feat: remove cost/费用 statistics from clinical analytics

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>

backend/routers/statistics.py

@@ -116,7 +116,6 @@ class KeyValueCount(BaseModel):
 class InpatientKpis(BaseModel):
     total_admissions: int
     median_los_days: float
-    mean_cost: float
     cure_rate: float
     emergency_admit_ratio: float
 
@@ -129,17 +128,6 @@ class LosByDisease(BaseModel):
     n: int
 
 
-class CostByDisease(BaseModel):
-    diagnosis: str
-    mean_cost: float
-    n: int
-
-
-class CostVsLos(BaseModel):
-    los: int
-    cost: float
-
-
 class LabelCount(BaseModel):
     outcome: Optional[str] = None
     route: Optional[str] = None
@@ -168,9 +156,6 @@ class InpatientClinicalResponse(BaseModel):
     kpis: InpatientKpis
     los_histogram: list[KeyValueCount]
     los_by_disease: list[LosByDisease]
-    cost_histogram: list[KeyValueCount]
-    cost_by_disease: list[CostByDisease]
-    cost_vs_los: list[CostVsLos]
     outcome_counts: list[OutcomeCount]
     admission_route_counts: list[RouteCount]
     bmi_by_age_band: list[BmiByAge]
@@ -252,11 +237,10 @@ class TemporalResponse(BaseModel):
 def _empty_inpatient_clinical() -> InpatientClinicalResponse:
     return InpatientClinicalResponse(
         kpis=InpatientKpis(
-            total_admissions=0, median_los_days=0.0, mean_cost=0.0,
+            total_admissions=0, median_los_days=0.0,
             cure_rate=0.0, emergency_admit_ratio=0.0,
         ),
-        los_histogram=[], los_by_disease=[], cost_histogram=[],
-        cost_by_disease=[], cost_vs_los=[], outcome_counts=[],
+        los_histogram=[], los_by_disease=[], outcome_counts=[],
         admission_route_counts=[], bmi_by_age_band=[],
     )
 
@@ -287,8 +271,6 @@ def _compute_inpatient_clinical() -> InpatientClinicalResponse:
 
     total = len(df)
     median_los = float(df_los["los"].median()) if len(df_los) else 0.0
-    cost = pd.to_numeric(df["住院总费用"], errors="coerce")
-    mean_cost = float(cost.mean()) if cost.notna().any() else 0.0
 
     outcome = df["出院情况"].fillna("未知")
     cure_n = int(outcome.isin(["治愈", "好转"]).sum())
@@ -301,7 +283,6 @@ def _compute_inpatient_clinical() -> InpatientClinicalResponse:
     kpis = InpatientKpis(
         total_admissions=total,
         median_los_days=round(median_los, 2),
-        mean_cost=round(mean_cost, 2),
         cure_rate=round(cure_rate, 4),
         emergency_admit_ratio=round(emerg_ratio, 4),
     )
@@ -328,39 +309,6 @@ def _compute_inpatient_clinical() -> InpatientClinicalResponse:
                 n=int(len(grp)),
             ))
 
-    # Cost histogram: 0-2k,2-4k,4-6k,6-8k,8-10k,10k+
-    cost_valid = cost.dropna()
-    cost_bins = [(0, 2000, "0-2k"), (2000, 4000, "2-4k"), (4000, 6000, "4-6k"),
-                 (6000, 8000, "6-8k"), (8000, 10000, "8-10k")]
-    cost_histogram: list[KeyValueCount] = []
-    for lo, hi, label in cost_bins:
-        cost_histogram.append(KeyValueCount(
-            bin_label=label, count=int(((cost_valid >= lo) & (cost_valid < hi)).sum())))
-    cost_histogram.append(KeyValueCount(bin_label="10k+", count=int((cost_valid >= 10000).sum())))
-
-    # Cost by disease (top 8 by n)
-    cost_by_disease: list[CostByDisease] = []
-    df_cost = df[cost.notna()].copy()
-    df_cost["_cost"] = cost[cost.notna()]
-    if len(df_cost):
-        top_cd = df_cost["诊断名称"].value_counts().head(8).index.tolist()
-        for d in top_cd:
-            grp = df_cost[df_cost["诊断名称"] == d]["_cost"]
-            cost_by_disease.append(CostByDisease(
-                diagnosis=str(d),
-                mean_cost=round(float(grp.mean()), 2),
-                n=int(len(grp)),
-            ))
-
-    # cost vs los scatter (up to 500 points)
-    cost_vs_los: list[CostVsLos] = []
-    scatter_df = df_los[cost.reindex(df_los.index).notna()].copy()
-    scatter_df["_cost"] = cost.reindex(scatter_df.index)
-    if len(scatter_df) > 500:
-        scatter_df = scatter_df.sample(n=500, random_state=42)
-    for _, r in scatter_df.iterrows():
-        cost_vs_los.append(CostVsLos(los=int(r["los"]), cost=round(float(r["_cost"]), 2)))
-
     # outcome counts
     outcome_counts = [
         OutcomeCount(outcome=str(k), count=int(v))
@@ -399,9 +347,6 @@ def _compute_inpatient_clinical() -> InpatientClinicalResponse:
         kpis=kpis,
         los_histogram=los_histogram,
         los_by_disease=los_by_disease,
-        cost_histogram=cost_histogram,
-        cost_by_disease=cost_by_disease,
-        cost_vs_los=cost_vs_los,
         outcome_counts=outcome_counts,
         admission_route_counts=admission_route_counts,
         bmi_by_age_band=bmi_by_age_band,

