feat: Initial CBPOA commit — 武汉儿童呼吸疾病风险评估系统
Context: Build a spatial risk assessment system correlating air quality data with children's respiratory disease incidence across Wuhan. Approach: FastAPI backend serving PostGIS spatial queries, React frontend with Deck.gl maps, and a PyTorch SpatialTemporalGCN pipeline for multi-day (1d/3d/7d) risk prediction. Changes: - backend/ — FastAPI API with auth (JWT), alerts, risk analysis, geocoded case data, grid statistics, and report endpoints - frontend/ — React dashboard with interactive risk maps, alert monitoring, district comparison charts, and timeline player - models/ — SpatialTemporalGCN model with trained weights and ONNX export for inference - scripts/ — ETL pipeline for weather + medical data, grid generation, feature engineering, training, and daily inference - deploy/ — Docker Compose configs for backend, frontend, and MLflow - docs/ — API docs, deployment guide, user guide, and code review Impact: Enables spatial risk visualization, alert monitoring, and ML-driven health risk forecasting for environmental health teams.
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370
backend/routers/cases.py
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370
backend/routers/cases.py
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"""
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医疗病例数据 API 路由
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提供门诊和住院数据的统计、趋势、区域分布等接口
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"""
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from fastapi import APIRouter, HTTPException, Query
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from pydantic import BaseModel
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from typing import Optional
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from datetime import datetime, date
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import pandas as pd
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import re
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from pathlib import Path
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import json
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DATE_PATTERN = re.compile(r"^\d{4}-\d{2}-\d{2}$")
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router = APIRouter(prefix="/api/cases", tags=["cases"])
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# 数据缓存
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_cache = {
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"outpatient": None,
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"inpatient": None,
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"loaded_at": None,
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}
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# 武汉市区映射
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WUHAN_DISTRICTS = {
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'江岸区': ['江岸'],
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'江汉区': ['江汉'],
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'武昌区': ['武昌'],
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'洪山区': ['洪山'],
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'汉阳区': ['汉阳'],
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'东西湖区': ['东西湖'],
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'黄陂区': ['黄陂'],
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'硚口区': ['硚口'],
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'江夏区': ['江夏'],
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'青山区': ['青山'],
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'新洲区': ['新洲'],
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'蔡甸区': ['蔡甸'],
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'东湖新技术开发区': ['东湖新技术开发区', '光谷'],
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'经开（汉南）区': ['经开', '汉南', '经济开发区'],
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'东湖生态旅游风景区': ['东湖生态旅游风景区']
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}
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PROJECT_ROOT = Path(__file__).parent.parent.parent
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DATA_DIR = PROJECT_ROOT / "Datas"
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def _extract_district(addr: str) -> str:
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"""从地址提取武汉市区名"""
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if pd.isna(addr):
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return '未知'
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addr = str(addr)
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for district, keywords in WUHAN_DISTRICTS.items():
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for kw in keywords:
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if kw in addr:
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return district
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return '其他'
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def _load_data():
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"""加载并缓存数据"""
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if _cache["loaded_at"] is not None:
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return
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try:
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# 加载门诊数据
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df_out = pd.read_excel(DATA_DIR / "view_门诊.xlsx")
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df_out['date'] = pd.to_datetime(df_out['门诊日期_re'])
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df_out['district'] = df_out['现住址区'].fillna('未知')
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_cache["outpatient"] = df_out
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# 加载住院数据
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df_in = pd.read_excel(DATA_DIR / "view_住院.xlsx")
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df_in['date'] = pd.to_datetime(df_in['入院日期_re'])
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df_in['district'] = df_in['现住址_脱敏'].apply(_extract_district)
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_cache["inpatient"] = df_in
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_cache["loaded_at"] = datetime.now()
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except Exception as e:
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raise RuntimeError(f"数据加载失败：{str(e)}")
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def _get_combined_data():
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"""获取合并的病例数据"""
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_load_data()
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df_out = _cache["outpatient"][['date', 'district', '初诊', '主诉']].copy()
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df_out['type'] = 'outpatient'
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df_out['diagnosis'] = df_out['初诊']
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df_in = _cache["inpatient"][['date', 'district', '诊断名称']].copy()
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df_in['type'] = 'inpatient'
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df_in['diagnosis'] = df_in['诊断名称']
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df_in['主诉'] = None
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return pd.concat([df_out, df_in], ignore_index=True)
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# ============== Response Models ==============
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class StatsResponse(BaseModel):
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"""统计数据响应"""
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total_outpatient: int
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total_inpatient: int
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date_range: dict
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top_districts: list
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top_diagnoses: list
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class TrendPoint(BaseModel):
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"""趋势数据点"""
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date: str
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outpatient: int
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inpatient: int
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total: int
