Akiba So
e95e2f1338
Ship a new app version with broader analytics, restructured dashboards, and a server-rendered risk map. Frontend: - Add Overview, Demographic, Disease, and Environmental Health analysis pages - Add AnomalyMarkers, CalendarHeatmap, and MetricHeatmapTable components - Rebuild Alerts map onto server-rendered raster risk tiles; expand Monitoring, Trend, and District Comparison views - Extend API client, stores, and TypeScript types Backend: - Add environment router (pollutants, lag correlations) - Add risk_raster util serving XYZ 100m risk tiles - Expand cases endpoints (demographics, seasonality, diagnoses) and insights; harden auth and file-based loaders Data & tooling: - Add processed outpatient/inpatient/combined case parquet (LFS) - Add nested CLAUDE.md guides, pyrightconfig, and test updates
660 lines
21 KiB
Python
660 lines
21 KiB
Python
"""
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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 asyncio
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import pandas as pd
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import json
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from data.case_loader import load_data, get_combined_data, get_outpatient_data, get_inpatient_data, get_diagnoses, WUHAN_DISTRICTS, DATE_PATTERN
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router = APIRouter(prefix="/api/cases", tags=["cases"])
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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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diagnosis: Optional[str] = Query(None, description="Filter to single disease stats"),
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):
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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 = get_outpatient_data()
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df_in = get_inpatient_data()
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# 诊断过滤
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if diagnosis:
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df_out = df_out[df_out['初诊'].str.contains(diagnosis, na=False, case=False)]
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df_in = df_in[df_in['诊断名称'].str.contains(diagnosis, na=False, case=False)]
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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（过滤后可能为空，需防御 NaT）
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all_dates = pd.concat([df_out['date'], df_in['date']]).dropna()
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date_start = all_dates.min().strftime("%Y-%m-%d") if len(all_dates) else ""
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date_end = all_dates.max().strftime("%Y-%m-%d") if len(all_dates) else ""
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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": date_start,
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"end": date_end
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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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diagnosis: Optional[str] = Query(None, description="Filter by diagnosis name"),
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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().copy()
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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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# 诊断过滤
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if diagnosis:
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df = df[df['diagnosis'].str.contains(diagnosis, na=False, case=False)]
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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=str(p).split(' ')[0] if hasattr(p, 'strftime') else str(p)[:10],
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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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def _compute_cases_districts(
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case_type: Optional[str],
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min_count: int,
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diagnosis: Optional[str],
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start_date: Optional[str] = None,
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end_date: Optional[str] = None,
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) -> DistrictsResponse:
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"""Run the full pandas aggregation pipeline (called in thread pool)."""
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df = get_combined_data()
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# 诊断过滤
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if diagnosis:
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df = df[df['diagnosis'].str.contains(diagnosis, na=False, case=False)]
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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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# 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=str(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(float(row['outpatient']) / float(row['total']) * 100, 2) if row['total'] > 0 else 0,
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inpatient_ratio=round(float(row['inpatient']) / float(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("/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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diagnosis: Optional[str] = Query(None, description="Filter by diagnosis name"),
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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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):
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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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- 支持日期范围过滤
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- 返回各区门诊、住院量及占比
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Pandas processing runs in a thread pool to avoid blocking the async event loop.
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"""
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return await asyncio.to_thread(
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_compute_cases_districts, case_type, min_count, diagnosis, start_date, end_date
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)
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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=int(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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class DiagnosesResponse(BaseModel):
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"""诊断列表响应"""
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diagnoses: list[str]
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@router.get("/diagnoses", response_model=DiagnosesResponse, summary="获取所有诊断名称列表")
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async def get_diagnoses_list():
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"""Returns deduplicated, sorted list of unique diagnosis names (cached, fast)."""
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diagnoses = get_diagnoses()
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return DiagnosesResponse(diagnoses=diagnoses)
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# ============== Seasonal & Distribution Endpoints ==============
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class SeasonalPoint(BaseModel):
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"""月度聚合数据点"""
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month: int # 1-12
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month_label: str # "1月", "2月", ...
