Add Tutorials page and fix stale Colab links

data/tasks.json

@@ -83,7 +83,7 @@
       "MIMIC-IV"
     ],
     "description": "Predict onset of diabetic ketoacidosis (DKA) from clinical event sequences in ICU patients.",
-    "colab_url": "https://colab.research.google.com/drive/1kKkkBVS_GclHoYTbnOtjyYnSee79hsyT?usp=sharing",
+    "colab_url": "https://colab.research.google.com/drive/1QB0acnGb-wOuK53UNSgHxjCW74QeYjUl?usp=sharing",
     "docs_url": "https://pyhealth.readthedocs.io/en/latest/api/tasks/pyhealth.tasks.dka.html",
     "source_file": "pyhealth/tasks/dka.py",
     "modality": [
@@ -108,7 +108,7 @@
       "TUAB"
     ],
     "description": "Classify clinical EEG recordings as normal or abnormal using deep signal models.",
-    "colab_url": "https://colab.research.google.com/drive/1kKkkBVS_GclHoYTbnOtjyYnSee79hsyT?usp=sharing",
+    "colab_url": "https://colab.research.google.com/drive/1QB0acnGb-wOuK53UNSgHxjCW74QeYjUl?usp=sharing",
     "docs_url": "https://pyhealth.readthedocs.io/en/latest/api/tasks/pyhealth.tasks.temple_university_EEG_tasks.html",
     "source_file": "pyhealth/tasks/temple_university_EEG_tasks.py",
     "modality": [
@@ -157,7 +157,7 @@
       "COSMIC"
     ],
     "description": "Classify genomic variants as pathogenic or benign using sequence-based features.",
-    "colab_url": "https://colab.research.google.com/drive/1kKkkBVS_GclHoYTbnOtjyYnSee79hsyT?usp=sharing",
+    "colab_url": "https://colab.research.google.com/drive/1QB0acnGb-wOuK53UNSgHxjCW74QeYjUl?usp=sharing",
     "docs_url": "https://pyhealth.readthedocs.io/en/latest/api/tasks/pyhealth.tasks.MutationPathogenicityPrediction.html",
     "source_file": "pyhealth/tasks/variant_classification.py",
     "modality": [
@@ -182,7 +182,7 @@
       "TCGA-PRAD"
     ],
     "description": "Predict 5-year survival of prostate cancer patients from multi-omics profiles.",
-    "colab_url": "https://colab.research.google.com/drive/1kKkkBVS_GclHoYTbnOtjyYnSee79hsyT?usp=sharing",
+    "colab_url": "https://colab.research.google.com/drive/1QB0acnGb-wOuK53UNSgHxjCW74QeYjUl?usp=sharing",
     "docs_url": "https://pyhealth.readthedocs.io/en/latest/api/tasks/pyhealth.tasks.CancerSurvivalPrediction.html",
     "source_file": "pyhealth/tasks/cancer_survival.py",
     "modality": [
@@ -207,7 +207,7 @@
       "MIMIC-III"
     ],
     "description": "Determine whether two clinical records belong to the same patient across fragmented health systems.",
-    "colab_url": "https://colab.research.google.com/drive/1kKkkBVS_GclHoYTbnOtjyYnSee79hsyT?usp=sharing",
+    "colab_url": "https://colab.research.google.com/drive/1QB0acnGb-wOuK53UNSgHxjCW74QeYjUl?usp=sharing",
     "docs_url": "https://pyhealth.readthedocs.io/en/latest/api/tasks.html",
     "source_file": "pyhealth/tasks/patient_linkage_mimic3.py",
     "modality": [
@@ -241,7 +241,7 @@
       "MIMIC-III"
     ],
     "description": "Predict ICU length of stay as one of multiple duration buckets from admission data.",
-    "colab_url": "https://colab.research.google.com/drive/1kKkkBVS_GclHoYTbnOtjyYnSee79hsyT?usp=sharing",
+    "colab_url": "https://colab.research.google.com/drive/1QB0acnGb-wOuK53UNSgHxjCW74QeYjUl?usp=sharing",
     "docs_url": "https://pyhealth.readthedocs.io/en/latest/api/tasks/pyhealth.tasks.length_of_stay_prediction.html",
     "source_file": "pyhealth/tasks/length_of_stay_prediction.py",
     "modality": [
@@ -267,7 +267,7 @@
       "SleepEDF"
     ],
     "description": "Classify 30-second EEG epochs into Wake, N1, N2, N3, or REM sleep stages.",
-    "colab_url": "https://colab.research.google.com/drive/1kKkkBVS_GclHoYTbnOtjyYnSee79hsyT?usp=sharing",
+    "colab_url": "https://colab.research.google.com/drive/1QB0acnGb-wOuK53UNSgHxjCW74QeYjUl?usp=sharing",
     "docs_url": "https://pyhealth.readthedocs.io/en/latest/api/tasks/pyhealth.tasks.SleepStagingSleepEDF.html",
     "input_schema": {
       "signal": "tensor"
@@ -291,7 +291,7 @@
       "TUEV"
     ],
