Add architecture diagrams

Architecture Diagrams/secondlife high level architecture.drawio

@@ -0,0 +1,163 @@
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Architecture Diagrams/secondlife low level architecture.drawio

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+      <mxGeometry x="502" y="30" width="113" height="65" as="geometry" />
+    </mxCell>
+    <mxCell id="HP6" value="My Profile&#xa;name · location&#xa;conditions" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#e8f5e8;strokeColor=#5b9e5b;fontSize=9;" vertex="1" parent="HPG">
+      <mxGeometry x="625" y="30" width="113" height="65" as="geometry" />
+    </mxCell>
+    <mxCell id="HP_NOTE" value="Bootstrap 5.3 dark SPA  ·  escH() / escA() XSS guards  ·  navbar name updates live on profile save  ·  Search includes already-connected toggle" style="text;html=1;strokeColor=none;fillColor=none;fontSize=8;fontColor=#555;align=left;" vertex="1" parent="HPG">
+      <mxGeometry x="10" y="106" width="784" height="16" as="geometry" />
+    </mxCell>
+
+    <!-- ============================================================
+         LAYER 2 — FLASK SERVER
+         ============================================================ -->
+    <mxCell id="L2" value="⚙️  Flask Server — app.py  (port 5000)  ·  TEMPLATES_AUTO_RELOAD=True  ·  MAX_CONTENT_LENGTH=10 MB  ·  server-side cookie sessions" style="swimlane;startSize=32;fillColor=#e1d5e7;strokeColor=#9673a6;fontSize=13;fontStyle=1;fontColor=#3d0066;" vertex="1" parent="1">
+      <mxGeometry x="10" y="255" width="1680" height="280" as="geometry" />
+    </mxCell>
+
+    <mxCell id="AR" value="🔐 Auth Routes&#xa;(no session required)&#xa;&#xa;POST /auth/patient/register&#xa;POST /auth/patient/login&#xa;POST /auth/hospital/register&#xa;POST /auth/hospital/login&#xa;POST /auth/logout" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#f3e5f5;strokeColor=#9673a6;fontSize=9;align=left;spacingLeft=8;verticalAlign=top;spacingTop=8;" vertex="1" parent="L2">
+      <mxGeometry x="20" y="40" width="215" height="220" as="geometry" />
+    </mxCell>
+
+    <mxCell id="PAR" value="👤 Patient API&#xa;(patient session)&#xa;&#xa;GET | POST  /api/patient/profile&#xa;GET  /api/patient/matches&#xa;GET | POST | DELETE&#xa;     /api/patient/interest&#xa;GET  /api/patient/connections&#xa;POST  /api/patient/connect&#xa;GET  /api/patient/hospitals-for-trial" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#cce5ff;strokeColor=#0066cc;fontSize=9;align=left;spacingLeft=8;verticalAlign=top;spacingTop=8;" vertex="1" parent="L2">
+      <mxGeometry x="248" y="40" width="250" height="220" as="geometry" />
+    </mxCell>
+
+    <mxCell id="HAR" value="🏥 Hospital API&#xa;(hospital session)&#xa;&#xa;GET | POST  /api/hospital/profile&#xa;GET  /api/hospital/patients&#xa;GET  /api/hospital/trials&#xa;POST  /api/hospital/connect&#xa;GET  /api/hospital/connections&#xa;PUT  /api/hospital/connections/&#xa;       &lt;cid&gt;/status" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#d5e8d4;strokeColor=#5b9e5b;fontSize=9;align=left;spacingLeft=8;verticalAlign=top;spacingTop=8;" vertex="1" parent="L2">
+      <mxGeometry x="511" y="40" width="265" height="220" as="geometry" />
+    </mxCell>
+
+    <mxCell id="MR" value="💬 Messaging + Inbox&#xa;&#xa;GET | POST&#xa;  /api/patient/connections/&lt;cid&gt;/messages&#xa;GET  /api/patient/inbox&#xa;&#xa;GET | POST&#xa;  /api/hospital/connections/&lt;cid&gt;/messages&#xa;GET  /api/hospital/inbox&#xa;&#xa;→ mark_messages_read() on GET&#xa;→ get_*_inbox_threads() + unread_count" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#fff2cc;strokeColor=#d6b656;fontSize=9;align=left;spacingLeft=8;verticalAlign=top;spacingTop=8;" vertex="1" parent="L2">
+      <mxGeometry x="789" y="40" width="280" height="220" as="geometry" />
+    </mxCell>
+
+    <mxCell id="DR" value="📄 Documents&#xa;&#xa;GET  /api/patient/documents&#xa;POST  /api/patient/documents&#xa;  → multipart/form-data&#xa;  → secure_filename()&#xa;  → ALLOWED: .pdf .docx .doc&#xa;             .txt .png .jpg .jpeg&#xa;  → max 10 MB&#xa;DELETE  /api/patient/documents/&lt;id&gt;&#xa;GET  /api/patient/documents/&lt;id&gt;/download" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#ffe6cc;strokeColor=#d79b00;fontSize=9;align=left;spacingLeft=8;verticalAlign=top;spacingTop=8;" vertex="1" parent="L2">
+      <mxGeometry x="1082" y="40" width="255" height="220" as="geometry" />
+    </mxCell>
+
+    <mxCell id="SR" value="🔧 Shared + Boot Thread&#xa;&#xa;GET /api/status&#xa;  → ready: true|false + stats&#xa;GET /api/conditions/autocomplete&#xa;&#xa;─────────────────────&#xa;🚀 Boot Thread (background)&#xa;① db.init_db()&#xa;  → create 5 tables&#xa;  → seed 25 patients + 3 hospitals&#xa;② pipeline.load()&#xa;  → read 11 CSV files into memory&#xa;③ pipeline.train()&#xa;  → build RF or load model_cache.pkl" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#f5f5f5;strokeColor=#666666;fontSize=9;align=left;spacingLeft=8;verticalAlign=top;spacingTop=8;" vertex="1" parent="L2">
+      <mxGeometry x="1350" y="40" width="310" height="220" as="geometry" />
+    </mxCell>
+
+    <!-- ============================================================
+         LAYER 3 — BUSINESS LOGIC
+         ============================================================ -->
+    <mxCell id="L3" value="🧠  Business Logic" style="swimlane;startSize=32;fillColor=#fff8e1;strokeColor=#d6a000;fontSize=14;fontStyle=1;fontColor=#5a3e00;" vertex="1" parent="1">
+      <mxGeometry x="10" y="545" width="1680" height="300" as="geometry" />
+    </mxCell>
+
+    <!-- database.py -->
+    <mxCell id="DBG" value="database.py — SQLite Layer" style="swimlane;startSize=24;fillColor=#d5e8d4;strokeColor=#5b9e5b;fontSize=11;fontStyle=1;" vertex="1" parent="L3">
+      <mxGeometry x="20" y="38" width="705" height="252" as="geometry" />
+    </mxCell>
+    <mxCell id="T1" value="patient_accounts&#xa;25 rows&#xa;5 demo + 20 Synthea" style="shape=cylinder3;whiteSpace=wrap;html=1;boundedLbl=1;backgroundOutline=1;size=12;fillColor=#d5e8d4;strokeColor=#5b9e5b;fontSize=9;" vertex="1" parent="DBG">
+      <mxGeometry x="10" y="32" width="122" height="68" as="geometry" />
+    </mxCell>
