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<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>PyHealth Models — 33+ Clinical ML Models</title>
<meta name="description" content="Browse all clinical machine learning models supported by PyHealth across EHR, drug recommendation, biosignals, graph, imaging, and text modalities.">
<link rel="preconnect" href="https://fonts.googleapis.com">
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<style>
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.model-card {
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@media (max-width: 500px) {
.model-grid { grid-template-columns: 1fr; }
}
</style>
</head>
<body>
<header class="site-header">
<div class="site-wrap header-inner">
<nav class="nav" aria-label="Main navigation">
<a href="index.html" class="back-link">
<svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2.2" stroke-linecap="round" stroke-linejoin="round"><polyline points="15 18 9 12 15 6"/></svg>
PyHealth
</a>
</nav>
<span style="color: var(--muted); font-size: 0.88rem; font-weight: 500;">Models</span>
</div>
</header>
<main class="site-wrap" style="padding: 2.2rem 0 3.5rem;">
<!-- ── Hero ── -->
<section class="ph-hero">
<p class="ph-eyebrow">PyHealth Models</p>
<h1>33+ 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">
<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>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="ph-btn outline" 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"><path d="M4 19.5A2.5 2.5 0 0 1 6.5 17H20"/><path d="M6.5 2H20v20H6.5A2.5 2.5 0 0 1 4 19.5v-15A2.5 2.5 0 0 1 6.5 2z"/></svg>
API Reference
</a>
</div>
<div class="growing-notice">
<span class="dot"></span>
Actively growing — new models added regularly. <a href="https://docs.google.com/spreadsheets/d/1PNMgDe-llOm1SM5ZyGLkmPysjC4wwaVblPLAHLxejTw/edit#gid=159213380" target="_blank" rel="noopener" style="color:rgba(255,255,255,0.85); text-decoration:underline; text-underline-offset:2px;">View planned additions →</a>
</div>
</section>
<!-- ── Filter + Grid ── -->
<section class="page-section fade-up">
<div style="display:flex; align-items:baseline; justify-content:space-between; flex-wrap:wrap; gap:0.5rem; margin-bottom:1rem;">
<h2 style="margin:0; font-size:clamp(1.1rem,2vw,1.4rem);">
All Models <span class="count-badge" id="visibleCount">34</span>
</h2>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" target="_blank" rel="noopener" style="font-size:0.85rem; color:var(--accent); text-decoration:none; font-weight:500;">Full API reference →</a>
</div>
<div class="filter-bar" role="group" aria-label="Filter by category">
<button class="filter-btn active" onclick="filterModels('all', this)">All <span style="opacity:0.65;">(34)</span></button>
<button class="filter-btn" onclick="filterModels('ehr', this)">EHR <span style="opacity:0.65;">(14)</span></button>
<button class="filter-btn" onclick="filterModels('drug', this)">Drug Rec <span style="opacity:0.65;">(4)</span></button>
<button class="filter-btn" onclick="filterModels('signal', this)">Signal <span style="opacity:0.65;">(4)</span></button>
<button class="filter-btn" onclick="filterModels('graph', this)">Graph <span style="opacity:0.65;">(2)</span></button>
<button class="filter-btn" onclick="filterModels('image', this)">Image <span style="opacity:0.65;">(4)</span></button>
<button class="filter-btn" onclick="filterModels('text', this)">Text <span style="opacity:0.65;">(3)</span></button>
<button class="filter-btn" onclick="filterModels('multimodal', this)">Multimodal <span style="opacity:0.65;">(3)</span></button>
</div>
<div class="model-grid" id="modelGrid">
<!-- ───────────── EHR Sequence (14) ───────────── -->
<div class="model-card" data-category="ehr">
<div class="model-card-header">
<span class="model-classname">RNN</span>
<span class="category-tag cat-ehr">EHR</span>
</div>
<p class="model-desc">Gated recurrent network (GRU/LSTM) for sequential EHR data — a strong, interpretable baseline for longitudinal clinical tasks.</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">RNN</span>
model <span class="tok-op">=</span> <span class="tok-cl">RNN</span>(dataset<span class="tok-op">=</span>samples, rnn_type<span class="tok-op">=</span><span class="tok-str">"GRU"</span>)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/1LcXZlu7ZUuqepf269X3FhXuhHeRvaJX5?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<div class="model-card" data-category="ehr">
<div class="model-card-header">
<span class="model-classname">Transformer</span>
<span class="category-tag cat-ehr">EHR</span>
</div>
<p class="model-desc">Self-attention transformer encoder for EHR sequences — captures long-range dependencies across clinical visits.</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">Transformer</span>