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outpatient: int
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inpatient: int
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total: int
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class SeasonalResponse(BaseModel):
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"""月度季节性响应"""
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monthly: list[SeasonalPoint]
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period_years: list[int] # e.g. [2022, 2023, 2024]
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total_cases: int
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class DiagnosisDistributionItem(BaseModel):
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"""诊断分布数据项"""
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diagnosis: str
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outpatient: int
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inpatient: int
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total: int
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percentage: float
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class DiagnosisDistributionResponse(BaseModel):
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"""诊断分布响应"""
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diagnoses: list[DiagnosisDistributionItem]
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total_cases: int
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# ============== Demographics Models ==============
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class AgeBin(BaseModel):
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"""年龄分段数据"""
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age_bin: int # 0-17
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outpatient: int
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inpatient: int
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class GenderSplit(BaseModel):
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"""性别拆分数据"""
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outpatient: int
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inpatient: int
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class GenderSplitData(BaseModel):
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"""性别分布响应内层"""
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male: GenderSplit
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female: GenderSplit
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class AgeDiagnosisMatrixItem(BaseModel):
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"""年龄-诊断矩阵项"""
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age_group: str # "0-1", "1-3", "3-6", "6-12", "12-18"
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diagnosis: str
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outpatient: int
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inpatient: int
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total: int
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class DemographicsResponse(BaseModel):
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"""人口统计响应"""
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age_distribution: list[AgeBin]
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gender_split: GenderSplitData
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age_diagnosis_matrix: list[AgeDiagnosisMatrixItem]
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# ============== Disease Seasonality Models ==============
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class DiseaseSeasonalityPoint(BaseModel):
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"""疾病月度季节性数据点"""
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diagnosis: str
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month: int # 1-12
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month_label: str # "1月"-"12月"
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outpatient: int
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inpatient: int
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total: int
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class DiseaseDistrictItem(BaseModel):
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"""单个诊断的区域分布（按病例数排序的前若干区）"""
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diagnosis: str
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district: str
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total: int
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class DiseaseSeasonalityResponse(BaseModel):