+    <mxCell id="T2" value="hospital_accounts&#xa;3 rows&#xa;mgh, cleveland, jhopkins" style="shape=cylinder3;whiteSpace=wrap;html=1;boundedLbl=1;backgroundOutline=1;size=12;fillColor=#d5e8d4;strokeColor=#5b9e5b;fontSize=9;" vertex="1" parent="DBG">
+      <mxGeometry x="144" y="32" width="122" height="68" as="geometry" />
+    </mxCell>
+    <mxCell id="T3" value="patient_trial_interests&#xa;UNIQUE(patient_id,&#xa;trial_id)" style="shape=cylinder3;whiteSpace=wrap;html=1;boundedLbl=1;backgroundOutline=1;size=12;fillColor=#d5e8d4;strokeColor=#5b9e5b;fontSize=9;" vertex="1" parent="DBG">
+      <mxGeometry x="278" y="32" width="122" height="68" as="geometry" />
+    </mxCell>
+    <mxCell id="T4" value="connections&#xa;UNIQUE(patient, hospital,&#xa;COALESCE(trial,''))" style="shape=cylinder3;whiteSpace=wrap;html=1;boundedLbl=1;backgroundOutline=1;size=12;fillColor=#d5e8d4;strokeColor=#5b9e5b;fontSize=9;" vertex="1" parent="DBG">
+      <mxGeometry x="412" y="32" width="132" height="68" as="geometry" />
+    </mxCell>
+    <mxCell id="T5" value="connection_messages&#xa;sender_role: patient|hospital&#xa;is_read: 0 | 1" style="shape=cylinder3;whiteSpace=wrap;html=1;boundedLbl=1;backgroundOutline=1;size=12;fillColor=#d5e8d4;strokeColor=#5b9e5b;fontSize=9;" vertex="1" parent="DBG">
+      <mxGeometry x="556" y="32" width="132" height="68" as="geometry" />
+    </mxCell>
+    <mxCell id="DBF1" value="Key functions:  authenticate_patient / authenticate_hospital  ·  update_patient_profile / update_hospital_profile  ·  get_open_patients_for_hospital(condition, include_connected)  ·  create_connection / connection_exists  ·  update_connection_status  ·  create_connection_message  ·  mark_messages_read  ·  get_hospital_inbox_threads / get_patient_inbox_threads  ·  _seed_dataset_patients(20)" style="text;html=1;strokeColor=none;fillColor=none;fontSize=8;fontColor=#333;align=left;" vertex="1" parent="DBG">
+      <mxGeometry x="10" y="112" width="685" height="30" as="geometry" />
+    </mxCell>
+    <mxCell id="DBF2" value="JSON columns (conditions, medications, documents, research_conditions) stored as TEXT  ·  parsed by _row_to_dict()  ·  passwords: SHA-256 via _hash()  ·  idx_conn_unique handles NULL trial_id  ·  idx_msgs_conn on (connection_id, created_at)" style="text;html=1;strokeColor=none;fillColor=none;fontSize=8;fontColor=#555;align=left;" vertex="1" parent="DBG">
+      <mxGeometry x="10" y="148" width="685" height="30" as="geometry" />
+    </mxCell>
+
+    <!-- pipeline.py -->
+    <mxCell id="PLG" value="pipeline.py — SecondLifePipeline (ML Engine)" style="swimlane;startSize=24;fillColor=#ffe6cc;strokeColor=#d79b00;fontSize=11;fontStyle=1;" vertex="1" parent="L3">
+      <mxGeometry x="745" y="38" width="925" height="252" as="geometry" />
+    </mxCell>
+    <mxCell id="PL1" value="load()&#xa;Reads 11 CSV files → memory&#xa;trial_profiles · active_trial_ids&#xa;trial_us_states · facility_tokens&#xa;drug_keywords · summaries&#xa;patient_conditions · meds · labs&#xa;~2–3 min on first boot" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#fff3e0;strokeColor=#d79b00;fontSize=9;" vertex="1" parent="PLG">
+      <mxGeometry x="10" y="32" width="165" height="115" as="geometry" />
+    </mxCell>
+    <mxCell id="PL2" value="train()&#xa;3000 patients × 30 trials&#xa;_compute_features() 17 feats&#xa;pseudo-label: 6-feat weighted score&#xa;GroupShuffleSplit 80/20 (patient-level)&#xa;RandomForest n=200, depth=12&#xa;CalibratedClassifierCV isotonic cv=3&#xa;→ save model_cache.pkl" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#fff3e0;strokeColor=#d79b00;fontSize=9;" vertex="1" parent="PLG">
+      <mxGeometry x="188" y="32" width="180" height="115" as="geometry" />
+    </mxCell>
+    <mxCell id="PL3" value="match_patient()&#xa;Input: conditions, age,&#xa;        gender, patient_id&#xa;Scores all active trials&#xa;combined = 0.6 × RF_prob&#xa;          + 0.4 × match_score&#xa;Returns top-20 ranked trials" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#fff3e0;strokeColor=#d79b00;fontSize=9;" vertex="1" parent="PLG">
+      <mxGeometry x="382" y="32" width="170" height="115" as="geometry" />
+    </mxCell>
+    <mxCell id="PL4" value="trials_for_hospital()&#xa;Input: name, location,&#xa;        research_conditions&#xa;Tier1: facility Jaccard ≥ 0.25&#xa;Tier2: same US state&#xa;Tier3: condition overlap&#xa;Active-only filter every tier" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#fff3e0;strokeColor=#d79b00;fontSize=9;" vertex="1" parent="PLG">
+      <mxGeometry x="566" y="32" width="165" height="115" as="geometry" />
+    </mxCell>
+    <mxCell id="PL5" value="hospitals_for_trial()&#xa;Input: trial_id&#xa;Tier1: Jaccard ≥ 0.25 → 🟢&#xa;Tier2: same state → ⬜&#xa;Tier3: cond overlap → ⬜&#xa;Tier4: fallback → ⬜&#xa;Modal: green vs grey sections" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#fff3e0;strokeColor=#d79b00;fontSize=9;" vertex="1" parent="PLG">
+      <mxGeometry x="745" y="32" width="165" height="115" as="geometry" />
+    </mxCell>
+    <mxCell id="PLM" value="17 Features: condition_overlap · jaccard_similarity · overlap_ratio_trial · overlap_ratio_patient · condition_rarity_score · trial_specificity · condition_burden · active_ratio · resolved_ratio · age_distance · age_centered · age_compatibility · gender_compatibility · geo_feasibility · med_compatibility · lab_availability · data_completeness" style="text;html=1;strokeColor=none;fillColor=none;fontSize=8;fontColor=#333;align=left;" vertex="1" parent="PLG">
+      <mxGeometry x="10" y="158" width="905" height="20" as="geometry" />
+    </mxCell>
+    <mxCell id="PLM2" value="AUC-ROC: 0.60  ·  Accuracy: 85.1%  ·  F1: 0.917  ·  Precision: 0.852  ·  Recall: 0.993  ·  90,994 training pairs  ·  82.1% pseudo-label positive rate  ·  GroupShuffleSplit prevents patient-level data leakage" style="text;html=1;strokeColor=none;fillColor=none;fontSize=8;fontColor=#555;align=left;" vertex="1" parent="PLG">
+      <mxGeometry x="10" y="182" width="905" height="20" as="geometry" />
+    </mxCell>
+
+    <!-- ============================================================
+         LAYER 4 — STORAGE
+         ============================================================ -->