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class TrendResponse(BaseModel):
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"""趋势数据响应"""
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trend: list[TrendPoint]
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summary: dict
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class DistrictData(BaseModel):
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"""区域数据"""
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district: str
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outpatient: int
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inpatient: int
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total: int
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outpatient_ratio: float
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inpatient_ratio: float
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class DistrictsResponse(BaseModel):
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"""区域分布响应"""
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districts: list[DistrictData]
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total: int
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class RealtimeData(BaseModel):
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"""实时数据"""
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today_outpatient: int
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today_inpatient: int
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today_total: int
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last_7d_avg: int
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change_ratio: float
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status: str
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# ============== API Endpoints ==============
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@router.get("/stats", response_model=StatsResponse, summary="获取病例统计数据")
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async def get_cases_stats():
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"""
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获取病例总体统计信息
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- 总门诊量、总住院量
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- 数据日期范围
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- 就诊量前 10 的区域
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- 最常见诊断前 10
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"""
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_load_data()
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df_out = _cache["outpatient"]
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df_in = _cache["inpatient"]
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# 计算统计
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total_outpatient = len(df_out)
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total_inpatient = len(df_in)
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# 日期范围
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min_date = min(df_out['date'].min(), df_in['date'].min())
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max_date = max(df_out['date'].max(), df_in['date'].max())
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# 区域统计
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out_districts = df_out[df_out['district'] != '未知']['district'].value_counts().head(10)
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in_districts = df_in[df_in['district'] != '其他']['district'].value_counts().head(10)
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combined_districts = pd.concat([out_districts, in_districts]).groupby(level=0).sum().nlargest(10)
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top_districts = [{"district": d, "count": int(c)} for d, c in combined_districts.items()]
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# 诊断统计
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out_diagnoses = df_out['初诊'].value_counts().head(10)
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in_diagnoses = df_in['诊断名称'].value_counts().head(10)
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top_diagnoses = [
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{"diagnosis": str(d), "outpatient": int(out_diagnoses.get(d, 0)), "inpatient": int(in_diagnoses.get(d, 0))}
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for d in set(list(out_diagnoses.index[:5]) + list(in_diagnoses.index[:5]))
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][:10]
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return StatsResponse(
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total_outpatient=total_outpatient,
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total_inpatient=total_inpatient,
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date_range={
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"start": min_date.strftime("%Y-%m-%d"),
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"end": max_date.strftime("%Y-%m-%d")
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},
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top_districts=top_districts,
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top_diagnoses=top_diagnoses
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)
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@router.get("/trend", response_model=TrendResponse, summary="获取病例趋势数据")
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async def get_cases_trend(
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start_date: Optional[str] = Query(None, description="开始日期 (YYYY-MM-DD)"),
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end_date: Optional[str] = Query(None, description="结束日期 (YYYY-MM-DD)"),
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group_by: str = Query("day", description="分组粒度：day, week, month"),
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):
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"""
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获取病例时间趋势数据
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- 支持按日、周、月分组
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- 可指定日期范围
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- 返回门诊、住院、总计趋势
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"""
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if start_date and not DATE_PATTERN.match(start_date):
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raise HTTPException(status_code=400, detail="Invalid start_date format. Use YYYY-MM-DD")
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if end_date and not DATE_PATTERN.match(end_date):
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raise HTTPException(status_code=400, detail="Invalid end_date format. Use YYYY-MM-DD")
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df = _get_combined_data()
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# 日期过滤
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if start_date:
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df = df[df['date'] >= pd.to_datetime(start_date)]
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if end_date:
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df = df[df['date'] <= pd.to_datetime(end_date)]
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# 分组
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if group_by == "week":
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df['period'] = df['date'].dt.to_period('W').dt.start_time
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elif group_by == "month":
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df['period'] = df['date'].dt.to_period('M').dt.start_time
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else:
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df['period'] = df['date'].dt.date
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# 聚合
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out_trend = df[df['type'] == 'outpatient'].groupby('period').size()
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in_trend = df[df['type'] == 'inpatient'].groupby('period').size()
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periods = sorted(set(out_trend.index.tolist() + in_trend.index.tolist()))
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trend = []
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total_out = total_in = 0
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for p in periods:
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out_count = int(out_trend.get(p, 0))
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in_count = int(in_trend.get(p, 0))
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total_out += out_count
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total_in += in_count
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trend.append(TrendPoint(
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date=pd.Timestamp(p).strftime("%Y-%m-%d"),
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outpatient=out_count,
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inpatient=in_count,
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total=out_count + in_count
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))
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return TrendResponse(
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trend=trend,
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summary={
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"total_outpatient": total_out,
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"total_inpatient": total_in,
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"period_count": len(periods),
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"avg_daily_outpatient": round(total_out / max(len(periods), 1), 2),
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"avg_daily_inpatient": round(total_in / max(len(periods), 1), 2),
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}
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)
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@router.get("/districts", response_model=DistrictsResponse, summary="获取区域分布数据")
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async def get_cases_districts(
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case_type: Optional[str] = Query(None, description="病例类型：outpatient, inpatient, all"),
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min_count: int = Query(10, description="最小病例数过滤"),
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):
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"""
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获取病例区域分布数据
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- 支持按病例类型筛选
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- 可设置最小病例数过滤
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- 返回各区门诊、住院量及占比
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"""
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df = _get_combined_data()
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# 类型过滤
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if case_type == "outpatient":
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df = df[df['type'] == 'outpatient']
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elif case_type == "inpatient":
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df = df[df['type'] == 'inpatient']
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# 过滤未知区域
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df = df[(df['district'] != '未知') & (df['district'] != '其他')]
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# 聚合
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district_stats = df.groupby(['district', 'type']).size().unstack(fill_value=0)
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if 'outpatient' not in district_stats.columns:
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district_stats['outpatient'] = 0
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if 'inpatient' not in district_stats.columns:
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district_stats['inpatient'] = 0
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district_stats['total'] = district_stats['outpatient'] + district_stats['inpatient']
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# 过滤
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district_stats = district_stats[district_stats['total'] >= min_count]
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district_stats = district_stats.sort_values('total', ascending=False)
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total = int(district_stats['total'].sum())
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districts = []
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for district, row in district_stats.iterrows():
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districts.append(DistrictData(
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district=district,
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outpatient=int(row['outpatient']),
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inpatient=int(row['inpatient']),
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total=int(row['total']),
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outpatient_ratio=round(row['outpatient'] / row['total'] * 100, 2) if row['total'] > 0 else 0,
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inpatient_ratio=round(row['inpatient'] / row['total'] * 100, 2) if row['total'] > 0 else 0
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))
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return DistrictsResponse(districts=districts, total=total)
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@router.get("/realtime", response_model=RealtimeData, summary="获取实时数据")
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async def get_cases_realtime():
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"""
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获取实时病例数据
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- 今日就诊量
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- 近 7 日平均值
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- 变化率
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- 状态评估 (正常/偏高/偏低)
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"""
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df = _get_combined_data()
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today = pd.Timestamp.today().normalize()
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last_7d = today - pd.Timedelta(days=7)
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# 今日数据
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today_data = df[df['date'] >= today]
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today_total = len(today_data)
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today_out = len(today_data[today_data['type'] == 'outpatient'])
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today_in = len(today_data[today_data['type'] == 'inpatient'])
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# 近 7 日平均
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last_7d_data = df[(df['date'] >= last_7d) & (df['date'] < today)]
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last_7d_avg = round(len(last_7d_data) / 7, 2) if len(last_7d_data) > 0 else 0
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# 变化率
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if last_7d_avg > 0:
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change_ratio = round((today_total - last_7d_avg) / last_7d_avg * 100, 2)
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else:
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change_ratio = 0.0
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# 状态评估
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if change_ratio > 20:
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status = "偏高"
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elif change_ratio < -20:
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status = "偏低"
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else:
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status = "正常"
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return RealtimeData(
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today_outpatient=today_out,
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today_inpatient=today_in,
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today_total=today_total,
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last_7d_avg=last_7d_avg,
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change_ratio=change_ratio,
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status=status
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)
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Block a user