# AI影像分析示例架构（简化版）
import tensorflow as tf
from tensorflow.keras import layers

def build_medical_imaging_model():
    model = tf.keras.Sequential([
        # 输入层：医学影像（如CT扫描）
        layers.Input(shape=(512, 512, 1)),

        # 卷积层1：检测基础特征（边缘、纹理）
        layers.Conv2D(32, 3, activation='relu'),
        layers.MaxPooling2D(2),

        # 卷积层2：检测复杂特征
        layers.Conv2D(64, 3, activation='relu'),
        layers.MaxPooling2D(2),

        # 卷积层3：检测高级特征
        layers.Conv2D(128, 3, activation='relu'),
        layers.MaxPooling2D(2),

        # 全连接层：整合特征
        layers.Flatten(),
        layers.Dense(256, activation='relu'),
        layers.Dropout(0.5),

        # 输出层：疾病分类
        layers.Dense(1, activation='sigmoid')  # 输出疾病概率
    ])

    return model

┌─────────────────────────────────────────────────────────────┐
│                   AI影像诊断工作流程                         │
└─────────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────────┐
│ 1. 影像获取                                                │
│    - X光、CT、MRI、超声、乳腺X光摄影                        │
└─────────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────────┐
│ 2. 影像预处理                                              │
│    - 去噪标准化                                             │
│    - 对比度增强                                             │
│    - 区域裁剪                                               │
└─────────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────────┐
│ 3. AI分析                                                  │
│    ├─ 病灶检测                                              │
│    ├─ 特征提取                                              │
│    ├─ 模式识别                                              │
│    └─ 风险评分                                              │
└─────────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────────┐
│ 4. 放射科医生审核                                          │
│    - 查看AI标记的异常区域                                   │
│    - 结合临床信息综合判断                                   │
│    - 做出最终诊断                                           │
└─────────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────────┐
│ 5. 诊断报告                                                │
│    - AI辅助置信度                                           │
│    - 关键发现                                               │
│    - 建议后续行动                                           │
└─────────────────────────────────────────────────────────────┘

# 乳腺癌检测AI模型应用示例
def detect_breast_cancer(mammogram_image):
    """
    使用AI模型分析乳腺X光图像
    """
    # 1. 影像预处理
    preprocessed = preprocess_mammogram(mammogram_image)

    # 2. AI模型推理
    model = load_breast_cancer_model()
    predictions = model.predict(preprocessed)

    # 3. 结果分析
    findings = {
        'malignancy_probability': predictions['malignancy'],
        'findings': predictions['detected_abnormalities'],
        'birads_score': predictions['classification'],
        'confidence': predictions['confidence'],
        'recommendations': generate_recommendations(predictions)
    }

    return findings

def generate_recommendations(predictions):
    """生成临床建议"""
    if predictions['malignancy'] > 0.8:
        return [
            "建议立即进行组织活检",
            "考虑进行乳腺MRI进一步评估",
            "咨询乳腺外科专科医生"
        ]
    elif predictions['malignancy'] > 0.5:
        return [
            "建议短期随访（6个月）",
            "考虑进行超声检查"
        ]
    else:
        return [
            "常规年度筛查",
            "保持健康生活方式"
        ]

# 肺结节检测和分析示例
class LungNoduleAnalyzer:
    def __init__(self):
        self.detection_model = load_nodule_detection_model()
        self.classification_model = load_malignancy_model()

    def analyze_ct_scan(self, ct_scan):
        """
        分析胸部CT扫描中的肺结节
        """
        # 1. 检测肺结节
        nodules = self.detection_model.detect(ct_scan)

        findings = []
        for nodule in nodules:
            # 2. 分析每个结节特征
            features = self.extract_nodule_features(nodule)