model <span class="tok-op">=</span> <span class="tok-cl">Transformer</span>(dataset<span class="tok-op">=</span>samples)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/1LcXZlu7ZUuqepf269X3FhXuhHeRvaJX5?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<div class="model-card" data-category="ehr">
<div class="model-card-header">
<span class="model-classname">MLP</span>
<span class="category-tag cat-ehr">EHR</span>
</div>
<p class="model-desc">Multi-layer perceptron over aggregated patient features — a fast, simple non-sequential baseline.</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">MLP</span>
model <span class="tok-op">=</span> <span class="tok-cl">MLP</span>(dataset<span class="tok-op">=</span>samples, hidden_dim<span class="tok-op">=</span><span class="tok-num">256</span>)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/1LcXZlu7ZUuqepf269X3FhXuhHeRvaJX5?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<div class="model-card" data-category="ehr">
<div class="model-card-header">
<span class="model-classname">LogisticRegression</span>
<span class="category-tag cat-ehr">EHR</span>
</div>
<p class="model-desc">Logistic regression over bag-of-codes features — the standard interpretable clinical baseline.</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">LogisticRegression</span>
model <span class="tok-op">=</span> <span class="tok-cl">LogisticRegression</span>(dataset<span class="tok-op">=</span>samples)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/1LcXZlu7ZUuqepf269X3FhXuhHeRvaJX5?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<div class="model-card" data-category="ehr">
<div class="model-card-header">
<span class="model-classname">RETAIN</span>
<span class="category-tag cat-ehr">EHR</span>
</div>
<p class="model-desc">Reverse-time attention network providing visit-level and code-level interpretability for clinical risk prediction (Choi et al., 2016).</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">RETAIN</span>
model <span class="tok-op">=</span> <span class="tok-cl">RETAIN</span>(dataset<span class="tok-op">=</span>samples)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/1LcXZlu7ZUuqepf269X3FhXuhHeRvaJX5?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<div class="model-card" data-category="ehr">
<div class="model-card-header">
<span class="model-classname">StageNet</span>
<span class="category-tag cat-ehr">EHR</span>
</div>
<p class="model-desc">Stage-aware LSTM that models disease progression stages from irregular time-series EHR data (Gao et al., 2020).</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">StageNet</span>
model <span class="tok-op">=</span> <span class="tok-cl">StageNet</span>(dataset<span class="tok-op">=</span>samples)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/1LcXZlu7ZUuqepf269X3FhXuhHeRvaJX5?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<div class="model-card" data-category="ehr">
<div class="model-card-header">
<span class="model-classname">StageNetMHA</span>
<span class="category-tag cat-ehr">EHR</span>
</div>
<p class="model-desc">StageNet variant with multi-head attention over clinical stages for richer temporal modeling of disease trajectories.</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">StageNetMHA</span>
model <span class="tok-op">=</span> <span class="tok-cl">StageNetMHA</span>(dataset<span class="tok-op">=</span>samples)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/1LcXZlu7ZUuqepf269X3FhXuhHeRvaJX5?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<div class="model-card" data-category="ehr">
<div class="model-card-header">
<span class="model-classname">AdaCare</span>
<span class="category-tag cat-ehr">EHR</span>
</div>
<p class="model-desc">Adaptive recurrent model with feature recalibration and multi-scale temporal convolution for clinical time-series (Ma et al., 2020).</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">AdaCare</span>
model <span class="tok-op">=</span> <span class="tok-cl">AdaCare</span>(dataset<span class="tok-op">=</span>samples)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/1LcXZlu7ZUuqepf269X3FhXuhHeRvaJX5?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<div class="model-card" data-category="ehr">
<div class="model-card-header">
<span class="model-classname">ConCare</span>
<span class="category-tag cat-ehr">EHR</span>
</div>
<p class="model-desc">Context-aware attention model that uses demographic features to guide multi-head attention over clinical features (Ma et al., 2020).</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">ConCare</span>
model <span class="tok-op">=</span> <span class="tok-cl">ConCare</span>(dataset<span class="tok-op">=</span>samples)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/1LcXZlu7ZUuqepf269X3FhXuhHeRvaJX5?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<div class="model-card" data-category="ehr">
<div class="model-card-header">
<span class="model-classname">Agent</span>