+    <mxCell id="L4" value="💾  Storage" style="swimlane;startSize=32;fillColor=#f5f5f5;strokeColor=#666666;fontSize=14;fontStyle=1;fontColor=#333;" vertex="1" parent="1">
+      <mxGeometry x="10" y="855" width="1680" height="215" as="geometry" />
+    </mxCell>
+
+    <mxCell id="S1" value="secondlife.db&#xa;SQLite file&#xa;5 tables · auto-created&#xa;on init_db()" style="shape=cylinder3;whiteSpace=wrap;html=1;boundedLbl=1;backgroundOutline=1;size=14;fillColor=#d5e8d4;strokeColor=#5b9e5b;fontStyle=1;fontSize=10;" vertex="1" parent="L4">
+      <mxGeometry x="75" y="45" width="155" height="120" as="geometry" />
+    </mxCell>
+
+    <mxCell id="S2" value="model_cache.pkl&#xa;RandomForest +&#xa;CalibratedClassifierCV&#xa;delete to force retrain" style="shape=cylinder3;whiteSpace=wrap;html=1;boundedLbl=1;backgroundOutline=1;size=14;fillColor=#ffe6cc;strokeColor=#d79b00;fontStyle=1;fontSize=10;" vertex="1" parent="L4">
+      <mxGeometry x="325" y="45" width="155" height="120" as="geometry" />
+    </mxCell>
+
+    <mxCell id="S3" value="uploads/patient_docs/&#xa;&lt;patient_id&gt;/&#xa;&lt;doc_id&gt;_filename&#xa;10 MB max · auto-created" style="shape=cylinder3;whiteSpace=wrap;html=1;boundedLbl=1;backgroundOutline=1;size=14;fillColor=#cce5ff;strokeColor=#0066cc;fontStyle=1;fontSize=10;" vertex="1" parent="L4">
+      <mxGeometry x="575" y="45" width="155" height="120" as="geometry" />
+    </mxCell>
+
+    <mxCell id="S4" value="Synthea Patient CSVs&#xa;&#xa;patients_details.csv&#xa;  265,893 patients · demographics&#xa;final_patients_conditions.csv&#xa;  967k rows · 106 conditions&#xa;patients_medications.csv&#xa;  213,182 patients&#xa;patients_observations.csv&#xa;  23,231 patients with lab data" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#e8f5e8;strokeColor=#5b9e5b;fontSize=9;align=left;spacingLeft=8;verticalAlign=top;spacingTop=8;" vertex="1" parent="L4">
+      <mxGeometry x="855" y="38" width="275" height="158" as="geometry" />
+    </mxCell>
+
+    <mxCell id="S5" value="ClinicalTrials.gov / AACT CSVs&#xa;&#xa;trail_studies.csv — 65k recruiting trials&#xa;trail_conditions.csv — 34k with conditions&#xa;trail_eligibilities.csv — age · gender&#xa;trail_facilities.csv — 189k with US geo&#xa;trail_interventions.csv — 196k with drugs&#xa;trail_brief_summaries.csv — summaries&#xa;trail_keywords.csv — keyword index" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#fff3e0;strokeColor=#d79b00;fontSize=9;align=left;spacingLeft=8;verticalAlign=top;spacingTop=8;" vertex="1" parent="L4">
+      <mxGeometry x="1230" y="38" width="310" height="158" as="geometry" />
+    </mxCell>
+
+    <!-- ============================================================
+         LEGEND
+         ============================================================ -->
+    <mxCell id="LEG" value="Legend" style="swimlane;startSize=22;fillColor=#ffffff;strokeColor=#999999;fontSize=10;fontStyle=1;" vertex="1" parent="1">
+      <mxGeometry x="10" y="1085" width="1680" height="0" as="geometry" />
+    </mxCell>
+
+    <!-- ============================================================
+         ARROWS — DATA FLOW
+         ============================================================ -->
+
+    <!-- Patient Portal → Patient API (REST/JSON) -->
+    <mxCell id="E1" value="REST/JSON" style="edgeStyle=orthogonalEdgeStyle;html=1;rounded=1;strokeColor=#0066cc;strokeWidth=2;fontStyle=2;fontSize=8;exitX=0.5;exitY=1;exitDx=0;exitDy=0;entryX=0.5;entryY=0;entryDx=0;entryDy=0;" edge="1" source="PPG" target="PAR" parent="1">
+      <mxGeometry relative="1" as="geometry" />
+    </mxCell>
+
+    <!-- Patient Portal → Messaging/Inbox (dashed) -->
+    <mxCell id="E2" value="chat / inbox" style="edgeStyle=orthogonalEdgeStyle;html=1;rounded=1;strokeColor=#d6b656;strokeWidth=1.5;dashed=1;fontStyle=2;fontSize=8;" edge="1" source="PP4" target="MR" parent="1">
+      <mxGeometry relative="1" as="geometry" />
+    </mxCell>
+
+    <!-- Patient Portal → Documents (dashed) -->
+    <mxCell id="E3" value="upload / download" style="edgeStyle=orthogonalEdgeStyle;html=1;rounded=1;strokeColor=#d79b00;strokeWidth=1.5;dashed=1;fontStyle=2;fontSize=8;" edge="1" source="PP1" target="DR" parent="1">
+      <mxGeometry relative="1" as="geometry" />
+    </mxCell>
+
+    <!-- Landing → Auth -->
+    <mxCell id="E4" value="login / register" style="edgeStyle=orthogonalEdgeStyle;html=1;rounded=1;strokeColor=#9673a6;strokeWidth=2;fontStyle=2;fontSize=8;" edge="1" source="LP" target="AR" parent="1">
+      <mxGeometry relative="1" as="geometry" />
+    </mxCell>
+
+    <!-- Hospital Portal → Hospital API -->
+    <mxCell id="E5" value="REST/JSON" style="edgeStyle=orthogonalEdgeStyle;html=1;rounded=1;strokeColor=#5b9e5b;strokeWidth=2;fontStyle=2;fontSize=8;exitX=0.5;exitY=1;exitDx=0;exitDy=0;entryX=0.5;entryY=0;entryDx=0;entryDy=0;" edge="1" source="HPG" target="HAR" parent="1">
+      <mxGeometry relative="1" as="geometry" />
+    </mxCell>
+
+    <!-- Hospital Portal → Messaging (dashed) -->
+    <mxCell id="E6" value="chat / inbox" style="edgeStyle=orthogonalEdgeStyle;html=1;rounded=1;strokeColor=#d6b656;strokeWidth=1.5;dashed=1;fontStyle=2;fontSize=8;" edge="1" source="HP4" target="MR" parent="1">
+      <mxGeometry relative="1" as="geometry" />
+    </mxCell>
+
+    <!-- Auth → DB -->
+    <mxCell id="E7" style="edgeStyle=orthogonalEdgeStyle;html=1;rounded=1;strokeColor=#9673a6;strokeWidth=1.5;exitX=0.5;exitY=1;exitDx=0;exitDy=0;entryX=0.1;entryY=0;entryDx=0;entryDy=0;" edge="1" source="AR" target="DBG" parent="1">
+      <mxGeometry relative="1" as="geometry" />
+    </mxCell>
+
+    <!-- Patient API → DB -->
+    <mxCell id="E8" style="edgeStyle=orthogonalEdgeStyle;html=1;rounded=1;strokeColor=#0066cc;strokeWidth=2;exitX=0.5;exitY=1;exitDx=0;exitDy=0;entryX=0.3;entryY=0;entryDx=0;entryDy=0;" edge="1" source="PAR" target="DBG" parent="1">
+      <mxGeometry relative="1" as="geometry" />
+    </mxCell>
+
+    <!-- Patient API → match_patient() -->
+    <mxCell id="E9" value="match_patient()" style="edgeStyle=orthogonalEdgeStyle;html=1;rounded=1;strokeColor=#d79b00;strokeWidth=2;fontStyle=3;fontSize=8;exitX=0.5;exitY=1;exitDx=0;exitDy=0;entryX=0.5;entryY=0;entryDx=0;entryDy=0;" edge="1" source="PAR" target="PL3" parent="1">