            # 3. 评估恶性概率
            malignancy_prob = self.classification_model.predict(features)

            # 4. 生成建议
            recommendation = self.generate_nodule_recommendation(
                nodule, malignancy_prob
            )

            findings.append({
                'location': nodule.location,
                'size_mm': nodule.diameter,
                'density': nodule.density_type,
                'malignancy_probability': malignancy_prob,
                'recommendation': recommendation
            })

        return findings

    def extract_nodule_features(self, nodule):
        """提取结节特征"""
        return {
            'diameter': nodule.diameter,
            'volume': nodule.volume,
            'density': nodule.mean_density,
            'shape_irregularity': calculate_shape_irregularity(nodule),
            'spiculation': detect_spiculation(nodule),
            'texture_features': extract_texture_features(nodule)
        }

    def generate_nodule_recommendation(self, nodule, malignancy_prob):
        """生成结节管理建议"""
        if malignancy_prob > 0.65:
            return {
                'action': 'immediate_intervention',
                'recommendation': '建议进行PET-CT和活检',
                'follow_up': '2周内'
            }
        elif malignancy_prob > 0.25:
            return {
                'action': 'short_term_follow_up',
                'recommendation': '3个月后复查CT',
                'follow_up': '3个月'
            }
        else:
            return {
                'action': 'routine_screening',
                'recommendation': '年度LDCT筛查',
                'follow_up': '12个月'
            }

# 可穿戴ECG监测系统
class WearableECGMonitor:
    def __init__(self):
        self.ai_model = load_ecg_ai_model()
        self.risk_predictor = load_cardio_risk_model()

    def analyze_realtime_ecg(self, ecg_stream):
        """
        实时分析来自可穿戴设备的ECG数据
        """
        # 1. 信号预处理
        cleaned_ecg = self.preprocess_ecg(ecg_stream)

        # 2. AI实时分析
        analysis = self.ai_model.analyze(cleaned_ecg)

        # 3. 异常检测
        if analysis['abnormality_detected']:
            # 4. 紧急评估
            emergency_level = self.assess_emergency(analysis)

            if emergency_level == 'HIGH':
                self.send_emergency_alert(analysis)
            else:
                self.schedule_consultation(analysis)

        # 5. 长期风险更新
        risk_update = self.risk_predictor.update_risk_profile(
            ecg_stream,
            analysis
        )

        return {
            'current_status': analysis['heart_status'],
            'abnormalities': analysis['detected_issues'],
            'emergency_alert': emergency_level == 'HIGH',
            'risk_factors': risk_update
        }

    def assess_emergency(self, analysis):
        """
        评估紧急程度
        """
        high_risk_conditions = [
            'ventricular_fibrillation',
            'acute_myocardial_infarction',
            'complete_heart_block',
            'severe_bradycardia_hr＜40'
        ]

        for condition in high_risk_conditions:
            if condition in analysis['detected_issues']:
                return 'HIGH'

        medium_risk_conditions = [
            'atrial_fibrillation',
            'frequent_pvcs',
            'first_degree_av_block'
        ]

        for condition in medium_risk_conditions:
            if condition in analysis['detected_issues']:
                return 'MEDIUM'

        return 'LOW'

# 综合心血管风险预测
class CardioRiskPredictor:
    def __init__(self):
        self.model = load_ensemble_risk_model()

    def predict_cardiovascular_risk(self, patient_data):
        """
        综合预测10年心血管疾病风险
        """
        # 1. 提取特征
        features = self.extract_features(patient_data)

        # 2. AI模型预测
        risk_score = self.model.predict(features)

        # 3. 风险分层
        risk_category = self.categorize_risk(risk_score)

        # 4. 生成干预建议
        interventions = self.generate_interventions(
            risk_category,
            features
        )

        return {
            'risk_score': risk_score,
            'risk_category': risk_category,
            'probability_cv_event': f"{risk_score * 100:.1f}%",
            'key_risk_factors': self.identify_key_factors(features),
            'recommended_interventions': interventions,
            'follow_up_schedule': self.recommend_follow_up(risk_category)
        }

    def extract_features(self, patient_data):
        """提取和工程化预测特征"""
        return {
            # 传统危险因素
            'age': patient_data['age'],
            'sex': 1 if patient_data['sex'] == 'male' else 0,
            'sbp': patient_data['systolic_bp'],
            'dbp': patient_data['diastolic_bp'],
            'total_cholesterol': patient_data['cholesterol'],
            'hdl': patient_data['hdl_cholesterol'],
            'diabetes': 1 if patient_data['has_diabetes'] else 0,
            'smoker': 1 if patient_data['current_smoker'] else 0,

            # 基因风险评分
            'genetic_risk_score': patient_data.get('grs', 0),

            # 影像学特征
            'coronary_calcium_score': patient_data.get('cac_score', 0),
            'plaques_detected': patient_data.get('num_plaques', 0),