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"""疾病季节性响应"""
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seasonality: list[DiseaseSeasonalityPoint]
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diagnoses: list[str]
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# 每个诊断的真实区域分布（按区聚合），使前端可为每个诊断显示其各自的"主要区域"
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diagnosis_districts: list[DiseaseDistrictItem]
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@router.get("/seasonal", response_model=SeasonalResponse, summary="获取季节性月度聚合数据")
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async def get_cases_seasonal(
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diagnosis: Optional[str] = Query(None, description="Filter by diagnosis name"),
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):
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"""
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按月聚合所有年份的病例数据
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- 返回 1-12 月各月门诊/住院/总计均值
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- 支持诊断过滤
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- 用于季节性分解图表
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"""
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df = get_combined_data()
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if diagnosis:
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df = df[df['diagnosis'].str.contains(diagnosis, na=False, case=False)]
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# Extract month and aggregate
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df = df.copy()
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df['month'] = df['date'].dt.month
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years = sorted(df['date'].dt.year.unique().tolist())
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out_monthly = df[df['type'] == 'outpatient'].groupby('month').size()
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in_monthly = df[df['type'] == 'inpatient'].groupby('month').size()
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month_labels = ['1月', '2月', '3月', '4月', '5月', '6月',
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'7月', '8月', '9月', '10月', '11月', '12月']
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monthly = []
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total_cases = 0
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for m in range(1, 13):
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out_count = int(out_monthly.get(m, 0))
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in_count = int(in_monthly.get(m, 0))
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total_cases += out_count + in_count
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monthly.append(SeasonalPoint(
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month=m,
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month_label=month_labels[m - 1],
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outpatient=out_count,
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inpatient=in_count,
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||
total=out_count + in_count,
|
||
))
|
||
|
||
return SeasonalResponse(
|
||
monthly=monthly,
|
||
period_years=years,
|
||
total_cases=total_cases,
|
||
)
|
||
|
||
|
||
@router.get("/diagnosis-distribution", response_model=DiagnosisDistributionResponse, summary="获取诊断分布统计")
|
||
async def get_diagnosis_distribution(
|
||
limit: int = Query(default=20, ge=1, le=50, description="Maximum diagnoses to return"),
|
||
):
|
||
"""
|
||
获取诊断名称分布统计（门诊+住院分列）
|
||
|
||
- 返回前 N 个诊断及门诊/住院/总计/占比
|
||
- 用于诊断分布饼图、树图等
|
||
"""
|
||
df = get_combined_data()
|
||
|
||
# Compute O/I counts per diagnosis
|
||
breakdown = df.groupby(['diagnosis', 'type']).size().unstack(fill_value=0)
|
||
if 'outpatient' not in breakdown.columns:
|
||
breakdown['outpatient'] = 0
|
||
if 'inpatient' not in breakdown.columns:
|
||
breakdown['inpatient'] = 0
|
||
breakdown['total'] = breakdown['outpatient'] + breakdown['inpatient']
|
||
breakdown = breakdown.sort_values('total', ascending=False).head(limit)
|
||
|
||
grand_total = int(breakdown['total'].sum())
|
||
|
||
diagnoses = []
|
||
for diagnosis_name, row in breakdown.iterrows():
|
||
diagnoses.append(DiagnosisDistributionItem(
|
||
diagnosis=str(diagnosis_name),
|
||
outpatient=int(row['outpatient']),
|
||
inpatient=int(row['inpatient']),
|
||
total=int(row['total']),
|
||
percentage=round(float(row['total']) / float(grand_total) * 100, 2) if grand_total > 0 else 0,
|
||
))
|
||
|
||
return DiagnosisDistributionResponse(
|
||
diagnoses=diagnoses,