<span class="category-tag cat-ehr">EHR</span>
</div>
<p class="model-desc">Attention-based model with global-to-local information gathering for learning patient representations from irregular clinical visits.</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">Agent</span>
model <span class="tok-op">=</span> <span class="tok-cl">Agent</span>(dataset<span class="tok-op">=</span>samples)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/1LcXZlu7ZUuqepf269X3FhXuhHeRvaJX5?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<div class="model-card" data-category="ehr">
<div class="model-card-header">
<span class="model-classname">GRASP</span>
<span class="category-tag cat-ehr">EHR</span>
</div>
<p class="model-desc">Graph-based similarity retrieval model that learns from both local patient features and similar patients in the dataset (Zhang et al., 2021).</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">GRASP</span>
model <span class="tok-op">=</span> <span class="tok-cl">GRASP</span>(dataset<span class="tok-op">=</span>samples)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/1LcXZlu7ZUuqepf269X3FhXuhHeRvaJX5?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<div class="model-card" data-category="ehr">
<div class="model-card-header">
<span class="model-classname">DeepR</span>
<span class="category-tag cat-ehr">EHR</span>
</div>
<p class="model-desc">Deep representation learning from clinical records using sparse ICD code embeddings with temporal decay (Nguyen et al., 2016).</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">DeepR</span>
model <span class="tok-op">=</span> <span class="tok-cl">DeepR</span>(dataset<span class="tok-op">=</span>samples)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/1LcXZlu7ZUuqepf269X3FhXuhHeRvaJX5?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<div class="model-card" data-category="ehr">
<div class="model-card-header">
<span class="model-classname">EHRMamba</span>
<span class="category-tag cat-ehr">EHR</span>
</div>
<p class="model-desc">State-space model (Mamba/SSM) adapted for EHR sequences — efficient linear-time alternative to transformers for long patient histories.</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">EHRMamba</span>
model <span class="tok-op">=</span> <span class="tok-cl">EHRMamba</span>(dataset<span class="tok-op">=</span>samples)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/1LcXZlu7ZUuqepf269X3FhXuhHeRvaJX5?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<div class="model-card" data-category="ehr">
<div class="model-card-header">
<span class="model-classname">JambaEHR</span>
<span class="category-tag cat-ehr">EHR</span>
</div>
<p class="model-desc">Hybrid Jamba architecture (SSM + attention layers) for EHR, balancing the efficiency of Mamba with the expressiveness of transformers.</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">JambaEHR</span>
model <span class="tok-op">=</span> <span class="tok-cl">JambaEHR</span>(dataset<span class="tok-op">=</span>samples)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/1LcXZlu7ZUuqepf269X3FhXuhHeRvaJX5?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<!-- ───────────── Drug Recommendation (4) ───────────── -->
<div class="model-card" data-category="drug">
<div class="model-card-header">
<span class="model-classname">SafeDrug</span>
<span class="category-tag cat-drug">Drug Rec</span>
</div>
<p class="model-desc">Drug recommendation with molecular structure and drug-drug interaction constraints for safe prescription (Yang et al., 2021).</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">SafeDrug</span>
model <span class="tok-op">=</span> <span class="tok-cl">SafeDrug</span>(dataset<span class="tok-op">=</span>samples)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/1b9xRbxUz-HLzxsrvxdsdJ868ajGQCY6U?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<div class="model-card" data-category="drug">
<div class="model-card-header">
<span class="model-classname">GAMENet</span>
<span class="category-tag cat-drug">Drug Rec</span>
</div>
<p class="model-desc">Graph augmented memory network leveraging drug knowledge graphs and patient history for medication recommendation (Shang et al., 2019).</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">GAMENet</span>
model <span class="tok-op">=</span> <span class="tok-cl">GAMENet</span>(dataset<span class="tok-op">=</span>samples)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/1b9xRbxUz-HLzxsrvxdsdJ868ajGQCY6U?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<div class="model-card" data-category="drug">
<div class="model-card-header">
<span class="model-classname">MICRON</span>
<span class="category-tag cat-drug">Drug Rec</span>
</div>
<p class="model-desc">Medication change prediction network that models prescription changes between visits via residual learning (Yang et al., 2021).</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">MICRON</span>
