Add BHCToAVS model for patient-friendly summaries

docs/api/models.rst

@@ -8,6 +8,7 @@ We implement the following models for supporting multiple healthcare predictive
     :maxdepth: 3
 
     models/pyhealth.models.BaseModel
+    models/pyhealth.models.BHCToAVS
     models/pyhealth.models.LogisticRegression
     models/pyhealth.models.MLP
     models/pyhealth.models.CNN

docs/api/models/pyhealth.models.BHCToAVS.rst

@@ -0,0 +1,11 @@
+pyhealth.models.bhc_to_avs
+==========================
+
+BHCToAVS
+------------------------------
+
+.. autoclass:: pyhealth.models.bhc_to_avs.BHCToAVS
+    :members:
+    :inherited-members:
+    :show-inheritance:
+    :undoc-members:

examples/bhc_to_avs_example.py

@@ -0,0 +1,21 @@
+from pyhealth.models.bhc_to_avs import BHCToAVS
+
+# Initialize the model
+model = BHCToAVS()
+
+# Example Brief Hospital Course (BHC) text with common clinical abbreviations generated synthetically via ChatGPT 5.1
+bhc = (
+    "Pt admitted with acute onset severe epigastric pain and hypotension. "
+    "Labs notable for elevated lactate, WBC 18K, mild AST/ALT elevation, and Cr 1.4 (baseline 0.9). "
+    "CT A/P w/ contrast demonstrated peripancreatic fat stranding c/w acute pancreatitis; "
+    "no necrosis or peripancreatic fluid collection. "
+    "Pt received aggressive IVFs, electrolyte repletion, IV analgesia, and NPO status initially. "
+    "Serial abd exams remained benign with no rebound or guarding. "
+    "BP stabilized, lactate downtrended, and pt tolerated ADAT to low-fat diet without recurrence of sx. "
+    "Discharged in stable condition w/ instructions for GI f/u and outpatient CMP in 1 week."
+)
+
+# Generate a patient-friendly After-Visit Summary
+print(model.predict(bhc))
+
+# Expected output: A simplified, patient-friendly summary explaining the hospital stay without medical jargon.

pyhealth/models/__init__.py

@@ -1,6 +1,7 @@
 from .adacare import AdaCare, AdaCareLayer
 from .agent import Agent, AgentLayer
 from .base_model import BaseModel
+from .bhc_to_avs import BHCToAVS
 from .cnn import CNN, CNNLayer
 from .concare import ConCare, ConCareLayer
 from .contrawr import ContraWR, ResBlock2D
@@ -26,4 +27,4 @@
 from .transformer import Transformer, TransformerLayer
 from .transformers_model import TransformersModel
 from .vae import VAE
-from .sdoh import SdohClassifier
+from .sdoh import SdohClassifier

pyhealth/models/bhc_to_avs.py

@@ -0,0 +1,98 @@
+# Author: Charan Williams
+# NetID: charanw2
+# Description: Converts clinical brief hospital course (BHC) data to after visit summaries using a fine-tuned Mistral 7B model.
+
+from typing import Dict, Any
+from dataclasses import dataclass, field
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+from peft import PeftModelForCausalLM
+from pyhealth.models.base_model import BaseModel
+
+_PROMPT = """Summarize for the patient what happened during the hospital stay:
+
+### Brief Hospital Course:
+{bhc}
+
+### Patient Summary:
+"""
+
+# System prompt used during inference
+_SYSTEM_PROMPT = (
+    "You are a clinical summarization model. Produce accurate, patient-friendly summaries "
+    "using only information from the doctor's note. Do not add new details.\n\n"
+)
+
+# Prompt used during fine-tuning
+_PROMPT = (
+    "Summarize for the patient what happened during the hospital stay based on this doctor's note:\n"
+    "{bhc}\n\n"
+    "Summary for the patient:\n"
+)
+
+@dataclass
+class BHCToAVS(BaseModel):
+    base_model_id: str = field(default="mistralai/Mistral-7B-Instruct")
+    """HuggingFace repo containing the base Mistral 7B model."""
+
+    adapter_model_id: str = field(default="williach31/mistral-7b-bhc-to-avs-lora")
+    """HuggingFace repo containing only LoRA adapter weights."""
+
+    def _get_pipeline(self):
+        """Create and cache the text-generation pipeline."""
+        if not hasattr(self, "_pipeline"):
+            # Load base model
+            base = AutoModelForCausalLM.from_pretrained(
+                self.base_model_id,
+                torch_dtype=torch.bfloat16,
+                device_map="auto"
+            )
+
+            # Load LoRA adapter
+            model = PeftModelForCausalLM.from_pretrained(
+                base,
+                self.adapter_model_id,
+                torch_dtype=torch.bfloat16
+            )
+
+            tokenizer = AutoTokenizer.from_pretrained(self.base_model_id)
+
+            # Create HF pipeline
+            self._pipeline = pipeline(
+                "text-generation",
+                model=model,
+                tokenizer=tokenizer,
+                device_map="auto",
+                model_kwargs={"torch_dtype": torch.bfloat16}
+            )
+
+        return self._pipeline
+
+    def predict(self, bhc_text: str) -> str:
+        """
+        Generate an After-Visit Summary (AVS) from a Brief Hospital Course (BHC) note.
+
+        Parameters
+        ----------
+        bhc_text : str
+            Raw BHC text.
+
+        Returns
+        -------
+        str
+            Patient-friendly summary.
+        """
+
+        prompt = _SYSTEM_PROMPT + _PROMPT.format(bhc=bhc_text)
+
+        pipe = self._get_pipeline()
+        outputs = pipe(
+            prompt,
+            max_new_tokens=512,
+            temperature=0.0,
+            eos_token_id=[pipe.tokenizer.eos_token_id],
+            pad_token_id=pipe.tokenizer.eos_token_id,
+        )
+
+        # Output is a single text string
+        return outputs[0]["generated_text"].strip()

tests/core/test_bhc_to_avs.py

@@ -0,0 +1,36 @@
+from tests.base import BaseTestCase
+from pyhealth.models.bhc_to_avs import BHCToAVS
+
+
+class TestBHCToAVS(BaseTestCase):
+    """Unit tests for the BHCToAVS model."""
+
+    def setUp(self):
+        self.set_random_seed()
+
+    def test_predict(self):
+        """Test the predict method of BHCToAVS."""
+        bhc_text = (
+            "Patient admitted with abdominal pain. Imaging showed no acute findings. "
+            "Pain improved with supportive care and the patient was discharged in stable condition."
+        )
+        model = BHCToAVS()
+        try:
+
+            summary = model.predict(bhc_text)
+
+            # Output must be type str
+            self.assertIsInstance(summary, str)
+
+            # Output should not be empty
+            self.assertGreater(len(summary.strip()), 0)
+
+            # Output should be different from input
+            self.assertNotIn(bhc_text[:40], summary)
+
+        except OSError as e:
+            # Allow test to pass if model download fails on e.g. on GitHub workflows
+            if "gated repo" in str(e).lower() or "404" in str(e):
+                pass
+            else:
+                raise e