+      <mxGeometry relative="1" as="geometry" />
+    </mxCell>
+
+    <!-- Patient API → hospitals_for_trial() -->
+    <mxCell id="E10" value="hospitals_for_trial()" style="edgeStyle=orthogonalEdgeStyle;html=1;rounded=1;strokeColor=#d79b00;strokeWidth=1.5;dashed=1;fontStyle=3;fontSize=8;exitX=0.75;exitY=1;exitDx=0;exitDy=0;entryX=0.5;entryY=0;entryDx=0;entryDy=0;" edge="1" source="PAR" target="PL5" parent="1">
+      <mxGeometry relative="1" as="geometry" />
+    </mxCell>
+
+    <!-- Hospital API → DB -->
+    <mxCell id="E11" style="edgeStyle=orthogonalEdgeStyle;html=1;rounded=1;strokeColor=#5b9e5b;strokeWidth=2;exitX=0.5;exitY=1;exitDx=0;exitDy=0;entryX=0.5;entryY=0;entryDx=0;entryDy=0;" edge="1" source="HAR" target="DBG" parent="1">
+      <mxGeometry relative="1" as="geometry" />
+    </mxCell>
+
+    <!-- Hospital API → trials_for_hospital() -->
+    <mxCell id="E12" value="trials_for_hospital()" style="edgeStyle=orthogonalEdgeStyle;html=1;rounded=1;strokeColor=#d79b00;strokeWidth=2;fontStyle=3;fontSize=8;exitX=0.5;exitY=1;exitDx=0;exitDy=0;entryX=0.5;entryY=0;entryDx=0;entryDy=0;" edge="1" source="HAR" target="PL4" parent="1">
+      <mxGeometry relative="1" as="geometry" />
+    </mxCell>
+
+    <!-- Messaging → DB (connections + messages tables) -->
+    <mxCell id="E13" style="edgeStyle=orthogonalEdgeStyle;html=1;rounded=1;strokeColor=#d6b656;strokeWidth=1.5;exitX=0.5;exitY=1;exitDx=0;exitDy=0;entryX=0.75;entryY=0;entryDx=0;entryDy=0;" edge="1" source="MR" target="DBG" parent="1">
+      <mxGeometry relative="1" as="geometry" />
+    </mxCell>
+
+    <!-- Documents → DB (updates patient documents JSON) -->
+    <mxCell id="E14" style="edgeStyle=orthogonalEdgeStyle;html=1;rounded=1;strokeColor=#d79b00;strokeWidth=1.5;exitX=0.5;exitY=1;exitDx=0;exitDy=0;entryX=0.88;entryY=0;entryDx=0;entryDy=0;" edge="1" source="DR" target="DBG" parent="1">
+      <mxGeometry relative="1" as="geometry" />
+    </mxCell>
+
+    <!-- Documents → File Storage -->
+    <mxCell id="E15" value="file I/O" style="edgeStyle=orthogonalEdgeStyle;html=1;rounded=1;strokeColor=#d79b00;strokeWidth=1.5;dashed=1;fontStyle=3;fontSize=8;" edge="1" source="DR" target="S3" parent="1">
+      <mxGeometry relative="1" as="geometry" />
+    </mxCell>
+
+    <!-- Boot Thread → DB (seed data) -->
+    <mxCell id="E16" value="① seed" style="edgeStyle=orthogonalEdgeStyle;html=1;rounded=1;strokeColor=#666;strokeWidth=1.5;dashed=1;fontStyle=3;fontSize=8;exitX=0.5;exitY=1;exitDx=0;exitDy=0;entryX=1;entryY=0;entryDx=0;entryDy=0;" edge="1" source="SR" target="DBG" parent="1">
+      <mxGeometry relative="1" as="geometry" />
+    </mxCell>
+
+    <!-- Boot Thread → Pipeline (load + train) -->
+    <mxCell id="E17" value="② load  ③ train" style="edgeStyle=orthogonalEdgeStyle;html=1;rounded=1;strokeColor=#666;strokeWidth=1.5;dashed=1;fontStyle=3;fontSize=8;exitX=0.5;exitY=1;exitDx=0;exitDy=0;entryX=1;entryY=0;entryDx=0;entryDy=0;" edge="1" source="SR" target="PLG" parent="1">
+      <mxGeometry relative="1" as="geometry" />
+    </mxCell>
+
+    <!-- DB → secondlife.db file -->
+    <mxCell id="E18" value="read / write" style="edgeStyle=orthogonalEdgeStyle;html=1;rounded=1;strokeColor=#5b9e5b;strokeWidth=2;fontStyle=2;fontSize=8;" edge="1" source="DBG" target="S1" parent="1">
+      <mxGeometry relative="1" as="geometry" />
+    </mxCell>
+
+    <!-- Pipeline → model_cache.pkl -->
+    <mxCell id="E19" value="load / save" style="edgeStyle=orthogonalEdgeStyle;html=1;rounded=1;strokeColor=#d79b00;strokeWidth=2;fontStyle=2;fontSize=8;" edge="1" source="PLG" target="S2" parent="1">
+      <mxGeometry relative="1" as="geometry" />
+    </mxCell>
+
+    <!-- pipeline.load() → Synthea CSVs -->
+    <mxCell id="E20" value="read on boot" style="edgeStyle=orthogonalEdgeStyle;html=1;rounded=1;strokeColor=#5b9e5b;strokeWidth=1.5;dashed=1;fontStyle=3;fontSize=8;exitX=0.2;exitY=1;exitDx=0;exitDy=0;entryX=0.5;entryY=0;entryDx=0;entryDy=0;" edge="1" source="PL1" target="S4" parent="1">
+      <mxGeometry relative="1" as="geometry" />
+    </mxCell>
+
+    <!-- pipeline.load() → Trial CSVs -->
+    <mxCell id="E21" value="read on boot" style="edgeStyle=orthogonalEdgeStyle;html=1;rounded=1;strokeColor=#d79b00;strokeWidth=1.5;dashed=1;fontStyle=3;fontSize=8;exitX=0.8;exitY=1;exitDx=0;exitDy=0;entryX=0.5;entryY=0;entryDx=0;entryDy=0;" edge="1" source="PL1" target="S5" parent="1">
+      <mxGeometry relative="1" as="geometry" />
+    </mxCell>
+
+  </root>
+</mxGraphModel>

Architecture Diagrams/secondlife workflow architecture diagrams.html

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+<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>Second Life — Architecture Diagrams</title>
+<script src="https://cdn.jsdelivr.net/npm/mermaid@10/dist/mermaid.min.js"></script>
+<style>
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+  }
+
+  @media (max-width: 900px) {
+    .grid-2 { grid-template-columns: 1fr; }
+    body { padding: 1rem; }
+  }
+</style>
+</head>
+<body>
+
+<header>
+  <h1>⚕️ Second Life — Architecture Diagrams</h1>
+  <p>DSCI 5260 · Group 7 · AI-powered Patient-to-Clinical-Trial Matching Platform · Flask + SQLite + Random Forest</p>
+</header>
+
+<div class="toc">
+  <h3>Contents</h3>
+  <ol>
+    <li><a href="#s1">System Architecture Overview</a></li>
+    <li><a href="#s2">Database Schema (ER Diagram)</a></li>
+    <li><a href="#s3">ML Pipeline — Boot, Train, Infer</a></li>
+    <li><a href="#s4">Patient User Journey (Sequence)</a></li>
+    <li><a href="#s5">Hospital User Journey (Sequence)</a></li>
+    <li><a href="#s6">Tiered Matching Logic</a></li>
+    <li><a href="#s7">Feature Engineering — 17 Features</a></li>
+  </ol>
+</div>
+
+<!-- =========================================================
+     1. SYSTEM ARCHITECTURE OVERVIEW
+     ========================================================= -->
+<section id="s1">
+  <h2>1. System Architecture Overview</h2>
+  <p class="subtitle">
+    Four-layer architecture: Browser &rarr; Flask Server &rarr; Business Logic (DB + ML) &rarr; Storage.
+    The pipeline boots in a background thread so the server is immediately available.
