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.gradio/certificate.pem

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+-----BEGIN CERTIFICATE-----
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+-----END CERTIFICATE-----

README.md

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 ---
-title: Risk Stratification
-emoji: 🌖
-colorFrom: pink
-colorTo: indigo
-sdk: gradio
-sdk_version: 5.49.1
+title: risk_stratification
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 5.47.2
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import pandas as pd
+import gradio as gr
+
+
+def risk_stratify(file):
+    try:
+        df = pd.read_csv(file.name)
+    except Exception as e:
+        return f"❌ Error reading file: {e}"
+
+    required_cols = [
+        "member_id",
+        "age",
+        "chronic_conditions",
+        "ed_visits_past_year",
+        "inpatient_admissions",
+        "total_cost",
+        "behavioral_health_flag",
+        "pharmacy_rx_count",
+        "social_risk_score"
+    ]
+
+    missing = [c for c in required_cols if c not in df.columns]
+    if missing:
+        return f"❌ Missing required columns: {missing}"
+
+    # Risk scoring
+    df["risk_score"] = 0
+    df["risk_score"] += (df["age"] >= 65).astype(int)
+    df["risk_score"] += (df["chronic_conditions"] >= 2).astype(int) * 2
+    df["risk_score"] += (df["ed_visits_past_year"] >= 3).astype(int) * 2
+    df["risk_score"] += (df["inpatient_admissions"] >= 1).astype(int) * 2
+    df["risk_score"] += (df["total_cost"] >= 20000).astype(int) * 2
+    df["risk_score"] += (df["behavioral_health_flag"] == 1).astype(int) * 2
+    df["risk_score"] += (df["pharmacy_rx_count"] >= 10).astype(int)
+    df["risk_score"] += (df["social_risk_score"] >= 5).astype(int)
+
+    # Categorization
+    def categorize(score):
+        if score >= 6:
+            return "High Risk"
+        elif score >= 3:
+            return "Moderate Risk"
+        else:
+            return "Low Risk"
+
+    df["risk_category"] = df["risk_score"].apply(categorize)
+
+    # Summary output
+    counts = df["risk_category"].value_counts().to_dict()
+
+    summary = f"""
+# Medicaid Risk Stratification Results
+
+### **Member Count by Risk Group:**
+- High Risk: **{counts.get("High Risk", 0)}**
+- Moderate Risk: **{counts.get("Moderate Risk", 0)}**
+- Low Risk: **{counts.get("Low Risk", 0)}**
+
+---
+
+### **Method Summary**
+This risk stratification uses:
+- Chronic disease counts  
+- ED and inpatient utilization  
+- Annual cost  
+- Pharmacy use  
+- Behavioral health indicator  
+- Social risk  
+- Member age  
+
+A risk score ≥ 6 indicates **High Need** for care management outreach.
+
+Upload another file to re-run the analysis.
+"""
+
+    return summary
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("""
+# Medicaid Risk Stratification Tool  
+Upload a Medicaid member dataset to calculate risk scores.
+
+### Required CSV Columns:
+`member_id, age, chronic_conditions, ed_visits_past_year, inpatient_admissions, total_cost, behavioral_health_flag, pharmacy_rx_count, social_risk_score`
+""")
+
+    file = gr.File(label="Upload Medicaid CSV")
+    output = gr.Markdown()
+
+    file.upload(risk_stratify, file, output)
+
+if __name__ == "__main__":
+    demo.launch(share=True)

requirements.com

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+pandas
+gradio>=4.0.0