frontend/e2e/clinical.spec.ts

@@ -10,7 +10,6 @@ const CLINICAL_FIXTURE = {
   kpis: {
     total_admissions: 5822,
     median_los_days: 4,
-    mean_cost: 6294,
     cure_rate: 0.991,
     emergency_admit_ratio: 0.47,
   },
@@ -23,19 +22,6 @@ const CLINICAL_FIXTURE = {
     { diagnosis: '肺炎', p25: 3, median: 5, p75: 7, n: 800 },
     { diagnosis: '支气管炎', p25: 2, median: 4, p75: 6, n: 600 },
   ],
-  cost_histogram: [
-    { bin_label: '0-3k', count: 1800 },
-    { bin_label: '3k-6k', count: 2200 },
-  ],
-  cost_by_disease: [
-    { diagnosis: '肺炎', mean_cost: 7200, n: 800 },
-    { diagnosis: '支气管炎', mean_cost: 5100, n: 600 },
-  ],
-  cost_vs_los: [
-    { los: 3, cost: 5000 },
-    { los: 5, cost: 7200 },
-    { los: 7, cost: 9100 },
-  ],
   outcome_counts: [
     { outcome: '治愈', count: 3474 },
     { outcome: '好转', count: 2298 },

frontend/src/components/clinical/ClinicalKpiRow.tsx

@@ -1,5 +1,5 @@
 import { memo } from 'react';
-import { Users, CalendarDays, Wallet, HeartPulse, Siren } from 'lucide-react';
+import { Users, CalendarDays, HeartPulse, Siren } from 'lucide-react';
 import { StatCard } from '@/components/StatCard';
 import { TESTIDS } from '@/utils/testids';
 import type { InpatientClinicalResponse } from '@/services/api';
@@ -8,12 +8,12 @@ interface ClinicalKpiRowProps {
   kpis: InpatientClinicalResponse['kpis'];
 }
 
-/** 住院临床 5 项核心指标。375px 下 2 列，sm 起 5 列。 */
+/** 住院临床 4 项核心指标。375px 下 2 列，sm 起 4 列。 */
 export const ClinicalKpiRow = memo(function ClinicalKpiRow({ kpis }: ClinicalKpiRowProps) {
   return (
     <div
       data-testid={TESTIDS.clinicalKpis}
-      className="grid grid-cols-2 sm:grid-cols-5 gap-3"
+      className="grid grid-cols-2 sm:grid-cols-4 gap-3"
     >
       <StatCard
         icon={<Users className="w-4 h-4 text-primary" />}
@@ -25,11 +25,6 @@ export const ClinicalKpiRow = memo(function ClinicalKpiRow({ kpis }: ClinicalKpi
         label="中位住院日"
         value={`${kpis.median_los_days} 天`}
       />
-      <StatCard
-        icon={<Wallet className="w-4 h-4 text-primary" />}
-        label="人均费用"
-        value={`¥${Math.round(kpis.mean_cost).toLocaleString()}`}
-      />
       <StatCard
         icon={<HeartPulse className="w-4 h-4 text-success" />}
         label="治愈好转率"