+  </p>
+  <div class="diagram-wrap">
+    <div class="mermaid">
+flowchart LR
+    subgraph BROWSER["🌐  Browser  (Client)"]
+        direction TB
+        LAND["<b>landing.html</b><br/>Login · Register<br/>(Patient &amp; Hospital)"]
+        subgraph PAT_SPA["Patient Portal — patient.html"]
+            direction TB
+            PA["My Profile<br/><small>conditions · meds · docs upload</small>"]
+            PB["Suggested Trials<br/><small>AI-ranked top-20</small>"]
+            PC["My Connections<br/><small>status · chat</small>"]
+            PD["Inbox 🔴<br/><small>unread count badge</small>"]
+        end
+        subgraph HOSP_SPA["Hospital Portal — hospital.html"]
+            direction TB
+            HA["Available Patients<br/><small>opt-in only, excl. connected</small>"]
+            HB["Search by Condition<br/><small>incl. connected toggle</small>"]
+            HC["My Trials<br/><small>active trials matched to hospital</small>"]
+            HD["Inbox 🔴<br/><small>unread count badge</small>"]
+            HE["My Connections<br/><small>accept · reject · complete · chat</small>"]
+            HF["My Profile<br/><small>name · location · conditions</small>"]
+        end
+    end
+
+    subgraph SERVER["⚙️  Flask Server — app.py  (:5000)"]
+        direction TB
+        subgraph AUTH_GRP["Auth (no session required)"]
+            AU["/auth/patient/register<br/>/auth/patient/login<br/>/auth/hospital/register<br/>/auth/hospital/login<br/>/auth/logout"]
+        end
+        subgraph PAT_GRP["Patient API"]
+            PAR["/api/patient/profile  GET · POST<br/>/api/patient/matches  GET<br/>/api/patient/interests  GET · POST · DELETE<br/>/api/patient/connections  GET<br/>/api/patient/connect  POST<br/>/api/patient/hospitals-for-trial  GET"]
+        end
+        subgraph MSG_GRP["Messaging + Inbox"]
+            MR["/api/patient/connections/&lt;cid&gt;/messages  GET · POST<br/>/api/patient/inbox  GET<br/>/api/hospital/connections/&lt;cid&gt;/messages  GET · POST<br/>/api/hospital/inbox  GET"]
+        end
+        subgraph HOSP_GRP["Hospital API"]
+            HAR["/api/hospital/profile  GET · POST<br/>/api/hospital/patients  GET<br/>/api/hospital/trials  GET<br/>/api/hospital/connect  POST<br/>/api/hospital/connections  GET<br/>/api/hospital/connections/&lt;cid&gt;/status  PUT"]
+        end
+        subgraph DOC_GRP["Documents"]
+            DR["/api/patient/documents  GET · POST<br/>/api/patient/documents/&lt;id&gt;  DELETE<br/>/api/patient/documents/&lt;id&gt;/download  GET"]
+        end
+        subgraph SHARED_GRP["Shared"]
+            SR["/api/status  GET<br/>/api/conditions/autocomplete  GET"]
+        end
+        BOOT["<b>Boot Thread</b><br/>init_db()<br/>pipeline.load()<br/>pipeline.train()"]
+    end
+
+    subgraph LOGIC["🧠  Business Logic"]
+        direction TB
+        subgraph DB_LAYER["database.py — SQLite Layer"]
+            T1[("patient_accounts<br/><small>25 rows</small>")]
+            T2[("hospital_accounts<br/><small>3 rows</small>")]
+            T3[("patient_trial_interests")]
+            T4[("connections")]
+            T5[("connection_messages")]
+        end
+        subgraph ML_LAYER["pipeline.py — ML Engine"]
+            ML1["Data Loader<br/><small>CSV → in-memory dicts</small>"]
+            ML2["Feature Engineer<br/><small>17 features per patient-trial pair</small>"]
+            ML3["RF + Isotonic Cal.<br/><small>90k pairs · AUC 0.60</small>"]
+            ML4["match_patient()"]
+            ML5["trials_for_hospital()"]
+            ML6["hospitals_for_trial()"]
+        end
+    end
+
+    subgraph STORAGE["💾  Storage"]
+        direction TB
+        SDB[("secondlife.db<br/><small>SQLite file</small>")]
+        SCACHE[("model_cache.pkl<br/><small>RF + calibrator</small>")]
+        SFILES["uploads/patient_docs/<br/><small>per-patient dirs</small>"]
+        SCSV_P["Synthea CSVs<br/><small>4 files · ~1M rows</small>"]
+        SCSV_T["ClinicalTrials CSVs<br/><small>7 files · ~2M rows</small>"]
+    end
+
+    LAND -->|"POST /auth/*"| AU
+    PAT_SPA -->|"REST/JSON"| PAR
+    PAT_SPA -->|"REST/JSON"| MSG_GRP
+    PAT_SPA -->|"multipart"| DOC_GRP
+    HOSP_SPA -->|"REST/JSON"| HAR
+    HOSP_SPA -->|"REST/JSON"| MSG_GRP
+    PAT_SPA & HOSP_SPA -->|"GET"| SHARED_GRP
+
+    AU & PAR & HAR --> DB_LAYER
+    MSG_GRP --> T4 & T5
+    DOC_GRP --> T1
+    DOC_GRP -->|"file I/O"| SFILES
+
+    PAR -->|"match_patient()"| ML4
+    PAR -->|"hospitals_for_trial()"| ML6
+    HAR -->|"trials_for_hospital()"| ML5
+
+    BOOT -->|"seeds data"| T1
+    BOOT -->|"load + train"| ML_LAYER
+
+    DB_LAYER <-->|"read/write"| SDB
+    ML3 <-->|"load/save"| SCACHE
+    ML1 -->|"read on boot"| SCSV_P
+    ML1 -->|"read on boot"| SCSV_T
+
+    ML1 --> ML2 --> ML3 --> ML4 & ML5 & ML6
+    </div>
+    <div class="note">
+      <strong>Boot sequence:</strong> On startup a background thread calls <code>init_db()</code> (seeds 25 patients + 3 hospitals),
+      then <code>pipeline.load()</code> (reads all CSVs into memory), then <code>pipeline.train()</code>
+      (builds RF model or loads <code>model_cache.pkl</code>). The Flask server is immediately available during boot;
+      <code>GET /api/status</code> returns <code>ready: false</code> until the thread completes.
+    </div>
+  </div>
+</section>
+
+<!-- =========================================================
+     2. DATABASE SCHEMA
+     ========================================================= -->
+<section id="s2">
+  <h2>2. Database Schema</h2>
+  <p class="subtitle">
+    SQLite database (<code>secondlife.db</code>) with 5 tables.
+    JSON columns (conditions, medications, documents, research_conditions) are stored as TEXT and parsed by
+    <code>_row_to_dict()</code> in database.py.
+  </p>
+  <div class="diagram-wrap">
+    <div class="mermaid">
+erDiagram
+    patient_accounts {
+        TEXT id PK
+        TEXT username UK
+        TEXT password_hash
+        TEXT synthea_id "null for hand-made patients"
+        TEXT first_name
+        TEXT last_name
+        TEXT dob
+        TEXT gender
+        TEXT address
+        TEXT conditions "JSON array of strings"
+        TEXT medications "JSON array of strings"
+        TEXT documents "JSON array of file metadata dicts"
+        INTEGER open_to_trials "0 or 1"
+        TEXT created_at
+    }
+
+    hospital_accounts {
+        TEXT id PK
+        TEXT username UK
+        TEXT password_hash
+        TEXT hospital_name
+        TEXT location "City, ST format"
+        TEXT research_conditions "JSON array of strings"
+        TEXT created_at
+    }
+
+    patient_trial_interests {
+        TEXT id PK
+        TEXT patient_id FK
+        TEXT trial_id "NCT number"
+        TEXT trial_title
+        REAL match_score
+        TEXT status "interested or withdrawn"
+        TEXT created_at
+    }
+
+    connections {
+        TEXT id PK
+        TEXT patient_id FK
+        TEXT hospital_id FK
+        TEXT trial_id "nullable NCT number"
+        TEXT trial_title
+        TEXT initiated_by "patient or hospital"
+        TEXT status "pending accepted rejected completed"
+        TEXT message
+        TEXT created_at
+    }
+
+    connection_messages {
+        TEXT id PK
+        TEXT connection_id FK
+        TEXT sender_role "patient or hospital"
+        TEXT sender_id
+        TEXT body "max 2000 chars"
+        TEXT created_at
+        INTEGER is_read "0 or 1"
+    }
+
+    patient_accounts ||--o{ patient_trial_interests : "saves interests"
+    patient_accounts ||--o{ connections : "initiates or receives"
+    hospital_accounts ||--o{ connections : "initiates or receives"
+    connections ||--o{ connection_messages : "contains messages"
+    </div>
+    <div class="note">
+      <strong>Uniqueness:</strong> <code>connections</code> has a composite unique index
+      <code>idx_conn_unique ON connections(patient_id, hospital_id, COALESCE(trial_id, ''))</code>
+      to prevent duplicate connection requests even when <code>trial_id</code> is NULL.