model <span class="tok-op">=</span> <span class="tok-cl">MICRON</span>(dataset<span class="tok-op">=</span>samples)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/1b9xRbxUz-HLzxsrvxdsdJ868ajGQCY6U?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<div class="model-card" data-category="drug">
<div class="model-card-header">
<span class="model-classname">MoleRec</span>
<span class="category-tag cat-drug">Drug Rec</span>
</div>
<p class="model-desc">Molecule-level drug recommendation with substructure-aware representation learning from drug molecular graphs (Yang et al., 2023).</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">MoleRec</span>
model <span class="tok-op">=</span> <span class="tok-cl">MoleRec</span>(dataset<span class="tok-op">=</span>samples)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/1b9xRbxUz-HLzxsrvxdsdJ868ajGQCY6U?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<!-- ───────────── Biosignal / EEG (4) ───────────── -->
<div class="model-card" data-category="signal">
<div class="model-card-header">
<span class="model-classname">SparcNet</span>
<span class="category-tag cat-signal">Signal</span>
</div>
<p class="model-desc">Sparse neural network for EEG and biosignal classification — strong performance on sleep staging, abnormality detection, and EEG events.</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">SparcNet</span>
model <span class="tok-op">=</span> <span class="tok-cl">SparcNet</span>(dataset<span class="tok-op">=</span>samples)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/1LcXZlu7ZUuqepf269X3FhXuhHeRvaJX5?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<div class="model-card" data-category="signal">
<div class="model-card-header">
<span class="model-classname">BIOT</span>
<span class="category-tag cat-signal">Signal</span>
</div>
<p class="model-desc">Biosignal foundation model with tokenized EEG patch representations and cross-dataset transfer learning capabilities (Yang et al., 2023).</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">BIOT</span>
model <span class="tok-op">=</span> <span class="tok-cl">BIOT</span>(dataset<span class="tok-op">=</span>samples)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/1LcXZlu7ZUuqepf269X3FhXuhHeRvaJX5?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<div class="model-card" data-category="signal">
<div class="model-card-header">
<span class="model-classname">ContraWR</span>
<span class="category-tag cat-signal">Signal</span>
</div>
<p class="model-desc">Contrastive learning for wearable and EEG signals — self-supervised pretraining for sleep staging and physiological classification (Yang et al., 2023).</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">ContraWR</span>
model <span class="tok-op">=</span> <span class="tok-cl">ContraWR</span>(dataset<span class="tok-op">=</span>samples)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/1LcXZlu7ZUuqepf269X3FhXuhHeRvaJX5?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<div class="model-card" data-category="signal">
<div class="model-card-header">
<span class="model-classname">TCN</span>
<span class="category-tag cat-signal">Signal</span>
</div>
<p class="model-desc">Temporal Convolutional Network with dilated causal convolutions for time-series classification — efficient and parallelizable.</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">TCN</span>
model <span class="tok-op">=</span> <span class="tok-cl">TCN</span>(dataset<span class="tok-op">=</span>samples)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/1LcXZlu7ZUuqepf269X3FhXuhHeRvaJX5?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<!-- ───────────── Graph (2) ───────────── -->
<div class="model-card" data-category="graph">
<div class="model-card-header">
<span class="model-classname">GNN</span>
<span class="category-tag cat-graph">Graph</span>
</div>
<p class="model-desc">General-purpose graph neural network for clinical knowledge graphs, patient similarity networks, and drug interaction graphs.</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">GNN</span>
model <span class="tok-op">=</span> <span class="tok-cl">GNN</span>(dataset<span class="tok-op">=</span>samples, conv_type<span class="tok-op">=</span><span class="tok-str">"GAT"</span>)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/1LcXZlu7ZUuqepf269X3FhXuhHeRvaJX5?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<div class="model-card" data-category="graph">
<div class="model-card-header">
<span class="model-classname">GraphCare</span>
<span class="category-tag cat-graph">Graph</span>
</div>
<p class="model-desc">Knowledge graph-enhanced patient representation that integrates clinical ontologies (ICD, ATC) for personalized healthcare prediction (Jiang et al., 2023).</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">GraphCare</span>
