import streamlit as st import streamlit.components.v1 as components import numpy as np import pandas as pd import torch from typing import Tuple, List from fpdf import FPDF from pyhealth.medcode import InnerMap from pyhealth.datasets import MIMIC3Dataset, SampleEHRDataset from pyhealth.tasks import medication_recommendation_mimic3_fn, diagnosis_prediction_mimic3_fn from pyhealth.models import GNN from pyhealth.explainer import HeteroGraphExplainer @st.cache_resource(hash_funcs={torch.nn.parameter.Parameter: lambda _: None}) def load_gnn() -> Tuple[torch.nn.Module, torch.nn.Module, torch.nn.Module, torch.nn.Module, MIMIC3Dataset, SampleEHRDataset, SampleEHRDataset]: # Monkey-patch pandas.read_csv to inject BackBlaze B2 storage options original_read_csv = pd.read_csv def patched_read_csv(filepath_or_buffer, *args, **kwargs): # If it's an S3 path, ensure storage_options are set for BackBlaze B2 if isinstance(filepath_or_buffer, str): # Fix Windows backslash in S3 paths if 'piemed' in filepath_or_buffer and '\\' in filepath_or_buffer: filepath_or_buffer = filepath_or_buffer.replace('\\', '/') if filepath_or_buffer.startswith('s3://') or 'piemed/' in filepath_or_buffer: if not filepath_or_buffer.startswith('s3://'): filepath_or_buffer = 's3://' + filepath_or_buffer kwargs['storage_options'] = { 'key': st.secrets.aws_access_key_id, 'secret': st.secrets.aws_secret_access_key, 'client_kwargs': { 'endpoint_url': st.secrets.endpoint_url, 'region_name': st.secrets.region_name } } return original_read_csv(filepath_or_buffer, *args, **kwargs) pd.read_csv = patched_read_csv try: dataset = MIMIC3Dataset( root=st.secrets.s3_uri, tables=["DIAGNOSES_ICD","PROCEDURES_ICD","PRESCRIPTIONS","NOTEEVENTS_ICD"], code_mapping={"NDC": ("ATC", {"target_kwargs": {"level": 4}})}, ) mimic3sample_med = dataset.set_task(task_fn=medication_recommendation_mimic3_fn) mimic3sample_diag = dataset.set_task(task_fn=diagnosis_prediction_mimic3_fn) model_med_ig = GNN( dataset=mimic3sample_med, convlayer="GraphConv", feature_keys=["procedures", "diagnosis", "symptoms"], label_key="medications", k=0, embedding_dim=128, hidden_channels=128 ) model_med_gnn = GNN( dataset=mimic3sample_med, convlayer="GraphConv", feature_keys=["procedures", "diagnosis", "symptoms"], label_key="medications", k=0, embedding_dim=128, hidden_channels=128 ) model_diag_ig = GNN( dataset=mimic3sample_diag, convlayer="GraphConv", feature_keys=["procedures", "medications", "symptoms"], label_key="diagnosis", k=0, embedding_dim=128, hidden_channels=128 ) model_diag_gnn = GNN( dataset=mimic3sample_diag, convlayer="GraphConv", feature_keys=["procedures", "medications", "symptoms"], label_key="diagnosis", k=0, embedding_dim=128, hidden_channels=128 ) return model_med_ig, model_med_gnn, model_diag_ig, model_diag_gnn, dataset, mimic3sample_med, mimic3sample_diag finally: # Restore original pandas.read_csv pd.read_csv = original_read_csv @st.cache_data(hash_funcs={torch.Tensor: lambda _: None}) def get_list_output(y_prob: torch.Tensor, last_visit: pd.DataFrame, task: str, _mimic3sample: SampleEHRDataset, top_k: int = 10) -> List[str]: sorted_indices = [] for i in range(len(y_prob)): top_indices = np.argsort(-y_prob[i, :])[:top_k] sorted_indices.append(top_indices) list_output = [] # get the list of all labels in the dataset if task == "medications": list_labels = _mimic3sample.get_all_tokens('medications') atc = InnerMap.load("ATC") elif task == "diagnosis": list_labels = _mimic3sample.get_all_tokens('diagnosis') icd9 = InnerMap.load("ICD9CM") sorted_indices = list(sorted_indices) # iterate over the top indexes for each sample in test_ds for (i, sample), top in zip(last_visit.iterrows(), sorted_indices): # create an empty