+      <code>patient_trial_interests</code> has <code>UNIQUE(patient_id, trial_id)</code>.
+      <code>connection_messages</code> has index <code>idx_msgs_conn ON (connection_id, created_at)</code>.
+    </div>
+  </div>
+</section>
+
+<!-- =========================================================
+     3. ML PIPELINE
+     ========================================================= -->
+<section id="s3">
+  <h2>3. ML Pipeline — Boot, Train, Infer</h2>
+  <p class="subtitle">
+    <code>pipeline.py</code> — Data loading from 11 CSV files, pseudo-label generation,
+    Random Forest training with isotonic calibration, and 3 inference functions.
+  </p>
+  <div class="diagram-wrap">
+    <div class="mermaid">
+flowchart TD
+    subgraph BOOT_TH["Boot Thread (background, server stays live during this)"]
+        direction LR
+        BT1["pipeline.load()"] --> BT2["pipeline.train()"]
+    end
+
+    subgraph LOAD_PHASE["load() — CSV Ingestion into Memory"]
+        direction LR
+        subgraph TRIAL_CSV["ClinicalTrials.gov CSVs"]
+            TC1["trail_studies.csv<br/><small>→ trial_profiles dict<br/>→ active_trial_ids set<br/>65k recruiting trials</small>"]
+            TC2["trail_conditions.csv<br/><small>→ trial_conditions dict<br/>34k trials with conditions</small>"]
+            TC3["trail_eligibilities.csv<br/><small>→ age range + gender per trial</small>"]
+            TC4["trail_facilities.csv<br/><small>→ trial_us_states dict<br/>→ trial_facility_tokens dict<br/>189k trials with US geo</small>"]
+            TC5["trail_interventions.csv<br/><small>→ trial_drug_keywords dict<br/>196k trials with drug data</small>"]
+            TC6["trail_brief_summaries.csv<br/><small>→ trial_summaries dict</small>"]
+            TC7["trail_keywords.csv<br/><small>→ trial_keywords dict</small>"]
+        end
+        subgraph SYNTHEA_CSV["Synthea Patient CSVs"]
+            SC1["patients_details.csv<br/><small>→ patient_demographics dict<br/>265k patients</small>"]
+            SC2["final_patients_conditions.csv<br/><small>→ patient_conditions dict<br/>106 overlapping conditions</small>"]
+            SC3["patients_medications.csv<br/><small>→ patient_medications dict<br/>213k patients</small>"]
+            SC4["patients_observations.csv<br/><small>→ patient_lab_types dict<br/>23k patients</small>"]
+        end
+    end
+
+    subgraph TRAIN_PHASE["train() — Model Training"]
+        direction TB
+        TR1["Sample 3000 patients × 30 trials<br/>+ random negatives<br/><small>GroupShuffleSplit 80/20 patient-level</small>"]
+        TR2["_compute_features()<br/><small>17-feature vector per patient-trial pair<br/>~90k training pairs total</small>"]
+        TR3["Pseudo-label generation<br/><small>6-feature weighted score >= 0.5<br/>+ 15% hash-deterministic noise<br/>→ 82.1% positive rate</small>"]
+        TR4["RandomForestClassifier<br/><small>n_estimators=200, max_depth=12<br/>class_weight=balanced</small>"]
+        TR5["CalibratedClassifierCV<br/><small>method=isotonic, cv=3</small>"]
+        TR6["Save model_cache.pkl<br/><small>skip training on next boot</small>"]
+        TR1 --> TR2 --> TR3 --> TR4 --> TR5 --> TR6
+    end
+
+    subgraph INFER["Inference — 3 Matching Functions"]
+        direction LR
+        IN1["<b>match_patient()</b><br/><small>Input: conditions, age, gender, patient_id<br/>Output: top-k trials ranked by<br/>combined_score = 0.6×eligibility_prob + 0.4×match_score</small>"]
+        IN2["<b>trials_for_hospital()</b><br/><small>Input: hospital name, location, research_conditions<br/>Output: active trials in 3 tiers<br/>Tier1: facility name Jaccard >= 0.25<br/>Tier2: same US state<br/>Tier3: condition overlap</small>"]
+        IN3["<b>hospitals_for_trial()</b><br/><small>Input: trial_id<br/>Output: DB hospitals in 4 tiers<br/>Tier1: name Jaccard >= 0.25<br/>Tier2: same state<br/>Tier3: condition overlap<br/>Tier4: fallback</small>"]
+    end
+
+    BOOT_TH --> LOAD_PHASE
+    BOOT_TH --> TRAIN_PHASE
+    LOAD_PHASE --> TR1
+    TR5 --> IN1
+    LOAD_PHASE --> IN2
+    LOAD_PHASE --> IN3
+    </div>
+    <div class="note">
+      <strong>Cache behaviour:</strong> If <code>model_cache.pkl</code> exists on disk, <code>train()</code> loads the cached RF + calibrator
+      and skips the ~2-3 min training step. Delete the file to force a full retrain (required after changing features or pipeline logic).
+    </div>
+  </div>
+</section>
+
+<!-- =========================================================
+     4. PATIENT JOURNEY
+     ========================================================= -->
+<section id="s4">
+  <h2>4. Patient User Journey</h2>
+  <p class="subtitle">
+    Key request-response flows through the patient portal, from registration to messaging.
+  </p>
+  <div class="diagram-wrap">
+    <div class="mermaid">
+sequenceDiagram
+    actor Patient
+    participant UI as patient.html
+    participant Flask as Flask (app.py)
+    participant DB as database.py
+    participant ML as pipeline.py
+    participant FS as File System
+
+    Note over Patient,FS: Registration / Login
+    Patient->>UI: Fill register form
+    UI->>Flask: POST /auth/patient/register
+    Flask->>DB: create_patient()
+    DB-->>Flask: patient dict (or 409 username taken)
+    Flask-->>UI: session cookie + patient JSON
+    UI->>UI: Render portal, call checkStatus()
+
+    Note over Patient,FS: Pipeline ready check
+    UI->>Flask: GET /api/status
+    Flask-->>UI: ready: false (202) or ready: true + stats
+    UI->>UI: Show yellow banner if not ready; startStatusPoll() every 5s
+
+    Note over Patient,FS: Profile update
+    Patient->>UI: Edit conditions / medications / toggle open_to_trials
+    UI->>Flask: POST /api/patient/profile
+    Flask->>DB: update_patient_profile()
+
+    Note over Patient,FS: Get AI trial matches
+    Patient->>UI: Click "Suggested Trials" tab
+    UI->>Flask: GET /api/patient/matches
+    Flask->>ML: match_patient(conditions, age, gender, patient_id)
+    ML->>ML: _compute_features() x all active trials
+    ML->>ML: RF.predict_proba() → calibrated eligibility prob
+    ML->>ML: combined_score = 0.6×prob + 0.4×match_score
+    ML-->>Flask: top-20 trials sorted by combined_score
+    Flask-->>UI: results array with 24 fields per trial
+
+    Note over Patient,FS: Connect with a hospital
+    Patient->>UI: Click "Connect with Hospital" on a trial card
+    UI->>Flask: GET /api/patient/hospitals-for-trial?trial_id=NCT...