model <span class="tok-op">=</span> <span class="tok-cl">GraphCare</span>(dataset<span class="tok-op">=</span>samples)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/1LcXZlu7ZUuqepf269X3FhXuhHeRvaJX5?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<!-- ───────────── Image (4) ───────────── -->
<div class="model-card" data-category="image">
<div class="model-card-header">
<span class="model-classname">CNN</span>
<span class="category-tag cat-image">Image</span>
</div>
<p class="model-desc">Convolutional neural network for medical image classification — configurable depth and normalization for chest X-ray and other imaging tasks.</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">CNN</span>
model <span class="tok-op">=</span> <span class="tok-cl">CNN</span>(dataset<span class="tok-op">=</span>samples)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/18vK23gyI1LjWbTgkq4f99yDZA3A7Pxp9?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<div class="model-card" data-category="image">
<div class="model-card-header">
<span class="model-classname">TorchvisionModel</span>
<span class="category-tag cat-image">Image</span>
</div>
<p class="model-desc">Wrapper for any torchvision architecture (ResNet, ViT, EfficientNet, DenseNet) as a drop-in PyHealth model with pre-trained weights support.</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">TorchvisionModel</span>
model <span class="tok-op">=</span> <span class="tok-cl">TorchvisionModel</span>(dataset<span class="tok-op">=</span>samples,
backbone<span class="tok-op">=</span><span class="tok-str">"resnet50"</span>)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/18vK23gyI1LjWbTgkq4f99yDZA3A7Pxp9?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<div class="model-card" data-category="image">
<div class="model-card-header">
<span class="model-classname">VAE</span>
<span class="category-tag cat-image">Image</span>
</div>
<p class="model-desc">Variational autoencoder for medical image generation and latent-space representation learning — supports chest X-ray synthesis.</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">VAE</span>
model <span class="tok-op">=</span> <span class="tok-cl">VAE</span>(dataset<span class="tok-op">=</span>samples, latent_dim<span class="tok-op">=</span><span class="tok-num">128</span>)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/18vK23gyI1LjWbTgkq4f99yDZA3A7Pxp9?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<div class="model-card" data-category="image">
<div class="model-card-header">
<span class="model-classname">GAN</span>
<span class="category-tag cat-image">Image</span>
</div>
<p class="model-desc">Generative adversarial network for medical image synthesis and data augmentation — includes generator and discriminator for chest X-ray generation.</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">GAN</span>
model <span class="tok-op">=</span> <span class="tok-cl">GAN</span>(dataset<span class="tok-op">=</span>samples)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/18vK23gyI1LjWbTgkq4f99yDZA3A7Pxp9?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<!-- ───────────── Text / NLP (3) ───────────── -->
<div class="model-card" data-category="text">
<div class="model-card-header">
<span class="model-classname">TransformersModel</span>
<span class="category-tag cat-text">Text</span>
</div>
<p class="model-desc">HuggingFace model wrapper — plug in any BERT, ClinicalBERT, BioBERT, or LLM checkpoint as a PyHealth model for clinical NLP tasks.</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">TransformersModel</span>
model <span class="tok-op">=</span> <span class="tok-cl">TransformersModel</span>(dataset<span class="tok-op">=</span>samples,
model_name<span class="tok-op">=</span><span class="tok-str">"emilyalsentzer/Bio_ClinicalBERT"</span>)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/1ThYP_5ng5xPQwscv5XztefkkoTruhjeK?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<div class="model-card" data-category="text">
<div class="model-card-header">
<span class="model-classname">TextEmbeddingModel</span>
<span class="category-tag cat-text">Text</span>
</div>
<p class="model-desc">Extracts fixed-size text embeddings from clinical notes using a HuggingFace encoder, feeding downstream PyHealth models.</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">TextEmbeddingModel</span>
model <span class="tok-op">=</span> <span class="tok-cl">TextEmbeddingModel</span>(dataset<span class="tok-op">=</span>samples,
model_name<span class="tok-op">=</span><span class="tok-str">"bert-base-uncased"</span>)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/1ThYP_5ng5xPQwscv5XztefkkoTruhjeK?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<div class="model-card" data-category="text">
<div class="model-card-header">
<span class="model-classname">SDOH</span>
<span class="category-tag cat-text">Text</span>
</div>
