DATA[ "pasc_check_lite" ][ "features" ] ] pasc_button = gr.Button( "Estimate probability", variant="primary", ) pasc_probability = gr.Number( label="Predicted probability", precision=4, ) pasc_zone = gr.Textbox( label="Research risk zone", ) pasc_button.click( fn=predict_pasc, inputs=pasc_inputs, outputs=[ pasc_probability, pasc_zone, ], ) # -------------------------------------------------------- # Model C # -------------------------------------------------------- with gr.Tab( "Acute + HR Biomarker" ): gr.Markdown( """ ### Model C — Acute symptoms + HR signature Combines three acute clinical symptoms with a seven-feature candidate digital HR biomarker signature. The seven HR values are mathematically derived features and are not raw heart-rate measurements. """ ) wearable_inputs = [ make_input( feature ) for feature in MODEL_DATA[ "acute_hr_biomarker" ][ "features" ] ] wearable_button = gr.Button( "Estimate probability", variant="primary", ) wearable_probability = gr.Number( label="Predicted probability", precision=4, ) wearable_zone_output = gr.Textbox( label="Research classification", ) wearable_button.click( fn=predict_wearable, inputs=wearable_inputs, outputs=[ wearable_probability, wearable_zone_output, ], ) gr.Markdown( """ --- ### Validation status Internal Stanford cohort evaluation only. The wearable-derived HR signature is a candidate digital biomarker and has not undergone independent external validation. """ ) demo.launch()