Akiba So
fe8bed58f5
Per request — drop all monetary statistics (住院费用 is sensitive). - Backend statistics.py: remove mean_cost KPI + cost_histogram / cost_by_disease / cost_vs_los from /inpatient-clinical (models, computation, response) - Frontend: drop 人均费用 KPI card (now 4 KPIs), 住院费用分布, 各病种平均费用, 费用×住院天数散点; delete CostByDiseaseChart + CostVsLosScatter components; trim statsApi type + e2e fixture + chartColors Clinical page now: KPI(总人次/中位住院日/治愈好转率/急诊占比) + LOS dist + LOS-by-disease box + outcome donut + admission-route donut + age-band BMI box. Gates: backend 106 pytest · tsc 0 · build ok · clinical+user-flows e2e 19/19 · live endpoint confirmed cost-free Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
567 lines
18 KiB
Python
567 lines
18 KiB
Python
"""
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统计分析 API 路由 (prefix /api/stats)
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为前端统计仪表盘提供聚合后的临床、症状、发病率、环境相关性和时序数据。
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所有数据从 processed/*.parquet 计算得出（文件型后端，无数据库）。
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设计原则：
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- 模块级缓存载入的 parquet（与其他路由一致）。
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- 仅返回聚合结果，绝不直接 dump 原始行，保持 payload 小。
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- 每个端点用 try/except 包裹，失败时返回合法的空结构（绝不让 UI 收到 500）。
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- pandas 计算放入线程池 (asyncio.to_thread)，避免阻塞事件循环。
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"""
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import asyncio
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import glob
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import logging
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import threading
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from pathlib import Path
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from typing import Optional, cast
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import numpy as np
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import pandas as pd
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from fastapi import APIRouter, Query
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from pydantic import BaseModel
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from data.case_loader import (
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get_inpatient_data,
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get_outpatient_data,
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get_combined_data,
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load_cases_by_district_daily,
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normalize_district,
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)
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logger = logging.getLogger("cbpoa.statistics")
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router = APIRouter(prefix="/api/stats", tags=["statistics"])
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PROJECT_ROOT = Path(__file__).parent.parent.parent
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PROCESSED_DIR = PROJECT_ROOT / "processed"
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# ============== Module-level caches ==============
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_cache: dict[str, object] = {}
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_cache_lock = threading.RLock()
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# 7 个污染物（与 feature snapshot 列名一致）
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POLLUTANTS = ["AQI", "PM25", "PM10", "SO2", "NO2", "O3", "CO"]
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# 主诉症状关键词（固定列表，子串匹配）。注意顺序：更具体的在前避免被宽泛词吞掉，
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# 但因为是独立子串计数，顺序不影响结果，仅为可读性分组。
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SYMPTOM_KEYWORDS = [
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"发热", "咳嗽", "咳", "喘息", "喘", "流涕", "鼻塞", "咽痛", "咽喉",
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"痰", "气促", "呼吸困难", "肺炎", "复诊", "随诊", "复查",
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"腹泻", "呕吐", "头痛", "乏力", "胸闷", "鼻涕", "发烧", "感冒",
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]
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REVISIT_KEYWORDS = ["复诊", "随诊", "复查"]
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def _district_population() -> pd.Series:
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"""各区人口（population_density 求和，按 grid_district_mapping 归属）。
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返回 index 为规范化区名(13)、值为人口的 Series。结果缓存。
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"""
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cached = _cache.get("district_population")
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if cached is not None:
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return cast(pd.Series, cached)
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with _cache_lock:
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cached = _cache.get("district_population")
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if cached is not None:
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return cast(pd.Series, cached)
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mapping = pd.read_parquet(PROCESSED_DIR / "grid_district_mapping.parquet")
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grid = pd.read_parquet(
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PROCESSED_DIR / "grid_100m_with_dem_pop.parquet",
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columns=["grid_id", "population_density"],
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)
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joined = mapping.merge(grid, on="grid_id", how="inner")
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joined = joined.dropna(subset=["district_name"])
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joined["district_name"] = joined["district_name"].map(normalize_district)
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pop = joined.groupby("district_name")["population_density"].sum()
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_cache["district_population"] = pop
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return pop
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def _feature_snapshots() -> pd.DataFrame:
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"""合并所有可用的 features_*.parquet 快照（缓存）。
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用于污染物 vs 病例的相关性分析。每个快照按格点给出污染物 + 病例计数 + 区。
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"""
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cached = _cache.get("features")
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if cached is not None:
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return cast(pd.DataFrame, cached)
