AI技术 - 数字人医疗健康应用技术

# 智能医疗助手示例 - Python import numpy as np import pandas as pd from typing import List # import Dict import Tuple import json from datetime import datetime import torch import torch.nn as nn from transformers import BertTokenizer import BertModel class IntelligentMedicalAssistant: def __init__(self, medical_kb_path: str, symptom_model_path: str): self.medical_knowledge_base = self.load_medical_kb(medical_kb_path) self.symptom_model = self.load_symptom_model(symptom_model_path) self.patient_records = {} self.diagnosis_history = [] def load_medical_kb(self, path: str) -> Dict: """加载医学知识库""" with open(path, 'r', encoding='utf-8') as f: return json.load(f) def load_symptom_model(self, path: str) -> nn.Module: """加载症状分析模型""" model = SymptomAnalysisModel() model.load_state_dict(torch.load(path)) model.eval() return model def analyze_symptoms(self, patient_id: str, symptoms: List[str], additional_info: Dict = None) -> Dict: """分析患者症状""" # 症状预处理 processed_symptoms = # self.preprocess_symptoms(symptoms) # 症状分析 symptom_analysis = # self.symptom_analysis_engine(processed_symptoms) # 获取患者历史记录 patient_history = # self.patient_records.get(patient_id, {}) # 生成诊断建议 diagnosis_suggestions = # self.generate_diagnosis_suggestions( symptom_analysis, patient_history, additional_info ) # 生成治疗建议 # treatment_suggestions = self.generate_treatment_suggestions( diagnosis_suggestions, patient_history ) # 记录诊断历史 self.record_diagnosis(patient_id, symptoms, diagnosis_suggestions) return { # 'symptom_analysis': symptom_analysis, 'diagnosis_suggestions': diagnosis_suggestions, 'treatment_suggestions': treatment_suggestions, 'health_recommendations': self.generate_health_recommendations( diagnosis_suggestions, patient_history ), 'follow_up_advice': self.generate_follow_up_advice(diagnosis_suggestions) } def preprocess_symptoms(self, symptoms: List[str]) -> List[Dict]: """预处理症状信息""" processed = [] for symptom in symptoms: # 症状标准化 # standardized = self.standardize_symptom(symptom) # 症状分类 category = # self.classify_symptom(standardized) # 症状严重程度评估 severity = # self.assess_symptom_severity(standardized) processed.append({ 'original': symptom, 'standardized': standardized, 'category': category, 'severity': severity }) return processed def symptom_analysis_engine(self, processed_symptoms: List[Dict]) -> Dict: """症状分析引擎""" analysis = { 'primary_symptoms': [], 'secondary_symptoms': [], 'symptom_patterns': [], 'risk_factors': [], 'urgency_level': 'low' } for symptom in processed_symptoms: # 分析主要症状 if symptom['severity'] >= # 7: analysis['primary_symptoms'].append(symptom) else: analysis['secondary_symptoms'].append(symptom) # 识别症状模式 pattern = self.identify_symptom_pattern(symptom) if pattern: # analysis['symptom_patterns'].append(pattern) # 评估风险因素 risk_factors = # self.identify_risk_factors(symptom) analysis['risk_factors'].extend(risk_factors) # 评估紧急程度 # analysis['urgency_level'] = self.assess_urgency_level(analysis) return analysis def generate_diagnosis_suggestions(self, symptom_analysis: Dict, patient_history: Dict, additional_info: Dict = None) -> List[Dict]: """生成诊断建议""" suggestions = [] # 基于症状匹配疾病 matched_diseases = # self.match_symptoms_to_diseases(symptom_analysis) for disease in matched_diseases: # 计算诊断概率 # probability = self.calculate_diagnosis_probability( disease, symptom_analysis, patient_history ) # 生成诊断建议 suggestion = { 'disease_name': disease['name'], 'disease_code': disease['code'], # 'probability': probability, 'confidence_level': self.assess_confidence_level(probability), 'supporting_evidence': disease['supporting_evidence'], 'differential_diagnosis': disease['differential_diagnosis'], 'recommended_tests': disease['recommended_tests'] } suggestions.append(suggestion) # 按概率排序 suggestions.sort(key=lambda x: x['probability'], # reverse=True) return suggestions[:5] # 返回前5个最可能的诊断 def generate_treatment_suggestions(self, # diagnosis_suggestions: List[Dict], patient_history: Dict) -> List[Dict]: """生成治疗建议""" treatments = [] for diagnosis in diagnosis_suggestions: if diagnosis['probability'] > 0.3: # 只对高概率诊断提供治疗建议 treatment = self.get_treatment_plan( diagnosis['disease_code'], # patient_history ) if treatment: treatments.append({ 'disease_name': diagnosis['disease_name'], 'treatment_plan': treatment, 'medications': self.recommend_medications( diagnosis['disease_code'], patient_history ), 'lifestyle_changes': self.recommend_lifestyle_changes( diagnosis['disease_code'] ), 'follow_up_schedule': self.suggest_follow_up_schedule( diagnosis['disease_code'] ) }) return treatments def generate_health_recommendations(self, diagnosis_suggestions: List[Dict], patient_history: Dict) -> List[str]: """生成健康建议""" recommendations = [] # 基于诊断生成建议 for diagnosis in # diagnosis_suggestions: if diagnosis['probability'] > 0.2: disease_recommendations = self.get_disease_specific_recommendations( diagnosis['disease_code'] ) recommendations.extend(disease_recommendations) # 基于患者历史生成建议 if patient_history: # history_recommendations = self.get_history_based_recommendations( patient_history ) recommendations.extend(history_recommendations) # 通用健康建议 general_recommendations = # self.get_general_health_recommendations() recommendations.extend(general_recommendations) return list(set(recommendations)) # 去重 def generate_follow_up_advice(self, diagnosis_suggestions: List[Dict]) # -> Dict: """生成随访建议""" follow_up = { 'immediate_actions': [], 'short_term_monitoring': [], 'long_term_follow_up': [], 'warning_signs': [], 'when_to_seek_help': [] } for diagnosis in diagnosis_suggestions: if diagnosis['probability'] > 0.3: disease_follow_up = self.get_disease_follow_up_plan( diagnosis['disease_code'] ) if disease_follow_up: follow_up['immediate_actions'].extend( disease_follow_up.get('immediate_actions', []) ) follow_up['short_term_monitoring'].extend( disease_follow_up.get('short_term_monitoring', []) ) follow_up['long_term_follow_up'].extend( disease_follow_up.get('long_term_follow_up', []) ) follow_up['warning_signs'].extend( disease_follow_up.get('warning_signs', []) ) follow_up['when_to_seek_help'].extend( disease_follow_up.get('when_to_seek_help', []) ) return follow_up def record_diagnosis(self, patient_id: str, symptoms: List[str], diagnosis_suggestions: List[Dict]): """记录诊断历史""" diagnosis_record = { 'patient_id': patient_id, 'timestamp': datetime.now().isoformat(), 'symptoms': symptoms, 'diagnosis_suggestions': diagnosis_suggestions } self.diagnosis_history.append(diagnosis_record) # 更新患者记录 if patient_id not in # self.patient_records: self.patient_records[patient_id] = { 'diagnosis_history': [], 'treatment_history': [], 'health_metrics': {} } self.patient_records[patient_id]['diagnosis_history'].append(diagnosis_record) def standardize_symptom(self, symptom: str) -> str: """症状标准化""" # 实现症状标准化逻辑 pass def # classify_symptom(self, symptom: str) -> str: """症状分类""" # 实现症状分类逻辑 pass def # assess_symptom_severity(self, symptom: str) -> int: """评估症状严重程度""" # 实现症状严重程度评估逻辑 # pass def identify_symptom_pattern(self, symptom: Dict) -> str: """识别症状模式""" # 实现症状模式识别逻辑 # pass def identify_risk_factors(self, symptom: Dict) -> List[str]: """识别风险因素""" # 实现风险因素识别逻辑 # pass def assess_urgency_level(self, analysis: Dict) -> str: """评估紧急程度""" # 实现紧急程度评估逻辑 # pass def match_symptoms_to_diseases(self, symptom_analysis: Dict) -> List[Dict]: """匹配症状到疾病""" # 实现症状疾病匹配逻辑 pass def calculate_diagnosis_probability(self, disease: Dict, symptom_analysis: # Dict, patient_history: Dict) -> float: """计算诊断概率""" # 实现诊断概率计算逻辑 pass def # assess_confidence_level(self, probability: float) -> str: """评估置信度""" if probability >= 0.8: return 'high' elif probability >= 0.5: return 'medium' else: return 'low' def get_treatment_plan(self, disease_code: str, patient_history: Dict) -> Dict: """获取治疗计划""" # 实现治疗计划获取逻辑 pass def recommend_medications(self, disease_code: str, patient_history: Dict) -> # List[Dict]: """推荐药物""" # 实现药物推荐逻辑 pass def recommend_lifestyle_changes(self, disease_code: # str) -> List[str]: """推荐生活方式改变""" # 实现生活方式建议逻辑 pass def # suggest_follow_up_schedule(self, disease_code: str) -> Dict: """建议随访计划""" # 实现随访计划建议逻辑 # pass def get_disease_specific_recommendations(self, disease_code: str) -> List[str]: """获取疾病特定建议""" # 实现疾病特定建议逻辑 pass def get_history_based_recommendations(self, patient_history: Dict) -> # List[str]: """获取基于历史的建议""" # 实现基于历史的建议逻辑 pass def # get_general_health_recommendations(self) -> List[str]: """获取通用健康建议""" return [ "保持规律的作息时间", "均衡饮食，多吃蔬菜水果", "适量运动，每周至少150分钟中等强度运动", "保持充足睡眠，每晚7-9小时", "定期体检，预防疾病" ] def get_disease_follow_up_plan(self, disease_code: str) -> Dict: """获取疾病随访计划""" # 实现疾病随访计划逻辑 pass class SymptomAnalysisModel(nn.Module): def __init__(self, vocab_size=10000, # hidden_size=256, num_classes=100): super().__init__() self.embedding = nn.Embedding(vocab_size, hidden_size) self.lstm = nn.LSTM(hidden_size, hidden_size, batch_first=True) self.classifier = nn.Linear(hidden_size, num_classes) def forward(self, x): embedded = self.embedding(x) lstm_out, _ = self.lstm(embedded) output = self.classifier(lstm_out[:, -1, :]) return output # 使用示例 if __name__ == # "__main__": # 初始化医疗助手 assistant = IntelligentMedicalAssistant( # medical_kb_path="medical_knowledge_base.json", symptom_model_path="symptom_model.pth" ) # 分析患者症状 # patient_id = "patient_001" symptoms = ["头痛", "发热", "咳嗽", "乏力"] additional_info = { "age": 35, "gender": "male", "duration": "3天" } result = assistant.analyze_symptoms(patient_id, symptoms, additional_info) print("症状分析:", result['symptom_analysis']) print("诊断建议:", result['diagnosis_suggestions']) print("治疗建议:", result['treatment_suggestions']) print("健康建议:", result['health_recommendations'])