+    Flask->>ML: hospitals_for_trial(trial_id)
+    ML-->>Flask: tiered hospital list (Tier 1..4)
+    Flask-->>UI: hospitals array
+    UI->>UI: Render modal — green section (Tier1) + grey section (Tier2-4)
+    Patient->>UI: Select hospital + enter message + submit
+    UI->>Flask: POST /api/patient/connect
+    Flask->>DB: connection_exists()? if no → create_connection()
+    DB-->>Flask: connection dict (or 409 duplicate)
+    Flask-->>UI: success redirect to connections tab
+
+    Note over Patient,FS: Inbox and messaging
+    Patient->>UI: Click "Inbox" tab
+    UI->>Flask: GET /api/patient/inbox
+    Flask->>DB: get_patient_inbox_threads(patient_id)
+    DB-->>Flask: threads with last_message + unread_count
+    Flask-->>UI: threads array + update badge
+    Patient->>UI: Click "Open" on a thread
+    UI->>Flask: GET /api/patient/connections/{cid}/messages
+    Flask->>DB: mark_messages_read(role=hospital)
+    Flask->>DB: get_connection_messages(cid)
+    Flask-->>UI: messages array
+    Patient->>UI: Type reply + Enter or Send
+    UI->>Flask: POST /api/patient/connections/{cid}/messages
+    Flask->>DB: create_connection_message(role=patient)
+
+    Note over Patient,FS: Document upload
+    Patient->>UI: Select file in Documents section
+    UI->>Flask: POST /api/patient/documents (multipart/form-data)
+    Flask->>Flask: secure_filename, validate extension + 10MB limit
+    Flask->>FS: Save to uploads/patient_docs/{pid}/{doc_id}_filename
+    Flask->>DB: update_patient_profile(documents=[...append])
+    Flask-->>UI: document metadata dict
+    </div>
+  </div>
+</section>
+
+<!-- =========================================================
+     5. HOSPITAL JOURNEY
+     ========================================================= -->
+<section id="s5">
+  <h2>5. Hospital User Journey</h2>
+  <p class="subtitle">
+    Key request-response flows through the hospital portal, from login to connection management and messaging.
+  </p>
+  <div class="diagram-wrap">
+    <div class="mermaid">
+sequenceDiagram
+    actor Hospital
+    participant UI as hospital.html
+    participant Flask as Flask (app.py)
+    participant DB as database.py
+    participant ML as pipeline.py
+
+    Note over Hospital,ML: Login
+    Hospital->>UI: Enter credentials
+    UI->>Flask: POST /auth/hospital/login
+    Flask->>DB: authenticate_hospital()
+    DB-->>Flask: hospital dict
+    Flask-->>UI: session cookie + hospital JSON
+
+    Note over Hospital,ML: Browse available patients
+    Hospital->>UI: Click "Available Patients" tab (default)
+    UI->>Flask: GET /api/hospital/patients
+    Flask->>DB: get_open_patients_for_hospital(include_connected=False)
+    DB-->>Flask: patients with open_to_trials=1 excluding already-connected
+    Flask-->>UI: patient cards (25 opt-in patients total)
+
+    Note over Hospital,ML: Search by condition
+    Hospital->>UI: Type condition + toggle "include connected"
+    UI->>Flask: GET /api/hospital/patients?condition=diabetes&include_connected=true
+    Flask->>DB: get_open_patients_for_hospital(condition_filter, include_connected=True)
+    Flask-->>UI: filtered patient list
+
+    Note over Hospital,ML: View matched trials
+    Hospital->>UI: Click "My Trials" tab
+    UI->>Flask: GET /api/hospital/trials
+    Flask->>ML: trials_for_hospital(hospital_name, location, research_conditions)
+    ML->>ML: Tier1 — facility name Jaccard >= 0.25 + is_active
+    ML->>ML: Tier2 — same US state + is_active
+    ML->>ML: Tier3 — condition overlap + is_active
+    ML-->>Flask: active trial list with match_tier and match_reason
+    Flask-->>UI: trial cards with status, phase, facility, ClinicalTrials.gov link
+
+    Note over Hospital,ML: Connect with a patient
+    Hospital->>UI: Click "Connect" on a patient card
+    UI->>Flask: POST /api/hospital/connect
+    Flask->>DB: create_connection(initiated_by=hospital)
+    DB-->>Flask: connection dict (or 409 duplicate)
+
+    Note over Hospital,ML: Manage connections
+    Hospital->>UI: Click "My Connections" tab
+    UI->>Flask: GET /api/hospital/connections
+    Flask->>DB: get_hospital_connections(hospital_id)
+    Flask-->>UI: connection table with patient details
+    Hospital->>UI: Click "Accept" / "Reject" / "Complete"
+    UI->>Flask: PUT /api/hospital/connections/{cid}/status
+    Flask->>DB: update_connection_status(cid, status)
+
+    Note over Hospital,ML: Inbox and messaging
+    Hospital->>UI: Click "Inbox" tab
+    UI->>Flask: GET /api/hospital/inbox
+    Flask->>DB: get_hospital_inbox_threads(hospital_id)
+    DB-->>Flask: threads sorted by last_message_at DESC
+    Flask-->>UI: thread list with unread_count, update nav badge
+    Hospital->>UI: Click "Open" on a thread
+    UI->>Flask: GET /api/hospital/connections/{cid}/messages
+    Flask->>DB: mark_messages_read(role=hospital)
+    Flask->>DB: get_connection_messages(cid)
+    Flask-->>UI: messages in chronological order
+    Hospital->>UI: Type reply + Send
+    UI->>Flask: POST /api/hospital/connections/{cid}/messages
+    Flask->>DB: create_connection_message(role=hospital)
+
+    Note over Hospital,ML: Update profile
+    Hospital->>UI: Edit name / location / research conditions
+    UI->>Flask: POST /api/hospital/profile
+    Flask->>DB: update_hospital_profile()
+    Flask-->>UI: updated hospital dict
+    UI->>UI: Update navbar name live (no page reload)
+    </div>
+  </div>
+</section>
+
+<!-- =========================================================
+     6. TIERED MATCHING
+     ========================================================= -->
+<section id="s6">
+  <h2>6. Tiered Matching Logic</h2>
+  <p class="subtitle">
+    Two reverse-matching functions sharing the same 4-tier cascade. Both use
+    <code>_name_tokens()</code> + Jaccard similarity for Tier 1, US state extraction for Tier 2,
+    and condition set intersection for Tier 3.