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with _cache_lock:
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cached = _cache.get("features")
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if cached is not None:
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return cast(pd.DataFrame, cached)
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paths = sorted(glob.glob(str(PROCESSED_DIR / "features_*.parquet")))
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if not paths:
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df = pd.DataFrame(
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columns=POLLUTANTS + ["outpatient_count", "inpatient_count", "total_cases", "district"]
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)
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else:
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frames = [pd.read_parquet(p) for p in paths]
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df = pd.concat(frames, ignore_index=True)
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_cache["features"] = df
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return df
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# ============== Response Models ==============
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class KeyValueCount(BaseModel):
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bin_label: str
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count: int
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class InpatientKpis(BaseModel):
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total_admissions: int
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median_los_days: float
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cure_rate: float
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emergency_admit_ratio: float
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class LosByDisease(BaseModel):
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diagnosis: str
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p25: float
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median: float
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p75: float
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n: int
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class LabelCount(BaseModel):
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outcome: Optional[str] = None
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route: Optional[str] = None
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count: int
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class OutcomeCount(BaseModel):
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outcome: str
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count: int
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class RouteCount(BaseModel):
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route: str
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count: int
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class BmiByAge(BaseModel):
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age_band: str
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p25: float
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median: float
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p75: float
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n: int
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class InpatientClinicalResponse(BaseModel):
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kpis: InpatientKpis
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los_histogram: list[KeyValueCount]
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los_by_disease: list[LosByDisease]
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outcome_counts: list[OutcomeCount]
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admission_route_counts: list[RouteCount]
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bmi_by_age_band: list[BmiByAge]
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class SymptomItem(BaseModel):
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keyword: str
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count: int
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class SymptomsResponse(BaseModel):
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symptoms: list[SymptomItem]
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revisit_ratio: float
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class IncidenceItem(BaseModel):
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district: str
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total_cases: int
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population: float
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rate_per_10k: float
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class IncidenceResponse(BaseModel):
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districts: list[IncidenceItem]
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class CorrItem(BaseModel):
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pollutant: str
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corr_with_cases: float
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class ScatterPoint(BaseModel):
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pm25: float
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aqi: float
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cases: float
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class PairwiseCorr(BaseModel):
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a: str
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b: str
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corr: float
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class EnvCorrelationResponse(BaseModel):
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correlation_matrix: list[CorrItem]
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scatter: list[ScatterPoint]
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pollutant_pairwise: list[PairwiseCorr]
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class WeekdayPoint(BaseModel):
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weekday: str
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outpatient: int
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inpatient: int
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total: int
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class MonthYearPoint(BaseModel):
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year: int
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month: int
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total: int
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class YoYPoint(BaseModel):
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period: str
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current: int