            # 生活方式
            'physical_activity_min_week': patient_data.get('exercise_minutes', 0),
            'sleep_hours': patient_data.get('avg_sleep_hours', 7),
            'diet_quality_score': patient_data.get('diet_score', 50),

            # 生物标志物
            'lp_a': patient_data.get('lipoprotein_a', 0),
            'hs_crp': patient_data.get('hs_crp', 0),
            'hba1c': patient_data.get('hba1c', 5.7),

            # 可穿戴设备数据
            'daily_steps': patient_data.get('avg_daily_steps', 5000),
            'resting_hr': patient_data.get('resting_heart_rate', 70),
            'hrv': patient_data.get('heart_rate_variability', 50)
        }

    def categorize_risk(self, risk_score):
        """风险分层"""
        if risk_score ＜ 0.05:
            return '低风险'
        elif risk_score ＜ 0.10:
            return '中等风险'
        elif risk_score ＜ 0.20:
            return '高风险'
        else:
            return '极高风险'

    def generate_interventions(self, risk_category, features):
        """生成个性化干预建议"""
        interventions = []

        # 基于关键风险因素推荐
        if features['sbp'] > 140:
            interventions.append({
                'priority': 'HIGH',
                'area': '血压管理',
                'recommendations': [
                    '启动或调整降压药物治疗',
                    '每日监测血压',
                    '减少钠盐摄入',
                    '增加有氧运动'
                ]
            })

        if features['smoker'] == 1:
            interventions.append({
                'priority': 'CRITICAL',
                'area': '戒烟',
                'recommendations': [
                    '立即开始戒烟程序',
                    '考虑尼古丁替代疗法',
                    '参加戒烟支持小组',
                    '使用戒烟应用程序'
                ]
            })

        if features['physical_activity_min_week'] ＜ 150:
            interventions.append({
                'priority': 'HIGH',
                'area': '身体活动',
                'recommendations': [
                    '每周至少150分钟中等强度运动',
                    '每天步行30分钟',
                    '使用健身追踪器监测活动',
                    '参加心脏康复项目'
                ]
            })

        return interventions

# 阿尔茨海默病风险预测
class AlzheimerRiskPredictor:
    def __init__(self):
        self.model = load_alzheimer_prediction_model()

    def predict_alzheimer_risk(self, patient_data):
        """
        综合多模态数据预测阿尔茨海默病风险
        """
        # 1. MRI分析
        if 'mri_scan' in patient_data:
            mri_features = self.analyze_mri(patient_data['mri_scan'])
        else:
            mri_features = None

        # 2. 基因风险分析
        genetic_risk = self.analyze_genetic_markers(
            patient_data.get('apoe_genotype', 'ε3/ε3')
        )

        # 3. 认知测试分析
        cognitive_scores = self.analyze_cognitive_tests(
            patient_data.get('cognitive_tests', {})
        )

        # 4. 语言模式分析
        if 'language_samples' in patient_data:
            language_features = self.analyze_language_patterns(
                patient_data['language_samples']
            )
        else:
            language_features = None

        # 5. 生活方式因素
        lifestyle_risk = self.assess_lifestyle_factors(patient_data)

        # 6. 综合预测
        risk_assessment = self.model.predict({
            'mri_features': mri_features,
            'genetic_risk': genetic_risk,
            'cognitive_scores': cognitive_scores,
            'language_features': language_features,
            'lifestyle': lifestyle_risk
        })

        return {
            'risk_probability': risk_assessment['probability'],
            'risk_category': risk_assessment['category'],
            'predicted_onset_years': risk_assessment['onset_estimate'],
            'key_risk_factors': risk_assessment['contributing_factors'],
            'preventive_recommendations': self.generate_prevention_plan(risk_assessment)
        }

    def analyze_mri(self, mri_scan):
        """分析MRI扫描"""
        # 海马体体积
        hippocampal_volume = self.measure_hippocampal_volume(mri_scan)