|
||
total_cases=grand_total,
|
||
)
|
||
|
||
|
||
# ============== Demographics Endpoint ==============
|
||
|
||
@router.get("/demographics", response_model=DemographicsResponse, summary="获取人口统计信息")
|
||
async def get_cases_demographics():
|
||
"""
|
||
获取病例人口统计信息
|
||
|
||
- 年龄分布（0-17岁，按1岁分段，仅住院数据）
|
||
- 性别分布（仅住院数据）
|
||
- 年龄-诊断矩阵（按年龄段分组，仅住院数据）
|
||
|
||
注意：门诊数据不包含人口统计信息（性别/年龄），因此门诊计数均为 0。
|
||
"""
|
||
df = get_inpatient_data()
|
||
df = df.copy()
|
||
df['age_bin'] = df['年龄'].clip(0, 17).astype(int)
|
||
|
||
# --- Age distribution: 1-year bins from 0 to 17 ---
|
||
age_counts = df.groupby('age_bin').size()
|
||
age_distribution = [
|
||
AgeBin(age_bin=a, outpatient=0, inpatient=int(age_counts.get(a, 0)))
|
||
for a in range(0, 18)
|
||
]
|
||
|
||
# --- Gender split ---
|
||
gender_counts = df['性别'].value_counts()
|
||
gender_split = GenderSplitData(
|
||
male=GenderSplit(outpatient=0, inpatient=int(gender_counts.get('男性', 0))),
|
||
female=GenderSplit(outpatient=0, inpatient=int(gender_counts.get('女性', 0))),
|
||
)
|
||
|
||
# --- Age-diagnosis matrix ---
|
||
age_bins = [
|
||
(0, 1, "0-1"), (1, 3, "1-3"), (3, 6, "3-6"),
|
||
(6, 12, "6-12"), (12, 18, "12-18"),
|
||
]
|
||
|
||
matrix_rows: list[AgeDiagnosisMatrixItem] = []
|
||
for low, high, label in age_bins:
|
||
group = df[(df['年龄'] >= low) & (df['年龄'] < high)]
|
||
for diag, count in group['诊断名称'].value_counts().items():
|
||
matrix_rows.append(AgeDiagnosisMatrixItem(
|
||
age_group=label, diagnosis=str(diag),
|
||
outpatient=0, inpatient=int(count), total=int(count),
|
||
))
|
||
|
||
return DemographicsResponse(
|
||
age_distribution=age_distribution,
|
||
gender_split=gender_split,
|
||
age_diagnosis_matrix=matrix_rows,
|
||
)
|
||
|
||
|
||
# ============== Disease Seasonality Endpoint ==============
|
||
|
||
@router.get("/disease-seasonality", response_model=DiseaseSeasonalityResponse, summary="获取疾病季节性数据")
|
||
async def get_disease_seasonality(
|
||
diagnosis: Optional[str] = Query(None, description="Filter by diagnosis name"),
|
||
):
|
||
"""
|
||
获取各诊断的月度季节性分布数据
|
||
|
||
- 基于门诊+住院合并数据
|
||
- 按月聚合所有年份，返回 top 10 诊断的月度分布
|
||
- 支持可选诊断过滤
|
||
- 用于疾病季节性热力图、雷达图等
|
||
"""
|
||
df = get_combined_data()
|
||
|
||
if diagnosis:
|
||
df = df[df['diagnosis'].str.contains(diagnosis, na=False, case=False)]
|
||
|
||
# Extract month
|
||
df = df.copy()
|
||
df['month'] = df['date'].dt.month
|
||
|
||
# Get top 10 diagnoses by total case count
|
||
diag_totals = df.groupby('diagnosis').size().nlargest(10)
|
||
top_diagnoses = diag_totals.index.tolist()
|
||
|
||
month_labels = ['1月', '2月', '3月', '4月', '5月', '6月',
|
||
'7月', '8月', '9月', '10月', '11月', '12月']
|
||
|
||
# Filter to top diagnoses
|
||
df_top = df[df['diagnosis'].isin(top_diagnoses)]
|
||
|
||
# Group by diagnosis + month
|
||
breakdown = df_top.groupby(['diagnosis', 'month', 'type']).size().unstack(fill_value=0)
|
||
if 'outpatient' not in breakdown.columns:
|
||
breakdown['outpatient'] = 0
|
||
if 'inpatient' not in breakdown.columns:
|
||
breakdown['inpatient'] = 0
|
||
|
||
seasonality: list[DiseaseSeasonalityPoint] = []
|
||
for diag in top_diagnoses:
|
||
for m in range(1, 13):
|
||
row = breakdown.loc[(diag, m)] if (diag, m) in breakdown.index else None
|
||
out_count = int(row['outpatient']) if row is not None else 0
|
||
in_count = int(row['inpatient']) if row is not None else 0
|
||
seasonality.append(DiseaseSeasonalityPoint(
|
||
diagnosis=str(diag),
|
||
month=m,
|
||
month_label=month_labels[m - 1],
|
||
outpatient=out_count,
|
||
inpatient=in_count,
|
||
total=out_count + in_count,
|
||
))
|
||
|
||
# Per-diagnosis district distribution (real aggregation by diagnosis × district).
|
||
# Previously the frontend showed the same "主要区域" for every diagnosis because
|
||
# no per-diagnosis district data was exposed. Top 3 districts per diagnosis.
|
||
df_districts = df_top[(df_top['district'] != '未知') & (df_top['district'] != '其他')]
|
||
diag_district_counts = df_districts.groupby(['diagnosis', 'district']).size()
|
||
diagnosis_districts: list[DiseaseDistrictItem] = []
|
||
for diag in top_diagnoses:
|
||
if diag not in diag_district_counts.index.get_level_values('diagnosis'):
|
||
continue
|
||
top_d = diag_district_counts.loc[diag].sort_values(ascending=False).head(3)
|
||
for district_name, count in top_d.items():
|
||
diagnosis_districts.append(DiseaseDistrictItem(
|
||
diagnosis=str(diag),
|
||
district=str(district_name),
|
||
total=int(count),
|
||
))
|
||
|
||
return DiseaseSeasonalityResponse(
|
||
seasonality=seasonality,
|
||
diagnoses=[str(d) for d in top_diagnoses],
|
||
diagnosis_districts=diagnosis_districts,
|
||
)
|