201 lines
8.0 KiB
Python
201 lines
8.0 KiB
Python
"""
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将「数据」目录下三份 Excel（与 #数据与表结构.md 一致）转换为 analytics-demo-web/public/mock/*.json。
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派生字段（源表无列时）：
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- AE occurrenceDate / registrationDate：由审核日回溯生成，保证 occurrence < registration < review（演示用）。
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- AE province：由入院量表按医院名称众数映射。
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- AE SAE 四布尔：由「伤害表现」关键词启发式判定。
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- 入院 productName：将 MaterialDesc 映射到 AE∪投诉 产品名称集合（最长前缀包含匹配），便于与投诉率联表。
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"""
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from __future__ import annotations
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import hashlib
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import json
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from pathlib import Path
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import pandas as pd
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ROOT = Path(__file__).resolve().parents[1]
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DATA_DIR = ROOT / "数据"
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OUT_DIR = ROOT / "analytics-demo-web" / "public" / "mock"
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def _d(v) -> pd.Timestamp | pd.NaTType:
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if pd.isna(v):
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return pd.NaT
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return pd.to_datetime(v)
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def _fmt(d: pd.Timestamp | pd.NaTType) -> str:
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if pd.isna(d):
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return ""
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return d.strftime("%Y-%m-%d")
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def _stable_int(s: str) -> int:
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return int(hashlib.md5(s.encode("utf-8")).hexdigest()[:8], 16)
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def infer_sae_flags(injury_text: str) -> dict[str, bool]:
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t = injury_text or ""
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return {
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"saeDeath": ("死亡" in t) and ("非死亡" not in t),
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"saeLifeThreatening": "危及生命" in t or "生命威胁" in t or "心跳骤停" in t,
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"saeDisability": "残疾" in t or "功能障碍" in t,
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"saeHospitalization": "住院" in t or "延长住院" in t or "住院时间延长" in t,
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}
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def map_material_to_product(material_desc: str, canonical_products: list[str]) -> str:
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if not isinstance(material_desc, str) or not material_desc.strip():
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return ""
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s = material_desc.strip()
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for p in canonical_products:
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if p and p in s:
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return p
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return s.split()[0] if " " in s else s
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def main() -> None:
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ae_path = DATA_DIR / "不良事件数据-模拟1000条-20260414.xlsx"
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adm_path = DATA_DIR / "入院量数据-模拟1000条-20260414.xlsx"
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cmp_path = DATA_DIR / "质量投诉数据-模拟1000条-20260414.xlsx"
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ae_df = pd.read_excel(ae_path, sheet_name=0)
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adm_df = pd.read_excel(adm_path, sheet_name=0)
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cmp_df = pd.read_excel(cmp_path, sheet_name=0)
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cmp_df.columns = [str(c).replace("\n", "").strip() for c in cmp_df.columns]
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hosp_prov = (
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adm_df.groupby("HospitalName")["Province"]
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.agg(lambda s: s.mode().iloc[0] if len(s.mode()) else (s.iloc[0] if len(s) else ""))
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.to_dict()
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)
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hospital_keys = sorted(hosp_prov.keys(), key=len, reverse=True)
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# 入院量表仅覆盖部分医院；其余 AE 单位按常识补省（仅用于演示地图/省聚合）
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province_override: dict[str, str] = {
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"中国医科大学附属第一医院": "辽宁省",
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"华中科技大学同济医学院附属协和医院": "湖北省",
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"昆明医科大学第一附属医院": "云南省",
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"天津市肿瘤医院": "天津市",
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"重庆医科大学附属第一医院": "重庆市",
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}
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def resolve_province(unit: str) -> str:
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if unit in province_override:
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return province_override[unit]
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if unit in hosp_prov:
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return str(hosp_prov[unit]).strip()
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for h in hospital_keys:
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if not h:
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continue
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if h in unit or unit in h:
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return str(hosp_prov[h]).strip()
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return "（未映射）"
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ae_products = set(ae_df["产品名称"].dropna().astype(str).unique())
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cmp_products = set(cmp_df["产品名称"].dropna().astype(str).unique())
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canonical_products = sorted(ae_products | cmp_products, key=len, reverse=True)
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ae_rows: list[dict] = []
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for _, row in ae_df.iterrows():
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code = str(row.get("报告编码", "")).strip()
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review = _d(row.get("审核日期"))
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if pd.isna(review):
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continue
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h = _stable_int(code) % 10000
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reg = review - pd.Timedelta(days=3 + (h % 6))