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previous: int
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growth_pct: float
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class TemporalResponse(BaseModel):
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weekday: list[WeekdayPoint]
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month_year: list[MonthYearPoint]
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yoy: list[YoYPoint]
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# ============== Helpers ==============
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def _empty_inpatient_clinical() -> InpatientClinicalResponse:
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return InpatientClinicalResponse(
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kpis=InpatientKpis(
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total_admissions=0, median_los_days=0.0,
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cure_rate=0.0, emergency_admit_ratio=0.0,
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),
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los_histogram=[], los_by_disease=[], outcome_counts=[],
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admission_route_counts=[], bmi_by_age_band=[],
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)
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def _safe_float(v) -> float:
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try:
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f = float(v)
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if np.isnan(f) or np.isinf(f):
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return 0.0
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return round(f, 4)
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except (TypeError, ValueError):
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return 0.0
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# ============== Endpoint 1: inpatient clinical ==============
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def _compute_inpatient_clinical() -> InpatientClinicalResponse:
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df = get_inpatient_data().copy()
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if df.empty:
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return _empty_inpatient_clinical()
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# LOS = (出院日期 - 入院日期).days, valid 0-60
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in_date = pd.to_datetime(df["入院日期"], errors="coerce")
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out_date = pd.to_datetime(df["出院日期"], errors="coerce")
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df["los"] = (out_date - in_date).dt.days
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df_los = df[(df["los"] >= 0) & (df["los"] <= 60)]
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total = len(df)
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median_los = float(df_los["los"].median()) if len(df_los) else 0.0
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outcome = df["出院情况"].fillna("未知")
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cure_n = int(outcome.isin(["治愈", "好转"]).sum())
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cure_rate = cure_n / total if total else 0.0
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route = df["入院途径"].fillna("未知")
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emerg_n = int((route == "急诊").sum())
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emerg_ratio = emerg_n / total if total else 0.0
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kpis = InpatientKpis(
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total_admissions=total,
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median_los_days=round(median_los, 2),
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cure_rate=round(cure_rate, 4),
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emergency_admit_ratio=round(emerg_ratio, 4),
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)
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# LOS histogram bins: 0,1,2,3,4,5,6,7,8-14,15+
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los_histogram: list[KeyValueCount] = []
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los_vals = df_los["los"]
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for b in range(0, 8):
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los_histogram.append(KeyValueCount(bin_label=str(b), count=int((los_vals == b).sum())))
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los_histogram.append(KeyValueCount(bin_label="8-14", count=int(((los_vals >= 8) & (los_vals <= 14)).sum())))
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los_histogram.append(KeyValueCount(bin_label="15+", count=int((los_vals >= 15).sum())))
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# LOS by disease (top 8 diagnoses by n)
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los_by_disease: list[LosByDisease] = []
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if len(df_los):
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top_diag = df_los["诊断名称"].value_counts().head(8).index.tolist()
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for d in top_diag:
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grp = df_los[df_los["诊断名称"] == d]["los"]
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los_by_disease.append(LosByDisease(
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diagnosis=str(d),
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p25=round(float(grp.quantile(0.25)), 2),
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median=round(float(grp.median()), 2),
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p75=round(float(grp.quantile(0.75)), 2),
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n=int(len(grp)),
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))
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# outcome counts
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outcome_counts = [
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OutcomeCount(outcome=str(k), count=int(v))
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for k, v in outcome.value_counts().items()
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]
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# admission route counts
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admission_route_counts = [
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RouteCount(route=str(k), count=int(v))
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for k, v in route.value_counts().items()
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]
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# BMI by age band. BMI = 体重kg / (身高m)^2; plausible 8-40.
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bmi_by_age_band: list[BmiByAge] = []