frontend/src/components/clinical/CostByDiseaseChart.tsx

@@ -1,62 +0,0 @@
-import { memo } from 'react';
-import {
-  BarChart,
-  Bar,
-  XAxis,
-  YAxis,
-  CartesianGrid,
-  Tooltip,
-  ResponsiveContainer,
-} from 'recharts';
-import { CLINICAL_COLORS, TOOLTIP_STYLE } from './chartColors';
-
-interface CostByDiseaseChartProps {
-  data: { diagnosis: string; mean_cost: number; n: number }[];
-}
-
-function truncate(s: string, max: number): string {
-  return s.length > max ? s.slice(0, max) + '…' : s;
-}
-
-/** 各病种平均费用横向柱状图。 */
-export const CostByDiseaseChart = memo(function CostByDiseaseChart({
-  data,
-}: CostByDiseaseChartProps) {
-  if (!data || data.length === 0) {
-    return <div className="text-center py-8 text-text-muted text-sm">暂无数据</div>;
-  }
-
-  const chartData = [...data]
-    .sort((a, b) => a.mean_cost - b.mean_cost)
-    .map((d) => ({ ...d, displayName: truncate(d.diagnosis, 8) }));
-
-  return (
-    <ResponsiveContainer width="100%" height={Math.max(240, chartData.length * 34)}>
-      <BarChart
-        data={chartData}
-        layout="vertical"
-        margin={{ top: 5, right: 20, left: 12, bottom: 5 }}
-      >
-        <CartesianGrid strokeDasharray="3 3" stroke={CLINICAL_COLORS.grid} horizontal={false} />
-        <XAxis
-          type="number"
-          tick={{ fontSize: 10, fill: CLINICAL_COLORS.axis }}
-          tickFormatter={(v: number) => `¥${(v / 1000).toFixed(0)}k`}
-        />
-        <YAxis
-          type="category"
-          dataKey="displayName"
-          tick={{ fontSize: 10, fill: CLINICAL_COLORS.axisLabel }}
-          width={72}
-          axisLine={false}
-          tickLine={false}
-        />
-        <Tooltip
-          contentStyle={TOOLTIP_STYLE}
-          formatter={(v: number) => [`¥${Math.round(v).toLocaleString()}`, '人均费用']}
-        />
-        <Bar dataKey="mean_cost" fill={CLINICAL_COLORS.cost} barSize={16} radius={[0, 3, 3, 0]} />
-      </BarChart>
-    </ResponsiveContainer>
-  );
-});

frontend/src/components/clinical/CostVsLosScatter.tsx

@@ -1,55 +0,0 @@
-import { memo } from 'react';
-import {
-  ScatterChart,
-  Scatter,
-  XAxis,
-  YAxis,
-  CartesianGrid,
-  Tooltip,
-  ResponsiveContainer,
-} from 'recharts';
-import { CLINICAL_COLORS, TOOLTIP_STYLE } from './chartColors';
-
-interface CostVsLosScatterProps {
-  data: { los: number; cost: number }[];
-}
-
-/** 费用 vs 住院天数散点。 */
-export const CostVsLosScatter = memo(function CostVsLosScatter({ data }: CostVsLosScatterProps) {
-  if (!data || data.length === 0) {
-    return <div className="text-center py-8 text-text-muted text-sm">暂无数据</div>;
-  }
-
-  return (
-    <ResponsiveContainer width="100%" height={300}>
-      <ScatterChart margin={{ top: 10, right: 16, left: 6, bottom: 16 }}>
-        <CartesianGrid strokeDasharray="3 3" stroke={CLINICAL_COLORS.grid} />
-        <XAxis
-          type="number"
-          dataKey="los"
-          name="住院天数"
-          unit="天"
-          tick={{ fontSize: 10, fill: CLINICAL_COLORS.axis }}
-        />
-        <YAxis
-          type="number"
-          dataKey="cost"
-          name="费用"
-          tick={{ fontSize: 10, fill: CLINICAL_COLORS.axis }}
-          width={52}
-          tickFormatter={(v: number) => `¥${(v / 1000).toFixed(0)}k`}
-        />
-        <Tooltip
-          contentStyle={TOOLTIP_STYLE}
-          cursor={{ strokeDasharray: '3 3' }}
-          formatter={(value: number, name: string) =>
-            name === '费用'
-              ? [`¥${value.toLocaleString()}`, name]
-              : [`${value} 天`, name]
-          }
-        />
-        <Scatter data={data} fill={CLINICAL_COLORS.scatter} fillOpacity={0.5} />
-      </ScatterChart>
-    </ResponsiveContainer>
-  );
-});