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occ = reg - pd.Timedelta(days=1 + (h % 12))
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unit = str(row.get("单位名称", "")).strip()
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injury = str(row.get("伤害表现", "") or "").strip()
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flags = infer_sae_flags(injury)
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ae_rows.append(
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{
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"reportCode": code,
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"occurrenceDate": _fmt(occ),
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"registrationDate": _fmt(reg),
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"unitName": unit,
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"businessUnit": str(row.get("事业线", "") or "").strip(),
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"productName": str(row.get("产品名称", "") or "").strip(),
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"registrationNo": str(row.get("注册证编号/曾用注册证编号", "") or "").strip(),
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"model": str(row.get("型号", "") or "").strip(),
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"batchNo": str(row.get("产品批号", "") or "").strip(),
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"injuryExpression": injury,
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"deviceFailure": str(row.get("器械故障表现", "") or "").strip(),
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"reviewDate": _fmt(review),
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"province": resolve_province(unit) or "（未映射）",
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**flags,
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}
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)
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adm_rows: list[dict] = []
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for _, row in adm_df.iterrows():
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md = row.get("MaterialDesc")
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pname = map_material_to_product(str(md) if md == md else "", canonical_products)
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adm_rows.append(
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{
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"year": int(row["Year"]),
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"month": int(row["Month"]),
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"hospitalName": str(row.get("HospitalName", "") or "").strip(),
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"dealerName": str(row.get("DealerName", "") or "").strip(),
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"province": str(row.get("Province", "") or "").strip(),
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"bu": str(row.get("BU", "") or "").strip(),
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"productName": pname,
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"cyQty": float(row.get("CY Qty", 0) or 0),
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"lyQty": float(row.get("LY Qty", 0) or 0),
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"growthQtyPct": round(float(row.get("Growth% Qty", 0) or 0) * 100, 4),
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"cyAmt": float(row.get("CY Amt", 0) or 0),
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"growthAmtPct": round(float(row.get("Growth% Amt", 0) or 0) * 100, 4),
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}
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)
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def yn_to_bool(v) -> bool:
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s = str(v).strip()
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return s == "是" or s.lower() == "true" or s == "1"
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cmp_rows: list[dict] = []
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for _, row in cmp_df.iterrows():
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reg = _d(row.get("C3登记日期"))
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if pd.isna(reg):
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continue
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close = _d(row.get("关闭日期"))
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survey = _d(row.get("调查报告完成日期"))
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if pd.isna(close):
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close = survey if not pd.isna(survey) else reg
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cmp_rows.append(
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{
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"c3Code": str(row.get("C3编号", "")).strip(),
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"model": str(row.get("型号", "") or "").strip(),
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"batchNo": str(row.get("批号", "") or "").strip(),
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"registrationNo": str(row.get("注册证号", "") or "").strip(),
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"productName": str(row.get("产品名称", "") or "").strip(),
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"hospitalName": str(row.get("医院名称", "") or "").strip(),
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"faultType": str(row.get("故障类型", "") or "").strip(),
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"registerDate": _fmt(reg),
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"isAe": yn_to_bool(row.get("是否不良事件")),
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"conclusion": str(row.get("调查结论（处理结果）", "") or "").strip(),
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"compensation": str(row.get("赔付结论", "") or "").strip(),
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"closeDate": _fmt(close),
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}
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)
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OUT_DIR.mkdir(parents=True, exist_ok=True)
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(OUT_DIR / "ae.json").write_text(
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json.dumps(ae_rows, ensure_ascii=False, indent=2),
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encoding="utf-8",
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)
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(OUT_DIR / "admission.json").write_text(
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json.dumps(adm_rows, ensure_ascii=False, indent=2),
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encoding="utf-8",
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)
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(OUT_DIR / "complaint.json").write_text(
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json.dumps(cmp_rows, ensure_ascii=False, indent=2),
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encoding="utf-8",
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)
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print("Wrote", len(ae_rows), "ae,", len(adm_rows), "admission,", len(cmp_rows), "complaint ->", OUT_DIR)
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if __name__ == "__main__":
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main()
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