dw做存资料网站网站设计制作都有哪些

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