一、术前阶段

1.1 数据采集与预处理系统

伪代码实现

# 多模态数据采集函数
def collect_patient_data(patient_id):
    # 医学影像数据
    ct_images = load_dicom_files("path/to/ct_scans")  # DICOM格式解析
    mri_images = load_nii_files("path/to/mri_scans")   # NIFTI格式解析
    
    # 电子病历结构化数据
    vital_signs = get_vital_parameters("patient_records")  # 血压、心率等
    medical_history = parse_medical_records("text_records") # 既往病史NLP处理
    
    # 基因数据（可选）
    gene_data = fetch_gene_profile("genome_database", patient_id)
    
    return {
   
        "images": {
   "CT": ct_images, "MRI": mri_images},
        "vitals": vital_signs,
        "history": medical_history,
        "genes": gene_data
    }

# 数据清洗与标准化
def preprocess_data(raw_data):
    # 缺失值处理
    filled_vitals = fill_missing_values(raw_data["vitals"], method="mean")
    
    # 医学影像标准化
    normalized_ct = normalize_image(raw_data["images"]["CT"])
    normalized_mri = normalize_image(raw_data["images"]["MRI"])
    
    # 特征编码
    encoded_history = encode_categorical(raw_data["history"])
    
    return {
   
        "images": {
   "CT": normalized_ct, "MRI": normalized_mri},
        "vitals": filled_vitals,
        "history": encoded_history
    }

流程图

1.2 特征提取与选择模块

伪代码实现

# 医学影像特征提取（以血肿分割为例）
def extract_imaging_features(ct_image):
    # 基于U-Net的血肿分割
    segmented_mask = unet_predict(ct_image)
    
    # 形态学特征计算
    volume = calculate_volume(segmented_mask)
    shape_features = extract_shape_descriptors(segmented_mask)
    
    return {
   **shape_features, "volume": volume}

# 临床特征选择
def select_clinical_features(vitals, history):
    # 基于XGBoost的特征重要性排序
    feature_importance = xgboost_feature_selection(vitals + history)
    selected_features = filter_top_features(feature_importance, threshold=0.1)
    return selected_features

流程图