frontend/src/components/clinical/HistogramChart.tsx

@@ -13,11 +13,11 @@ import { CLINICAL_COLORS, TOOLTIP_STYLE } from './chartColors';
 interface HistogramChartProps {
   data: { bin_label: string; count: number }[];
   color?: string;
-  /** tooltip 中数量的标签，如 "住院天数" / "费用区间"。 */
+  /** tooltip 中数量的标签，如 "住院天数"。 */
   countLabel?: string;
 }
 
-/** 通用直方图。复用于「住院天数分布」与「住院费用分布」。 */
+/** 通用直方图。用于「住院天数分布」。 */
 export const HistogramChart = memo(function HistogramChart({
   data,
   color = CLINICAL_COLORS.los,

frontend/src/components/clinical/chartColors.ts

@@ -5,8 +5,6 @@
 export const CLINICAL_COLORS = {
   primary: '#2563EB', // primary
   los: '#2563EB',
-  cost: '#0891B2', // cyan — 费用维度
-  scatter: '#7C3AED', // violet — 散点
   box: '#3B82F6', // 箱体填充
   boxMedian: '#1D4ED8', // 中位刻度
   grid: '#E2E8F0',

frontend/src/pages/ClinicalAnalysis.tsx

@@ -7,8 +7,6 @@ import { TESTIDS } from '@/utils/testids';
 import { ClinicalKpiRow } from '@/components/clinical/ClinicalKpiRow';
 import { HistogramChart } from '@/components/clinical/HistogramChart';
 import { BoxPlotRows, type BoxRow } from '@/components/clinical/BoxPlotRows';
-import { CostVsLosScatter } from '@/components/clinical/CostVsLosScatter';
-import { CostByDiseaseChart } from '@/components/clinical/CostByDiseaseChart';
 import { DonutChart, type DonutSlice } from '@/components/clinical/DonutChart';
 import { CLINICAL_COLORS } from '@/components/clinical/chartColors';
 
@@ -58,7 +56,7 @@ export function ClinicalAnalysis() {
         住院临床分析
       </h1>
       <p className="text-[12px] text-text-secondary">
-        住院天数、费用、出院结局与入院途径等临床特征分析
+        住院天数、出院结局、入院途径与年龄别 BMI 等临床特征分析
       </p>
     </div>
   );
@@ -140,21 +138,6 @@ export function ClinicalAnalysis() {
           </Card>
         </div>
 
-        {/* 费用：分布 + 各病种平均费用 */}
-        <div className="grid grid-cols-1 lg:grid-cols-2 gap-6">
-          <Card title="住院费用分布">
-            <HistogramChart data={data.cost_histogram ?? []} color={CLINICAL_COLORS.cost} countLabel="人次" />
-          </Card>
-          <Card title="各病种平均费用">
-            <CostByDiseaseChart data={data.cost_by_disease ?? []} />
-          </Card>
-        </div>
-
-        {/* 费用 vs 住院天数 散点 */}
-        <Card title="费用 vs 住院天数">
-          <CostVsLosScatter data={data.cost_vs_los ?? []} />
-        </Card>
-
         {/* 出院结局 + 入院途径 双环 */}
         <div className="grid grid-cols-1 lg:grid-cols-2 gap-6">
           <Card title="出院结局构成">

frontend/src/services/api.ts

@@ -348,15 +348,11 @@ export interface InpatientClinicalResponse {
   kpis: {
     total_admissions: number;
     median_los_days: number;
-    mean_cost: number;
     cure_rate: number;
     emergency_admit_ratio: number;
   };
   los_histogram: { bin_label: string; count: number }[];
   los_by_disease: { diagnosis: string; p25: number; median: number; p75: number; n: number }[];
-  cost_histogram: { bin_label: string; count: number }[];
-  cost_by_disease: { diagnosis: string; mean_cost: number; n: number }[];
-  cost_vs_los: { los: number; cost: number }[];
   outcome_counts: { outcome: string; count: number }[];
   admission_route_counts: { route: string; count: number }[];
   bmi_by_age_band: { age_band: string; p25: number; median: number; p75: number; n: number }[];