     "description": "Classify EEG events into six types including spike-wave complexes, periodic discharges, and artifacts.",
-    "colab_url": "https://colab.research.google.com/drive/1kKkkBVS_GclHoYTbnOtjyYnSee79hsyT?usp=sharing",
+    "colab_url": "https://colab.research.google.com/drive/1QB0acnGb-wOuK53UNSgHxjCW74QeYjUl?usp=sharing",
     "docs_url": "https://pyhealth.readthedocs.io/en/latest/api/tasks/pyhealth.tasks.temple_university_EEG_tasks.html",
     "source_file": "pyhealth/tasks/temple_university_EEG_tasks.py",
     "modality": [
@@ -363,7 +363,7 @@
       "EHRShot"
     ],
     "description": "15-task few-shot benchmark suite for evaluating clinical foundation models on real-world EHR prediction challenges.",
-    "colab_url": "https://colab.research.google.com/drive/1kKkkBVS_GclHoYTbnOtjyYnSee79hsyT?usp=sharing",
+    "colab_url": "https://colab.research.google.com/drive/1QB0acnGb-wOuK53UNSgHxjCW74QeYjUl?usp=sharing",
     "docs_url": "https://pyhealth.readthedocs.io/en/latest/api/tasks/pyhealth.tasks.benchmark_ehrshot.html",
     "input_schema": {
       "feature": "sequence"
@@ -387,7 +387,7 @@
       "MIMIC-III"
     ],
     "description": "Recommend a safe set of medications for a patient visit given their diagnosis and procedure history.",
-    "colab_url": "https://colab.research.google.com/drive/1kKkkBVS_GclHoYTbnOtjyYnSee79hsyT?usp=sharing",
+    "colab_url": "https://colab.research.google.com/drive/1QB0acnGb-wOuK53UNSgHxjCW74QeYjUl?usp=sharing",
     "docs_url": "https://pyhealth.readthedocs.io/en/latest/api/tasks/pyhealth.tasks.drug_recommendation.html",
     "source_file": "pyhealth/tasks/drug_recommendation.py",
     "modality": [
@@ -437,7 +437,7 @@
       "BMDHS"
     ],
     "description": "Classify cardiac valve disease conditions from phonocardiogram (PCG) heart sound recordings.",
-    "colab_url": "https://colab.research.google.com/drive/1kKkkBVS_GclHoYTbnOtjyYnSee79hsyT?usp=sharing",
+    "colab_url": "https://colab.research.google.com/drive/1QB0acnGb-wOuK53UNSgHxjCW74QeYjUl?usp=sharing",
     "docs_url": "https://pyhealth.readthedocs.io/en/latest/api/tasks/pyhealth.tasks.bmd_hs_disease_classification.html",
     "source_file": "pyhealth/tasks/bmd_hs_disease_classification.py",
     "modality": [
@@ -495,7 +495,7 @@
       "COSMIC"
     ],
     "description": "Predict functional impact and cancer driver gene status of somatic mutations.",
-    "colab_url": "https://colab.research.google.com/drive/1kKkkBVS_GclHoYTbnOtjyYnSee79hsyT?usp=sharing",
+    "colab_url": "https://colab.research.google.com/drive/1QB0acnGb-wOuK53UNSgHxjCW74QeYjUl?usp=sharing",
     "docs_url": "https://pyhealth.readthedocs.io/en/latest/api/tasks/pyhealth.tasks.VariantClassificationClinVar.html",
     "source_file": "pyhealth/tasks/variant_classification.py",
     "modality": [
@@ -520,7 +520,7 @@
       "eICU"
     ],
     "description": "Medication recommendation from multi-center ICU data with cross-site generalization evaluation.",
-    "colab_url": "https://colab.research.google.com/drive/1kKkkBVS_GclHoYTbnOtjyYnSee79hsyT?usp=sharing",
+    "colab_url": "https://colab.research.google.com/drive/1QB0acnGb-wOuK53UNSgHxjCW74QeYjUl?usp=sharing",
     "docs_url": "https://pyhealth.readthedocs.io/en/latest/api/tasks/pyhealth.tasks.drug_recommendation.html",
     "source_file": "pyhealth/tasks/drug_recommendation.py",
     "modality": [
@@ -546,7 +546,7 @@
       "TCGA-PRAD"
     ],
     "description": "Predict tumor mutation burden (TMB) as a continuous value from multi-omics cancer profiles.",
-    "colab_url": "https://colab.research.google.com/drive/1kKkkBVS_GclHoYTbnOtjyYnSee79hsyT?usp=sharing",
+    "colab_url": "https://colab.research.google.com/drive/1QB0acnGb-wOuK53UNSgHxjCW74QeYjUl?usp=sharing",
     "docs_url": "https://pyhealth.readthedocs.io/en/latest/api/tasks/pyhealth.tasks.CancerMutationBurden.html",
     "source_file": "pyhealth/tasks/cancer_survival.py",
     "modality": [