+  </p>
+  <div class="grid-2">
+    <div>
+      <h3 style="color:#3fb950;margin-bottom:.75rem;font-size:1rem;">
+        hospitals_for_trial(trial_id) — Patient selects a trial
+      </h3>
+      <div class="diagram-wrap">
+        <div class="mermaid">
+flowchart TD
+    START_A["Patient clicks<br/>'Connect with Hospital'<br/>on trial NCTxxxxxx"]
+
+    subgraph T1A["Tier 1 — Verified Site"]
+        C1A["For each hospital in DB:<br/>Jaccard(hospital name tokens,<br/>ANY trial facility name tokens) >= 0.25"]
+        R1A["🟢 match_reason:<br/>'verified site on this trial'"]
+    end
+
+    subgraph T2A["Tier 2 — Same State"]
+        C2A["Hospital state extracted from<br/>'City, ST' location field<br/>== any trial US facility state"]
+        R2A["⬜ match_reason:<br/>'in same state as a trial site'"]
+    end
+
+    subgraph T3A["Tier 3 — Condition Overlap"]
+        C3A["hospital.research_conditions<br/>∩ trial.conditions ≠ empty"]
+        R3A["⬜ match_reason:<br/>'researches related conditions'"]
+    end
+
+    subgraph T4A["Tier 4 — Fallback"]
+        C4A["Trial has no US facility data<br/>all remaining hospitals included"]
+        R4A["⬜ match_reason: ''"]
+    end
+
+    MODAL["Render connect modal<br/>🟢 VERIFIED TRIAL SITES header<br/>⬜ RELATED HOSPITALS header<br/>(Tiers 2–4)"]
+
+    START_A --> T1A --> T2A --> T3A --> T4A --> MODAL
+        </div>
+      </div>
+    </div>
+
+    <div>
+      <h3 style="color:#79c0ff;margin-bottom:.75rem;font-size:1rem;">
+        trials_for_hospital(name, loc, conditions) — Hospital views My Trials
+      </h3>
+      <div class="diagram-wrap">
+        <div class="mermaid">
+flowchart TD
+    START_B["Hospital opens<br/>'My Trials' tab"]
+
+    subgraph T1B["Tier 1 — Name Matched"]
+        C1B["For each active trial:<br/>Jaccard(hospital name tokens,<br/>trial facility name tokens) >= 0.25<br/>+ is_active = True"]
+        R1B["match_tier: 1<br/>match_reason:<br/>'name matched to trial site'"]
+    end
+
+    subgraph T2B["Tier 2 — Same State"]
+        C2B["Hospital state<br/>== trial US facility state<br/>+ is_active = True"]
+        R2B["match_tier: 2<br/>match_reason:<br/>'in same state as trial site'"]
+    end
+
+    subgraph T3B["Tier 3 — Condition Overlap"]
+        C3B["hospital.research_conditions<br/>∩ trial.conditions ≠ empty<br/>+ is_active = True"]
+        R3B["match_tier: 3<br/>match_reason:<br/>'researches related conditions'"]
+    end
+
+    SORT["Sort by match_tier ASC<br/>cap at top_k=20<br/>Return trial cards with<br/>status · phase · facility · summary"]
+
+    START_B --> T1B --> T2B --> T3B --> SORT
+        </div>
+      </div>
+    </div>
+  </div>
+
+  <div style="margin-top:1.5rem">
+    <h3 style="color:#c9d1d9;margin-bottom:.75rem;font-size:1rem;">Shared Helpers</h3>
+    <div class="diagram-wrap">
+      <div class="mermaid">
+flowchart LR
+    subgraph TOK["_name_tokens(name)"]
+        TK1["Lowercase all words"]
+        TK2["Remove stopwords:<br/>hospital, medical, center, clinic, university,<br/>health, care, system, institute, general,<br/>regional, national, community, the, of, and, at…"]
+        TK3["Remove words shorter than 3 chars"]
+        TK4["Return frozenset of tokens"]
+        TK1 --> TK2 --> TK3 --> TK4
+    end
+
+    subgraph JAC["Jaccard Similarity"]
+        J1["score = |A ∩ B| / |A ∪ B|"]
+        J2["Best score across ALL<br/>facility names for the trial"]
+        J1 --> J2
+    end
+
+    subgraph STATE["State Extraction"]
+        S1["Parse 'City, ST' location string"]
+        S2["Lookup ST abbreviation<br/>in US_STATE_MAP dict<br/>→ full name e.g. 'Massachusetts'"]
+        S3["Compare against<br/>trial_us_states set<br/>(from trail_facilities.csv)"]
+        S1 --> S2 --> S3
+    end
+
+    TOK --> JAC
+    TOK --> STATE
+      </div>
+    </div>
+  </div>
+</section>
+
+<!-- =========================================================
+     7. FEATURE ENGINEERING
+     ========================================================= -->
+<section id="s7">
+  <h2>7. Feature Engineering — 17 Features</h2>
+  <p class="subtitle">
+    All features are normalised to 0–1. Computed by <code>_compute_features(patient_row, trial_id)</code>
+    in pipeline.py. Used for both training pseudo-labels and inference scoring.
+  </p>
+  <div class="diagram-wrap">
+    <div class="mermaid">
+flowchart LR
+    INPUT["Patient Profile<br/>+ Trial Profile"]
+
+    subgraph COND_GRP["Condition Overlap (4)"]
+        direction TB
+        CF1["condition_overlap<br/><small>raw count: |patient_conds ∩ trial_conds|</small>"]
+        CF2["jaccard_similarity<br/><small>overlap / union</small>"]
+        CF3["overlap_ratio_trial<br/><small>overlap / |trial_conds|</small>"]
+        CF4["overlap_ratio_patient<br/><small>overlap / |patient_conds|</small>"]
+    end
+
+    subgraph PROF_GRP["Condition Profile (5)"]
+        direction TB
+        PF1["condition_rarity_score<br/><small>mean(1/log2(n_trials_per_cond + 2)), norm 0-1</small>"]
+        PF2["trial_specificity<br/><small>1 / |trial_conds|</small>"]
+        PF3["condition_burden<br/><small>total_patient_conds / 10</small>"]
+        PF4["active_ratio<br/><small>active_conds / total_conds</small>"]
+        PF5["resolved_ratio<br/><small>resolved_conds / total_conds</small>"]
+    end
+
+    subgraph ELIG_GRP["Eligibility (4)"]
+        direction TB
+        EF1["age_distance<br/><small>norm distance outside [min_age, max_age]</small>"]
+        EF2["age_centered<br/><small>position within range, -1 to +1</small>"]
+        EF3["age_compatibility<br/><small>1.0 in range; decays over 30yr gap</small>"]
+        EF4["gender_compatibility<br/><small>1.0 match/ALL; 0.1 mismatch</small>"]
+    end
+
+    subgraph EXT_GRP["External Signals (3)"]
+        direction TB
+        XF1["geo_feasibility<br/><small>1.0 same state · 0.75 other US state · 0.5 no data</small>"]
+        XF2["med_compatibility<br/><small>patient med keywords ∩ trial drug intervention keywords</small>"]
+        XF3["lab_availability<br/><small>patient observation types covered by trial / 20</small>"]
+    end
+
+    subgraph META_GRP["Metadata (1)"]
+        MF1["data_completeness<br/><small>fraction of key fields non-empty<br/>importance: ~0.0 in trained RF</small>"]
+    end
+
+    RF["RandomForest<br/>predict_proba()<br/>→ eligibility_prob"]
+
+    INPUT --> COND_GRP
+    INPUT --> PROF_GRP
+    INPUT --> ELIG_GRP
+    INPUT --> EXT_GRP
+    INPUT --> META_GRP
+
+    COND_GRP --> RF
+    PROF_GRP --> RF
+    ELIG_GRP --> RF
+    EXT_GRP --> RF
+    META_GRP --> RF
+    </div>
+
+    <div class="note" style="margin-top:1rem">
+      <strong>Top features by importance (Random Forest):</strong>
+      age_distance (20.4%) · age_compatibility (16.9%) · gender_compatibility (12.9%) · age_centered (7.9%) ·
+      jaccard_similarity (7.5%) · condition_rarity_score (6.2%) · overlap_ratio_trial (5.2%) · overlap_ratio_patient (5.1%) ·
+      condition_overlap (4.0%) · remaining 8 features (&lt;3% each).
+      <br/><br/>
+      <strong>Note on AUC (0.60):</strong> The 82.1% positive pseudo-label rate makes the classification trivially high-accuracy
+      but limits discriminative power. Raising the label threshold from 0.5 → 0.6 or enforcing equal pos/neg sampling would improve AUC.
+    </div>
+  </div>
+</section>
+
+<footer style="border-top:1px solid #30363d;margin-top:3rem;padding-top:1.5rem;color:#8b949e;font-size:0.8rem;text-align:center">
+  Second Life · DSCI 5260 · Group 7 · Generated 2026-04-25
+</footer>
+
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