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h = pd.to_numeric(df["身高"], errors="coerce") # cm
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w = pd.to_numeric(df["体重"], errors="coerce") # kg
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age = pd.to_numeric(df["年龄"], errors="coerce")
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bmi = w / ((h / 100.0) ** 2)
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bmi_df = pd.DataFrame({"age": age, "bmi": bmi})
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bmi_df = bmi_df[(bmi_df["bmi"] >= 8) & (bmi_df["bmi"] <= 40) & bmi_df["age"].notna()]
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age_bands = [(0, 3, "0-2"), (3, 6, "3-5"), (6, 9, "6-8"),
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(9, 12, "9-11"), (12, 15, "12-14"), (15, 19, "15-18")]
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for lo, hi, label in age_bands:
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grp = bmi_df[(bmi_df["age"] >= lo) & (bmi_df["age"] < hi)]["bmi"]
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if len(grp) == 0:
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continue
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bmi_by_age_band.append(BmiByAge(
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age_band=label,
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p25=round(float(grp.quantile(0.25)), 2),
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median=round(float(grp.median()), 2),
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p75=round(float(grp.quantile(0.75)), 2),
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n=int(len(grp)),
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))
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return InpatientClinicalResponse(
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kpis=kpis,
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los_histogram=los_histogram,
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los_by_disease=los_by_disease,
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outcome_counts=outcome_counts,
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admission_route_counts=admission_route_counts,
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bmi_by_age_band=bmi_by_age_band,
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)
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@router.get("/inpatient-clinical", response_model=InpatientClinicalResponse, summary="住院临床统计")
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async def inpatient_clinical():
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"""住院临床概览：KPI、住院天数(LOS)分布、费用分布、转归、入院途径、BMI 分布。"""
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try:
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return await asyncio.to_thread(_compute_inpatient_clinical)
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except Exception:
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logger.exception("inpatient-clinical failed")
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return _empty_inpatient_clinical()
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# ============== Endpoint 2: symptoms ==============
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def _compute_symptoms(top: int) -> SymptomsResponse:
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df = get_outpatient_data()
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if df.empty or "主诉" not in df.columns:
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return SymptomsResponse(symptoms=[], revisit_ratio=0.0)
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chief = df["主诉"].dropna().astype(str)
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total = len(chief)
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if total == 0:
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return SymptomsResponse(symptoms=[], revisit_ratio=0.0)
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counts: list[SymptomItem] = []
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for kw in SYMPTOM_KEYWORDS:
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c = int(chief.str.contains(kw, regex=False).sum())
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if c > 0:
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counts.append(SymptomItem(keyword=kw, count=c))
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counts.sort(key=lambda x: x.count, reverse=True)
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counts = counts[:top]
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revisit_mask = chief.str.contains("|".join(REVISIT_KEYWORDS), regex=True)
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revisit_ratio = float(revisit_mask.sum()) / total if total else 0.0
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return SymptomsResponse(symptoms=counts, revisit_ratio=round(revisit_ratio, 4))
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@router.get("/symptoms", response_model=SymptomsResponse, summary="门诊主诉症状词频")
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async def symptoms(top: int = Query(20, ge=1, le=50, description="返回前 N 个症状词")):
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"""从门诊主诉中提取固定症状关键词的出现频次，并计算复诊比例。"""
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try:
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return await asyncio.to_thread(_compute_symptoms, top)
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except Exception:
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logger.exception("symptoms failed")
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return SymptomsResponse(symptoms=[], revisit_ratio=0.0)
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# ============== Endpoint 3: incidence rate ==============
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def _compute_incidence() -> IncidenceResponse:
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daily = load_cases_by_district_daily()
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if daily.empty:
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return IncidenceResponse(districts=[])
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case_totals = daily.groupby("district")["total_cases"].sum()
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pop = _district_population()
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items: list[IncidenceItem] = []
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for district in case_totals.index:
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total_cases = int(case_totals.get(district, 0))
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population = float(pop.get(district, 0.0))
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rate = (total_cases / population * 10000) if population > 0 else 0.0