datasets.html

@@ -294,7 +294,7 @@
       <h1><span id="heroCount">29</span> Clinical Datasets</h1>
       <p class="ph-tagline">A unified API for loading healthcare data across electronic health records, physiological signals, medical imaging, genomics, and clinical text — all ready for machine learning.</p>
       <div class="ph-cta-row">
-        <a href="https://colab.research.google.com/drive/1voSx7wEfzXfEf2sIfW6b-8p1KqMyuWxK?usp=sharing" class="ph-btn primary" target="_blank" rel="noopener">
+        <a href="https://colab.research.google.com/drive/1vI_oljc7rU5ocsC26ITM7HUgD5SGZkFE?usp=sharing" class="ph-btn primary" target="_blank" rel="noopener">
           <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><polygon points="5 3 19 12 5 21 5 3"/></svg>
           Open Tutorial Notebook
         </a>

index.html

@@ -649,6 +649,7 @@
         <a href="datasets.html">Datasets</a>
         <a href="tasks.html">Tasks</a>
         <a href="models.html">Models</a>
+        <a href="tutorials.html">Tutorials</a>
         <a href="roadmap.html">Roadmap</a>
         <a href="blog_list.html">Blog</a>
         <a href="research.html" class="nav-cta">Join Us</a>

models.html

@@ -316,7 +316,7 @@
       <h1><span id="heroCount">50</span> Clinical ML Models</h1>
       <p class="ph-tagline">Production-ready models spanning EHR sequence learning, drug recommendation, biosignal analysis, graph neural networks, medical imaging, and clinical NLP — all with a unified training API.</p>
       <div class="ph-cta-row">
-        <a href="https://colab.research.google.com/drive/1LcXZlu7ZUuqepf269X3FhXuhHeRvaJX5?usp=sharing" class="ph-btn primary" target="_blank" rel="noopener">
+        <a href="https://colab.research.google.com/drive/1cUTSfFL1wLUXDBtJGTAWntolvcmxrDGo?usp=sharing" class="ph-btn primary" target="_blank" rel="noopener">
           <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><polygon points="5 3 19 12 5 21 5 3"/></svg>
           Open Tutorial Notebook
         </a>

tasks.html

@@ -342,7 +342,7 @@
       <h1><span id="heroTaskCount">…</span> Clinical Tasks</h1>
       <p class="ph-tagline">Standardized clinical prediction task definitions — from mortality and readmission to drug recommendation, sleep staging, and genomic analysis. Call <code style="font-family:monospace; background:rgba(255,255,255,0.1); padding:0.1rem 0.4rem; border-radius:4px;">dataset.set_task()</code> and you're ready to train.</p>
       <div class="ph-cta-row">
-        <a href="https://colab.research.google.com/drive/1kKkkBVS_GclHoYTbnOtjyYnSee79hsyT?usp=sharing" class="ph-btn primary" target="_blank" rel="noopener">
+        <a href="https://colab.research.google.com/drive/1QB0acnGb-wOuK53UNSgHxjCW74QeYjUl?usp=sharing" class="ph-btn primary" target="_blank" rel="noopener">
           <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><polygon points="5 3 19 12 5 21 5 3"/></svg>
           Open Tutorial Notebook
         </a>