        # 皮层厚度
        cortical_thickness = self.measure_cortical_thickness(mri_scan)

        # 萎缩模式
        atrophy_pattern = self.analyze_atrophy_pattern(mri_scan)

        # 白质高信号
        white_matter_hyperintensities = self.detect_wmh(mri_scan)

        return {
            'hippocampal_volume': hippocampal_volume,
            'cortical_thickness_avg': cortical_thickness['average'],
            'entorhinal_cortex_thickness': cortical_thickness['entorhinal'],
            'atrophy_pattern': atrophy_pattern,
            'white_matter_load': white_matter_hyperintensities
        }

    def analyze_language_patterns(self, language_samples):
        """分析语言模式（早期认知衰退的标志）"""
        features = {
            # 词汇丰富度
            'vocabulary_diversity': self.calculate_vocabulary_diversity(language_samples),
            'lexical_complexity': self.assess_lexical_complexity(language_samples),

            # 句子结构
            'sentence_complexity': self.analyze_sentence_structure(language_samples),
            'grammar_errors': self.count_grammar_errors(language_samples),

            # 语言流畅度
            'speech_rate': self.calculate_speech_rate(language_samples),
            'pauses_per_minute': self.count_pauses(language_samples),
            'filler_words_ratio': self.count_fillers(language_samples),

            # 语义连贯性
            'semantic_coherence': self.assess_coherence(language_samples),
            'topic_maintenance': self.assess_topic_maintenance(language_samples),

            # 命名能力
            'word_finding_difficulties': self.detect_word_finding_issues(language_samples)
        }

        return features

    def generate_prevention_plan(self, risk_assessment):
        """生成个性化预防计划"""
        plan = {
            'immediate_actions': [],
            'lifestyle_modifications': [],
            'monitoring_schedule': [],
            'interventions': []
        }

        # 基于风险因素推荐
        if risk_assessment['vascular_risk'] == 'HIGH':
            plan['lifestyle_modifications'].extend([
                '启动地中海饮食',
                '每周150分钟中等强度有氧运动',
                '严格控制血压（目标＜130/80）',
                '管理糖尿病（如果存在）',
                '戒烟（如果适用）'
            ])

        if risk_assessment['cognitive_engagement'] == 'LOW':
            plan['immediate_actions'].extend([
                '参加认知训练活动',
                '学习新技能或语言',
                '增加社交活动频率',
                '进行认知刺激游戏'
            ])

        if risk_assessment['sleep_quality'] == 'POOR':
            plan['lifestyle_modifications'].extend([
                '改善睡眠卫生',
                '治疗睡眠呼吸暂停（如果存在）',
                '目标每晚7-8小时睡眠'
            ])

        plan['monitoring_schedule'] = [
            '每6个月进行认知功能评估',
            '年度MRI扫描',
            '定期血液生物标志物检测'
        ]

        return plan

┌────────────────────────────────────────────────────────────────┐
│                   多组学AI整合平台                             │
└────────────────────────────────────────────────────────────────┘
                              │
        ┌─────────────────────┼─────────────────────┐
        │                     │                     │
        ▼                     ▼                     ▼
┌──────────────┐    ┌──────────────┐    ┌──────────────┐
│   基因组学   │    │   蛋白质组学 │    │   代谢组学   │
│              │    │              │    │              │
│ - SNP芯片    │    │ - 质谱分析   │    │ - 代谢产物   │
│ - 全基因组   │    │ - 抗体阵列   │    │ - NMR光谱    │
│   测序       │    │ - 蛋白定量   │    │ - LC-MS分析  │
│ - 外显子组   │    │ - 磷酸化     │    │ - 脂质组学   │
│   测序       │    │   分析       │    │              │
└──────────────┘    └──────────────┘    └──────────────┘
        │                     │                     │
        └─────────────────────┼─────────────────────┘
                              │
                              ▼
┌────────────────────────────────────────────────────────────────┐
│                    AI整合分析引擎                               │
│  ┌──────────────────────────────────────────────────────┐    │
│  │ 1. 数据预处理和标准化                                 │    │
│  │ 2. 特征选择和工程                                     │    │
│  │ 3. 多模态机器学习模型                                 │    │
│  │    - 深度神经网络                                     │    │
│  │    - 图神经网络（GNN）                                │    │
│  │    - 集成学习                                         │    │
│  │ 4. 因果推断分析                                       │    │
│  │ 5. 不确定性量化                                       │    │
│  └──────────────────────────────────────────────────────┘    │
└────────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌────────────────────────────────────────────────────────────────┐
│                  个性化风险预测和干预                           │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐        │
│  │  疾病风险   │  │  治疗反应   │  │  预后预测   │        │
│  │  预测       │  │  预测       │  │             │        │
│  └──────────────┘  └──────────────┘  └──────────────┘        │
└────────────────────────────────────────────────────────────────┘