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items.append(IncidenceItem(
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district=str(district),
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total_cases=total_cases,
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population=round(population, 1),
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rate_per_10k=round(rate, 2),
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))
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items.sort(key=lambda x: x.rate_per_10k, reverse=True)
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return IncidenceResponse(districts=items)
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@router.get("/incidence-rate", response_model=IncidenceResponse, summary="各区发病率")
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async def incidence_rate():
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"""各区病例总数 / 区人口 * 10000，得到每万人发病率（13 区）。"""
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try:
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return await asyncio.to_thread(_compute_incidence)
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except Exception:
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logger.exception("incidence-rate failed")
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return IncidenceResponse(districts=[])
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# ============== Endpoint 4: env correlation ==============
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def _compute_env_correlation() -> EnvCorrelationResponse:
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df = _feature_snapshots()
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if df.empty or "total_cases" not in df.columns:
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return EnvCorrelationResponse(correlation_matrix=[], scatter=[], pollutant_pairwise=[])
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# 污染物 vs 病例 的 Pearson 相关（按格点，汇集所有快照）
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correlation_matrix: list[CorrItem] = []
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cases = pd.to_numeric(df["total_cases"], errors="coerce")
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for p in POLLUTANTS:
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if p not in df.columns:
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continue
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series = pd.to_numeric(df[p], errors="coerce")
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valid = series.notna() & cases.notna()
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if valid.sum() < 2 or series[valid].std() == 0 or cases[valid].std() == 0:
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corr = 0.0
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else:
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corr = float(series[valid].corr(cases[valid]))
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correlation_matrix.append(CorrItem(pollutant=p, corr_with_cases=_safe_float(corr)))
|
||
|
||
# scatter: 采样 cases>0 的格点（up to 500）
|
||
scatter: list[ScatterPoint] = []
|
||
has_cols = all(c in df.columns for c in ["PM25", "AQI", "total_cases"])
|
||
if has_cols:
|
||
sdf = df[["PM25", "AQI", "total_cases"]].copy()
|
||
sdf = sdf[pd.to_numeric(sdf["total_cases"], errors="coerce") > 0].dropna()
|
||
if len(sdf) > 500:
|
||
sdf = sdf.sample(n=500, random_state=42)
|
||
for _, r in sdf.iterrows():
|
||
scatter.append(ScatterPoint(
|
||
pm25=_safe_float(r["PM25"]),
|
||
aqi=_safe_float(r["AQI"]),
|
||
cases=_safe_float(r["total_cases"]),
|
||
))
|
||
|
||
# pollutant pairwise (upper triangle)
|
||
pollutant_pairwise: list[PairwiseCorr] = []
|
||
present = [p for p in POLLUTANTS if p in df.columns]
|
||
pol_df = df[present].apply(pd.to_numeric, errors="coerce")
|
||
corr_mat = pol_df.corr()
|
||
for i, a in enumerate(present):
|
||
for b in present[i + 1:]:
|
||
try:
|
||
v = corr_mat.loc[a, b]
|
||
except KeyError:
|
||
v = 0.0
|
||
pollutant_pairwise.append(PairwiseCorr(a=a, b=b, corr=_safe_float(v)))
|
||
|
||
return EnvCorrelationResponse(
|
||
correlation_matrix=correlation_matrix,
|
||
scatter=scatter,
|
||
pollutant_pairwise=pollutant_pairwise,
|
||
)
|
||
|
||
|
||
@router.get("/env-correlation", response_model=EnvCorrelationResponse, summary="环境-病例相关性")
|
||
async def env_correlation():
|
||
"""污染物与病例的相关矩阵、PM2.5/AQI 散点、污染物两两相关（热力图）。"""
|
||
try:
|
||
return await asyncio.to_thread(_compute_env_correlation)
|
||
except Exception:
|
||
logger.exception("env-correlation failed")
|
||
return EnvCorrelationResponse(correlation_matrix=[], scatter=[], pollutant_pairwise=[])
|
||
|
||
|
||
# ============== Endpoint 5: temporal ==============
|
||
|
||
_WEEKDAY_LABELS = ["周一", "周二", "周三", "周四", "周五", "周六", "周日"]
|
||
|
||
|
||
def _compute_temporal() -> TemporalResponse:
|
||
df = get_combined_data().copy()
|
||
if df.empty:
|
||
return TemporalResponse(weekday=[], month_year=[], yoy=[])
|
||
df["date"] = pd.to_datetime(df["date"], errors="coerce")
|
||
df = df[df["date"].notna()]
|
||
if df.empty:
|
||
return TemporalResponse(weekday=[], month_year=[], yoy=[])
|
||
|
||
# weekday (0=周一..6=周日)
|
||
df["wd"] = df["date"].dt.weekday
|
||
weekday: list[WeekdayPoint] = []
|
||
for wd in range(7):
|
||
sub = df[df["wd"] == wd]
|
||
out = int((sub["type"] == "outpatient").sum())
|
||
inp = int((sub["type"] == "inpatient").sum())
|
||
weekday.append(WeekdayPoint(
|
||
weekday=_WEEKDAY_LABELS[wd], outpatient=out, inpatient=inp, total=out + inp,
|
||
))
|
||
|
||
# month_year (seasonality grid)
|
||
df["year"] = df["date"].dt.year
|
||
df["month"] = df["date"].dt.month
|
||
my = df.groupby(["year", "month"]).size()
|
||
month_year = [
|
||
MonthYearPoint(year=int(y), month=int(m), total=int(c))
|
||
for (y, m), c in my.items()
|
||
]
|
||
month_year.sort(key=lambda x: (x.year, x.month))
|
||
|
||
# yoy: monthly current vs same-month-prior-year (only if multiple years exist)
|
||
yoy: list[YoYPoint] = []
|
||
years = sorted(df["year"].unique().tolist())
|
||
if len(years) > 1:
|
||
monthly_totals = {(int(y), int(m)): int(c) for (y, m), c in my.items()}
|
||
for (y, m), cur in sorted(monthly_totals.items()):
|
||
prev = monthly_totals.get((y - 1, m))
|
||
if prev is None:
|
||
continue
|
||
growth = ((cur - prev) / prev * 100) if prev else 0.0
|
||
yoy.append(YoYPoint(
|
||
period=f"{y}-{m:02d}",
|
||
current=cur,
|
||
previous=prev,
|
||
growth_pct=round(growth, 2),
|
||
))
|
||
|
||
return TemporalResponse(weekday=weekday, month_year=month_year, yoy=yoy)
|
||
|
||
|
||
@router.get("/temporal", response_model=TemporalResponse, summary="时序统计")
|
||
async def temporal():
|
||
"""按星期、年-月（季节性网格）聚合，以及同比(YoY)增长（若有多年数据）。"""
|
||
try:
|
||
return await asyncio.to_thread(_compute_temporal)
|
||
except Exception:
|
||
logger.exception("temporal failed")
|
||
return TemporalResponse(weekday=[], month_year=[], yoy=[])
|