<p class="model-desc">Social determinants of health model that extracts SDOH factors from unstructured clinical notes and integrates them into risk prediction.</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">SDOH</span>
model <span class="tok-op">=</span> <span class="tok-cl">SDOH</span>(dataset<span class="tok-op">=</span>samples)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/1LcXZlu7ZUuqepf269X3FhXuhHeRvaJX5?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<!-- ───────────── Multimodal / Utility (3) ───────────── -->
<div class="model-card" data-category="multimodal">
<div class="model-card-header">
<span class="model-classname">UnifiedMultimodalEmbeddingModel</span>
<span class="category-tag cat-multimodal">Multimodal</span>
</div>
<p class="model-desc">Unified temporal embedding model for simultaneous EHR codes, clinical text, medical images, and biosignals — the backbone of PyHealth 2.0's multimodal API.</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">UnifiedMultimodalEmbeddingModel</span>
model <span class="tok-op">=</span> <span class="tok-cl">UnifiedMultimodalEmbeddingModel</span>(
dataset<span class="tok-op">=</span>samples)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/1LcXZlu7ZUuqepf269X3FhXuhHeRvaJX5?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<div class="model-card" data-category="multimodal">
<div class="model-card-header">
<span class="model-classname">VisionEmbeddingModel</span>
<span class="category-tag cat-multimodal">Multimodal</span>
</div>
<p class="model-desc">Vision encoder bridge that wraps torchvision backbones as temporal feature processors compatible with the unified multimodal pipeline.</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">VisionEmbeddingModel</span>
model <span class="tok-op">=</span> <span class="tok-cl">VisionEmbeddingModel</span>(dataset<span class="tok-op">=</span>samples,
backbone<span class="tok-op">=</span><span class="tok-str">"vit_b_16"</span>)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/18vK23gyI1LjWbTgkq4f99yDZA3A7Pxp9?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
<div class="model-card" data-category="multimodal">
<div class="model-card-header">
<span class="model-classname">MedLink</span>
<span class="category-tag cat-multimodal">Multimodal</span>
</div>
<p class="model-desc">Patient record linkage model combining structured EHR features and free-text notes to identify duplicate patient records across data sources.</p>
<pre class="model-code"><span class="tok-kw">from</span> pyhealth.models <span class="tok-kw">import</span> <span class="tok-cl">MedLink</span>
model <span class="tok-op">=</span> <span class="tok-cl">MedLink</span>(dataset<span class="tok-op">=</span>samples)</pre>
<div class="model-card-footer">
<a href="https://colab.research.google.com/drive/1LcXZlu7ZUuqepf269X3FhXuhHeRvaJX5?usp=sharing" class="colab-link" target="_blank" rel="noopener">Open in Colab →</a>
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" class="docs-link" target="_blank" rel="noopener">Docs →</a>
</div>
</div>
</div><!-- /model-grid -->
</section>
<!-- ── Custom Model Note ── -->
<section class="page-section fade-up">
<div style="background:var(--paper); border:1px solid var(--line); border-radius:14px; padding:1.6rem 1.8rem; display:flex; gap:1.2rem; align-items:flex-start;">
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<div>
<h3 style="margin:0 0 0.4rem; font-size:1rem;">Need a custom model?</h3>
<p style="margin:0 0 0.8rem; font-size:0.88rem; color:var(--muted); line-height:1.55;">PyHealth models inherit from <code style="font-family:monospace; font-size:0.85rem; background:rgba(0,0,0,0.05); padding:0.1rem 0.35rem; border-radius:4px;">BaseModel</code>. Your custom model immediately works with the <code style="font-family:monospace; font-size:0.85rem; background:rgba(0,0,0,0.05); padding:0.1rem 0.35rem; border-radius:4px;">Trainer</code>, all metrics, and explainability tools — no extra wiring needed.</p>
<div style="display:flex; gap:0.6rem; flex-wrap:wrap;">
<a href="https://pyhealth.readthedocs.io/en/latest/api/models.html" target="_blank" rel="noopener" style="font-size:0.83rem; font-weight:500; color:var(--accent); text-decoration:none; padding:0.3rem 0.75rem; border:1.5px solid rgba(201,95,38,0.3); border-radius:7px; transition:background 0.15s;" onmouseover="this.style.background='rgba(201,95,38,0.07)'" onmouseout="this.style.background=''">Model API Reference →</a>
<a href="https://github.com/sunlabuiuc/PyHealth" target="_blank" rel="noopener" style="font-size:0.83rem; font-weight:500; color:var(--muted); text-decoration:none; padding:0.3rem 0.75rem; border:1.5px solid var(--line); border-radius:7px; transition:background 0.15s;" onmouseover="this.style.background='rgba(0,0,0,0.03)'" onmouseout="this.style.background=''">View Source on GitHub →</a>
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