diff --git a/backend/routers/cases.py b/backend/routers/cases.py index 1659d6e..2064161 100644 --- a/backend/routers/cases.py +++ b/backend/routers/cases.py @@ -8,10 +8,11 @@ from fastapi import APIRouter, HTTPException, Query from pydantic import BaseModel from typing import Optional from datetime import datetime, date +import asyncio import pandas as pd import json -from data.case_loader import load_data, get_combined_data, get_outpatient_data, get_inpatient_data, WUHAN_DISTRICTS, DATE_PATTERN +from data.case_loader import load_data, get_combined_data, get_outpatient_data, get_inpatient_data, get_diagnoses, WUHAN_DISTRICTS, DATE_PATTERN router = APIRouter(prefix="/api/cases", tags=["cases"]) @@ -90,31 +91,31 @@ async def get_cases_stats( if diagnosis: df_out = df_out[df_out['初诊'].str.contains(diagnosis, na=False, case=False)] df_in = df_in[df_in['诊断名称'].str.contains(diagnosis, na=False, case=False)] - + # 计算统计 total_outpatient = len(df_out) total_inpatient = len(df_in) - + # 日期范围 min_date = min(df_out['date'].min(), df_in['date'].min()) max_date = max(df_out['date'].max(), df_in['date'].max()) - + # 区域统计 out_districts = df_out[df_out['district'] != '未知']['district'].value_counts().head(10) in_districts = df_in[df_in['district'] != '其他']['district'].value_counts().head(10) - + combined_districts = pd.concat([out_districts, in_districts]).groupby(level=0).sum().nlargest(10) top_districts = [{"district": d, "count": int(c)} for d, c in combined_districts.items()] - + # 诊断统计 out_diagnoses = df_out['初诊'].value_counts().head(10) in_diagnoses = df_in['诊断名称'].value_counts().head(10) - + top_diagnoses = [ {"diagnosis": str(d), "outpatient": int(out_diagnoses.get(d, 0)), "inpatient": int(in_diagnoses.get(d, 0))} for d in set(list(out_diagnoses.index[:5]) + list(in_diagnoses.index[:5])) ][:10] - + return StatsResponse( total_outpatient=total_outpatient, total_inpatient=total_inpatient, @@ -157,7 +158,7 @@ async def get_cases_trend( # 诊断过滤 if diagnosis: df = df[df['diagnosis'].str.contains(diagnosis, na=False, case=False)] - + # 分组 if group_by == "week": df['period'] = df['date'].dt.to_period('W').dt.start_time @@ -165,13 +166,13 @@ async def get_cases_trend( df['period'] = df['date'].dt.to_period('M').dt.start_time else: df['period'] = df['date'].dt.date - + # 聚合 out_trend = df[df['type'] == 'outpatient'].groupby('period').size() in_trend = df[df['type'] == 'inpatient'].groupby('period').size() - + periods = sorted(set(out_trend.index.tolist() + in_trend.index.tolist())) - + trend = [] total_out = total_in = 0 for p in periods: @@ -180,12 +181,12 @@ async def get_cases_trend( total_out += out_count total_in += in_count trend.append(TrendPoint( - date=pd.Timestamp(p).strftime("%Y-%m-%d"), + date=str(p).split(' ')[0] if hasattr(p, 'strftime') else str(p)[:10], outpatient=out_count, inpatient=in_count, total=out_count + in_count )) - + return TrendResponse( trend=trend, summary={ @@ -198,6 +199,57 @@ async def get_cases_trend( ) +def _compute_cases_districts( + case_type: Optional[str], + min_count: int, + diagnosis: Optional[str], +) -> DistrictsResponse: + """Run the full pandas aggregation pipeline (called in thread pool).""" + df = get_combined_data() + + # 诊断过滤 + if diagnosis: + df = df[df['diagnosis'].str.contains(diagnosis, na=False, case=False)] + + # 类型过滤 + if case_type == "outpatient": + df = df[df['type'] == 'outpatient'] + elif case_type == "inpatient": + df = df[df['type'] == 'inpatient'] + + # 过滤未知区域 + df = df[(df['district'] != '未知') & (df['district'] != '其他')] + + # 聚合 + district_stats = df.groupby(['district', 'type']).size().unstack(fill_value=0) + + if 'outpatient' not in district_stats.columns: + district_stats['outpatient'] = 0 + if 'inpatient' not in district_stats.columns: + district_stats['inpatient'] = 0 + + district_stats['total'] = district_stats['outpatient'] + district_stats['inpatient'] + + # 过滤 + district_stats = district_stats[district_stats['total'] >= min_count] + district_stats = district_stats.sort_values('total', ascending=False) + + total = int(district_stats['total'].sum()) + + districts = [] + for district, row in district_stats.iterrows(): + districts.append(DistrictData( + district=str(district), + outpatient=int(row['outpatient']), + inpatient=int(row['inpatient']), + total=int(row['total']), + outpatient_ratio=round(float(row['outpatient']) / float(row['total']) * 100, 2) if row['total'] > 0 else 0, + inpatient_ratio=round(float(row['inpatient']) / float(row['total']) * 100, 2) if row['total'] > 0 else 0 + )) + + return DistrictsResponse(districts=districts, total=total) + + @router.get("/districts", response_model=DistrictsResponse, summary="获取区域分布数据") async def get_cases_districts( case_type: Optional[str] = Query(None, description="病例类型：outpatient, inpatient, all"), @@ -210,57 +262,19 @@ async def get_cases_districts( - 支持按病例类型筛选 - 可设置最小病例数过滤 - 返回各区门诊、住院量及占比 - """ - df = get_combined_data() - # 诊断过滤 - if diagnosis: - df = df[df['diagnosis'].str.contains(diagnosis, na=False, case=False)] - - # 类型过滤 - if case_type == "outpatient": - df = df[df['type'] == 'outpatient'] - elif case_type == "inpatient": - df = df[df['type'] == 'inpatient'] - - # 过滤未知区域 - df = df[(df['district'] != '未知') & (df['district'] != '其他')] - - # 聚合 - district_stats = df.groupby(['district', 'type']).size().unstack(fill_value=0) - - if 'outpatient' not in district_stats.columns: - district_stats['outpatient'] = 0 - if 'inpatient' not in district_stats.columns: - district_stats['inpatient'] = 0 - - district_stats['total'] = district_stats['outpatient'] + district_stats['inpatient'] - - # 过滤 - district_stats = district_stats[district_stats['total'] >= min_count] - district_stats = district_stats.sort_values('total', ascending=False) - - total = int(district_stats['total'].sum()) - - districts = [] - for district, row in district_stats.iterrows(): - districts.append(DistrictData( - district=district, - outpatient=int(row['outpatient']), - inpatient=int(row['inpatient']), - total=int(row['total']), - outpatient_ratio=round(row['outpatient'] / row['total'] * 100, 2) if row['total'] > 0 else 0, - inpatient_ratio=round(row['inpatient'] / row['total'] * 100, 2) if row['total'] > 0 else 0 - )) - - return DistrictsResponse(districts=districts, total=total) + Pandas processing runs in a thread pool to avoid blocking the async event loop. + """ + return await asyncio.to_thread( + _compute_cases_districts, case_type, min_count, diagnosis + ) @router.get("/realtime", response_model=RealtimeData, summary="获取实时数据") async def get_cases_realtime(): """ 获取实时病例数据 - + - 今日就诊量 - 近 7 日平均值 - 变化率 @@ -270,23 +284,23 @@ async def get_cases_realtime(): today = pd.Timestamp.today().normalize() last_7d = today - pd.Timedelta(days=7) - + # 今日数据 today_data = df[df['date'] >= today] today_total = len(today_data) today_out = len(today_data[today_data['type'] == 'outpatient']) today_in = len(today_data[today_data['type'] == 'inpatient']) - + # 近 7 日平均 last_7d_data = df[(df['date'] >= last_7d) & (df['date'] < today)] last_7d_avg = round(len(last_7d_data) / 7, 2) if len(last_7d_data) > 0 else 0 - + # 变化率 if last_7d_avg > 0: change_ratio = round((today_total - last_7d_avg) / last_7d_avg * 100, 2) else: change_ratio = 0.0 - + # 状态评估 if change_ratio > 20: status = "偏高" @@ -294,12 +308,12 @@ async def get_cases_realtime(): status = "偏低" else: status = "正常" - + return RealtimeData( today_outpatient=today_out, today_inpatient=today_in, today_total=today_total, - last_7d_avg=last_7d_avg, + last_7d_avg=int(last_7d_avg), change_ratio=change_ratio, status=status ) @@ -311,8 +325,290 @@ class DiagnosesResponse(BaseModel): @router.get("/diagnoses", response_model=DiagnosesResponse, summary="获取所有诊断名称列表") -async def get_diagnoses(): - """Returns deduplicated, sorted list of unique diagnosis names""" - df = get_combined_data() - diagnoses = sorted(df['diagnosis'].dropna().unique().tolist()) +async def get_diagnoses_list(): + """Returns deduplicated, sorted list of unique diagnosis names (cached, fast).""" + diagnoses = get_diagnoses() return DiagnosesResponse(diagnoses=diagnoses) + + +# ============== Seasonal & Distribution Endpoints ============== + +class SeasonalPoint(BaseModel): + """月度聚合数据点""" + month: int # 1-12 + month_label: str # "1月", "2月", ... + outpatient: int + inpatient: int + total: int + + +class SeasonalResponse(BaseModel): + """月度季节性响应""" + monthly: list[SeasonalPoint] + period_years: list[int] # e.g. [2022, 2023, 2024] + total_cases: int + + +class DiagnosisDistributionItem(BaseModel): + """诊断分布数据项""" + diagnosis: str + outpatient: int + inpatient: int + total: int + percentage: float + + +class DiagnosisDistributionResponse(BaseModel): + """诊断分布响应""" + diagnoses: list[DiagnosisDistributionItem] + total_cases: int + + +# ============== Demographics Models ============== + +class AgeBin(BaseModel): + """年龄分段数据""" + age_bin: int # 0-17 + outpatient: int + inpatient: int + + +class GenderSplit(BaseModel): + """性别拆分数据""" + outpatient: int + inpatient: int + + +class GenderSplitData(BaseModel): + """性别分布响应内层""" + male: GenderSplit + female: GenderSplit + + +class AgeDiagnosisMatrixItem(BaseModel): + """年龄-诊断矩阵项""" + age_group: str # "0-1", "1-3", "3-6", "6-12", "12-18" + diagnosis: str + outpatient: int + inpatient: int + total: int + + +class DemographicsResponse(BaseModel): + """人口统计响应""" + age_distribution: list[AgeBin] + gender_split: GenderSplitData + age_diagnosis_matrix: list[AgeDiagnosisMatrixItem] + + +# ============== Disease Seasonality Models ============== + +class DiseaseSeasonalityPoint(BaseModel): + """疾病月度季节性数据点""" + diagnosis: str + month: int # 1-12 + month_label: str # "1月"-"12月" + outpatient: int + inpatient: int + total: int + + +class DiseaseSeasonalityResponse(BaseModel): + """疾病季节性响应""" + seasonality: list[DiseaseSeasonalityPoint] + diagnoses: list[str] + + +@router.get("/seasonal", response_model=SeasonalResponse, summary="获取季节性月度聚合数据") +async def get_cases_seasonal( + diagnosis: Optional[str] = Query(None, description="Filter by diagnosis name"), +): + """ + 按月聚合所有年份的病例数据 + + - 返回 1-12 月各月门诊/住院/总计均值 + - 支持诊断过滤 + - 用于季节性分解图表 + """ + df = get_combined_data() + + if diagnosis: + df = df[df['diagnosis'].str.contains(diagnosis, na=False, case=False)] + + # Extract month and aggregate + df = df.copy() + df['month'] = df['date'].dt.month + years = sorted(df['date'].dt.year.unique().tolist()) + + out_monthly = df[df['type'] == 'outpatient'].groupby('month').size() + in_monthly = df[df['type'] == 'inpatient'].groupby('month').size() + + 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'] + + monthly = [] + total_cases = 0 + for m in range(1, 13): + out_count = int(out_monthly.get(m, 0)) + in_count = int(in_monthly.get(m, 0)) + total_cases += out_count + in_count + monthly.append(SeasonalPoint( + month=m, + month_label=month_labels[m - 1], + outpatient=out_count, + inpatient=in_count, + 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(): + """ + 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 + - 用于疾病季节性热力图、雷达图等 + """ + df = get_combined_data() + + # 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, + )) + + return DiseaseSeasonalityResponse( + seasonality=seasonality, + diagnoses=[str(d) for d in top_diagnoses], + )