# 基因组风险评分
class GenomicRiskAnalyzer:
    def __init__(self):
        self.prs_calculator = load_polygenic_risk_model()
        self.variant_interpreter = load_variant_interpretation_model()

    def analyze_genomic_risk(self, genetic_data, phenotype='disease'):
        """
        分析基因组数据预测疾病风险
        """
        # 1. 计算多基因风险评分（PRS）
        prs = self.prs_calculator.calculate(genetic_data)

        # 2. 罕见变异分析
        rare_variants = self.identify_rare_variants(genetic_data)

        # 3. 变异致病性预测
        pathogenic_variants = []
        for variant in rare_variants:
            pathogenicity = self.variant_interpreter.predict(variant)
            if pathogenicity['probability'] > 0.8:
                pathogenic_variants.append({
                    'variant': variant,
                    'pathogenicity': pathogenicity,
                    'associated_genes': variant['genes']
                })

        # 4. 基因-环境交互分析
        gene_environment_risk = self.analyze_gene_environment_interactions(
            genetic_data,
            self.get_lifestyle_factors()
        )

        # 5. 综合风险评估
        risk_assessment = {
            'polygenic_risk_score': prs,
            'prs_percentile': self.get_prs_percentile(prs, phenotype),
            'high_risk_variants': pathogenic_variants,
            'relative_risk': self.calculate_relative_risk(prs, pathogenic_variants),
            'absolute_risk': self.calculate_absolute_risk(
                prs,
                pathogenic_variants,
                gene_environment_risk
            ),
            'gene_environment_interactions': gene_environment_risk,
            'recommended_screening': self.recommend_screening_protocol(risk_assessment),
            'preventive_measures': self.recommend_preventive_measures(risk_assessment)
        }

        return risk_assessment

    def recommend_screening_protocol(self, risk_assessment):
        """基于基因风险推荐筛查方案"""
        if risk_assessment['absolute_risk'] > 0.20:  # >20%风险
            return {
                'screening_frequency': '每6个月',
                'imaging_studies': ['MRI', 'CT', '超声'],
                'laboratory_tests': ['肿瘤标志物', '血液检查'],
                'genetic_counseling': True
            }
        elif risk_assessment['absolute_risk'] > 0.10:  # 10-20%风险
            return {
                'screening_frequency': '每年',
                'imaging_studies': ['超声', 'X线'],
                'laboratory_tests': ['基础血液检查'],
                'genetic_counseling': True
            }
        else:  # ＜10%风险
            return {
                'screening_frequency': '每2-3年',
                'imaging_studies': ['常规筛查'],
                'laboratory_tests': ['常规检查'],
                'genetic_counseling': False
            }

# 传染病爆发早期预警系统
class EpidemicEarlyWarningSystem:
    def __init__(self):
        self.anomaly_detector = load_anomaly_detection_model()
        self.forecaster = load_epidemic_forecasting_model()
        self.cluster_detector = load_spatial_cluster_model()

    def monitor_disease_outbreaks(self, real_time_data_streams):
        """
        监测多种数据流以检测异常疫情信号
        """
        alerts = []

        # 1. 分析临床数据流
        clinical_anomalies = self.analyze_clinical_data(
            real_time_data_streams['clinical_reports']
        )

        # 2. 分析数字流行病学数据
        digital_epidemiology = self.analyze_digital_data(
            real_time_data_streams['search_trends'],
            real_time_data_streams['social_media']
        )

        # 3. 分析可穿戴设备数据
        wearable_signals = self.analyze_wearable_data(
            real_time_data_streams['wearable_devices']
        )

        # 4. 空间聚类分析
        spatial_clusters = self.detect_spatial_clusters(
            clinical_anomalies,
            digital_epidemiology
        )