list to store the recommended medications for this sample sample_list_output = [] # iterate over the top indexes for this sample for k in top: # append the medication at the i-th index to the recommended medications list for this sample if task == "medications": sample_list_output.append(atc.lookup(list_labels[k])) elif task == "diagnosis": if list_labels[k].startswith("E"): list_labels[k] = list_labels[k] + "0" sample_list_output.append(icd9.lookup(list_labels[k])) # append the recommended medications for this sample to the recommended medications list list_output.append(sample_list_output) return list_output, sorted_indices def explainability(model: GNN, explain_dataset: SampleEHRDataset, selected_idx: int, visualization: str, algorithm: str, task: str, threshold: int): explainer = HeteroGraphExplainer( algorithm=algorithm, dataset=explain_dataset, model=model, label_key=task, threshold_value=threshold, top_k=threshold, feat_size=128, root="./streamlit_results/", ) if task == "medications": visit_drug = explainer.subgraph['visit', 'medication'].edge_index visit_drug = visit_drug.T n = 0 for vis_drug in visit_drug: vis_drug = np.array(vis_drug) if vis_drug[1] == selected_idx: break n += 1 elif task == "diagnosis": visit_diag = explainer.subgraph['visit', 'diagnosis'].edge_index visit_diag = visit_diag.T n = 0 for vis_diag in visit_diag: vis_diag = np.array(vis_diag) if vis_diag[1] == selected_idx: break n += 1 explainer.explain(n=n) if visualization == "Explainable": explainer.explain_graph(k=0, human_readable=True, dashboard=True) else: explainer.explain_graph(k=0, human_readable=False, dashboard=True) explainer.explain_results(n=n) explainer.explain_results(n=n, doctor_type="Internist_Doctor") HtmlFile = open("streamlit_results/explain_graph.html", 'r', encoding='utf-8') source_code = HtmlFile.read() components.html(source_code, height=520) def gen_pdf(patient, name, lastname, visit, list_output, medical_scenario, internist_scenario): pdf = FPDF() pdf.add_page() pdf.add_font("OpenSans", style="", fname="font/OpenSans.ttf") pdf.add_font("OpenSans", style="B", fname="font/OpenSans-Bold.ttf") # Title pdf.set_font("OpenSans", 'B', 14) pdf.cell(0, 10, 'Patient Medical Report', 0, 1, 'C', markdown=True) pdf.ln(5) # Patient Info pdf.set_font("OpenSans", 'B', 10) pdf.cell(0, 10, 'Patient Information', 0, 1, 'L', markdown=True) pdf.set_font("OpenSans", '', 8) pdf.cell(0, 3, f"Patient ID: **{patient}** - Name: **{name.split('[')[1].split(']')[0]}** Surname: **{lastname}** - Hospital admission n°: **{visit}**", 0, 1, 'L', markdown=True) pdf.ln(5) # Left column (Medical Scenario) left_x = 10 right_x = 110 col_width = 90 # Right column (Recommendations) pdf.set_xy(right_x, pdf.get_y()) pdf.set_font("OpenSans", 'B', 10) pdf.cell(col_width - 20, 10, 'Recommendations', 0, 1, 'L') pdf.set_xy(right_x, pdf.get_y()) pdf.set_font("OpenSans", '', 8) for i, output in enumerate(list_output): tensor_value = output[0].item() # Convert tensor to number recommendation = output[1] pdf.set_xy(right_x, pdf.get_y()) pdf.cell(col_width - 20, 3, f"Medication {i+1}: {tensor_value}, {recommendation}", 0, 1, 'L') # Medical Scenario pdf.set_xy(left_x, pdf.get_y() - 40) pdf.set_font("OpenSans", 'B', 10) pdf.cell(col_width, 10, 'Medical Scenario', 0, 1, 'L', markdown=True) pdf.set_xy(left_x, pdf.get_y()) pdf.set_font("OpenSans", '', 8) pdf.multi_cell(col_width, 3, medical_scenario, 0, 'L', markdown=True) # internist_scenario pdf.set_xy(left_x, pdf.get_y()) pdf.set_font("OpenSans", 'B', 10) pdf.cell(0, 10, 'Internist Scenario', 0, 1, 'L', markdown=True) pdf.set_font("OpenSans", '', 8) pdf.multi_cell(0, 3, internist_scenario, 0, 'L', markdown=True) pdf.ln(5) return bytes(pdf.output())