        # 5. 综合风险评估
        for cluster in spatial_clusters:
            risk_score = self.calculate_outbreak_risk(
                cluster,
                clinical_anomalies,
                digital_epidemiology,
                wearable_signals
            )

            if risk_score > 0.7:  # 高风险阈值
                alert = self.generate_epidemic_alert(cluster, risk_score)
                alerts.append(alert)

        return alerts

    def analyze_clinical_data(self, clinical_reports):
        """分析临床报告数据"""
        # 按症状聚合
        symptom_counts = self.aggregate_symptoms(clinical_reports)

        # 检测异常增长
        anomalies = self.anomaly_detector.detect(symptom_counts)

        return {
            'symptom_clusters': anomalies,
            'geographic_distribution': self.map_geographically(clinical_reports),
            'severity_distribution': self.analyze_severity(clinical_reports)
        }

    def analyze_digital_data(self, search_trends, social_media):
        """分析数字流行病学数据"""
        # 搜索趋势分析
        search_anomalies = self.analyze_search_trends(search_trends)

        # 社交媒体文本挖掘
        social_signals = self.mine_social_media_health_mentions(social_media)

        return {
            'search_volume_anomalies': search_anomalies,
            'social_media_activity': social_signals,
            'sentiment_analysis': self.analyze_sentiment(social_media)
        }

    def analyze_wearable_data(self, wearable_data):
        """分析可穿戴设备数据"""
        # 聚合健康指标
        aggregated_metrics = self.aggregate_health_metrics(wearable_data)

        # 检测群体性异常
        population_anomalies = self.detect_population_level_anomalies(
            aggregated_metrics
        )

        return {
            'resting_heart_rate_elevations': population_anomalies.get('hr_elevation'),
            'sleep_pattern_changes': population_anomalies.get('sleep_changes'),
            'activity_level_declines': population_anomalies.get('activity_decline'),
            'temperature_anomalies': population_anomalies.get('temperature_spikes')
        }

    def calculate_outbreak_risk(self, cluster, clinical, digital, wearable):
        """综合计算爆发风险"""
        risk_components = {
            'clinical_intensity': clinical['symptom_clusters'].get(
                cluster['region'], 0
            ),
            'digital_signal_strength': digital['search_volume_anomalies'].get(
                cluster['region'], 0
            ),
            'wearable_concordance': wearable.get(cluster['region'], 0),
            'spatial_compactness': cluster['compactness_score'],
            'temporal_acceleration': cluster['growth_rate']
        }

        # 加权综合风险评分
        risk_score = (
            risk_components['clinical_intensity'] * 0.35 +
            risk_components['digital_signal_strength'] * 0.25 +
            risk_components['wearable_concordance'] * 0.20 +
            risk_components['spatial_compactness'] * 0.10 +
            risk_components['temporal_acceleration'] * 0.10
        )

        return risk_score

    def generate_epidemic_alert(self, cluster, risk_score):
        """生成流行病警报"""
        return {
            'alert_level': self.determine_alert_level(risk_score),
            'affected_region': cluster['region'],
            'suspected_pathogen': cluster['suspected_cause'],
            'risk_score': risk_score,
            'confidence_interval': cluster['confidence'],
            'recommended_actions': self.generate_response_actions(cluster, risk_score),
            'estimated_cases': cluster['estimated_case_count'],
            'growth_rate': cluster['growth_rate'],
            'timestamp': datetime.now().isoformat()
        }

# 未来的个性化健康管理系统
class PersonalizedHealthSystem:
    def __init__(self):
        self.digital_twin = create_patient_digital_twin()
        self.predictive_models = load_predictive_models()
        self.intervention_recommender = load_intervention_system()

    def continuous_health_monitoring(self, patient_id):
        """持续健康监测和预测"""
        while True:
            # 1. 收集多模态数据
            patient_data = self.collect_real_time_data(patient_id)

            # 2. 更新数字孪生
            self.digital_twin.update(patient_data)

            # 3. 预测健康轨迹
            health_trajectory = self.predictive_models.predict(
                self.digital_twin.current_state
            )

            # 4. 检测风险信号
            risks = self.detect_emerging_risks(health_trajectory)

            # 5. 主动干预建议
            if risks:
                interventions = self.intervention_recommender.recommend(
                    risks,
                    self.digital_twin
                )

                # 6. 实施预防性干预
                self.implement_interventions(interventions)

            # 7. 监测干预效果
            self.monitor_intervention_effectiveness(interventions)

            sleep(monitoring_interval)  # 持续监测