[Feature] Add custom CSV EHR dataset adapter for lightweight onboarding

examples/synthetic_ehr/generate_synthetic_ehr.py

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+"""
+生成教学用合成 EHR 数据集 (PyHealth)
+
+字段:
+    patient_id, visit_id, visit_date, age, gender,
+    diagnosis_code (ICD-9), procedure_code (ICD-9-CM),
+    drug_code (NDC), lab_glucose, lab_creatinine
+
+临床合理性设计:
+    - 每个患者被赋予一个"主诊断"(primary condition)，
+      该诊断决定其用药与实验室值的倾向。
+    - 糖尿病患者 (250.00): 高血糖倾向 + 更可能用二甲双胍(860975)
+    - 高血压/冠心病/心衰患者: 更可能用赖诺普利(003127)、阿司匹林(003127)、阿托伐他汀(551110)
+    - 体检患者 (V70.0): 实验室值接近正常，通常不用药
+"""
+
+import random
+import csv
+from datetime import date, timedelta
+
+random.seed(42)  # 可复现
+
+# ---- 诊断码与药物码定义 ----
+DIAGNOSES = {
+    "250.00": "糖尿病",
+    "401.9": "高血压",
+    "414.01": "冠心病",
+    "428.0": "心衰",
+    "530.81": "GERD",
+    "V70.0": "体检",
+}
+
+DRUGS = {
+    "860975": "二甲双胍",       # metformin
+    "003127": "赖诺普利",       # lisinopril
+    "551110": "阿托伐他汀",     # atorvastatin
+    "504580": "阿司匹林",       # aspirin
+}
+
+# ---- 每位患者的主诊断, 用于指导临床合理性 ----
+# 10 位患者: 4 糖尿病, 2 高血压, 1 冠心病, 1 心衰, 1 GERD, 1 体检
+PRIMARY_CONDITIONS = [
+    "250.00", "250.00", "250.00", "250.00",  # 4 糖尿病
+    "401.9", "401.9",                          # 2 高血压
+    "414.01",                                 # 1 冠心病
+    "428.0",                                  # 1 心衰
+    "530.81",                                 # 1 GERD
+    "V70.0",                                  # 1 体检
+]
+
+
+def pick_primary_diagnosis(condition: str) -> str:
+    """根据主诊断返回本次就诊的诊断码, 有时会附带并发症诊断。"""
+    return condition
+
+
+def pick_secondary_diagnoses(condition: str) -> list:
+    """返回可能伴随的诊断码列表(不含主诊断)。"""
+    secondary = []
+    if condition == "250.00" and random.random() < 0.5:
+        secondary.append("401.9")  # 糖尿病常合并高血压
+    if condition == "414.01" and random.random() < 0.6:
+        secondary.append("401.9")  # 冠心病常合并高血压
+    if condition == "428.0" and random.random() < 0.5:
+        secondary.append("401.9")
+    if condition == "401.9" and random.random() < 0.3:
+        secondary.append("250.00")
+    return secondary
+
+
+def pick_drugs(condition: str) -> list:
+    """根据主诊断选择药物, 体现临床合理性。"""
+    drugs = []
+    if condition == "250.00":
+        if random.random() < 0.9:
+            drugs.append("860975")          # 二甲双胍
+        if random.random() < 0.4:
+            drugs.append("003127")          # 赖诺普利 (合并高血压时)
+    elif condition == "401.9":
+        if random.random() < 0.9:
+            drugs.append("003127")          # 赖诺普利
+        if random.random() < 0.4:
+            drugs.append("551110")          # 阿托伐他汀
+    elif condition == "414.01":
+        if random.random() < 0.8:
+            drugs.append("003127")          # 阿司匹林
+        if random.random() < 0.7:
+            drugs.append("551110")          # 阿托伐他汀
+    elif condition == "428.0":
+        if random.random() < 0.8:
+            drugs.append("003127")          # 赖诺普利
+    elif condition == "530.81":
+        if random.random() < 0.5:
+            pass  # GERD 有时不用所列药物
+    # V70.0 体检通常不用药
+    if not drugs:
+        # 保证非空, 给一个低概率安慰用药或留空由调用方处理
+        if random.random() < 0.3:
+            drugs.append("551110")
+    return drugs
+
+
+def gen_glucose(condition: str) -> float:
+    """根据诊断生成合理血糖值。"""
+    if condition == "250.00":
+        # 糖尿病: 偏高 (110-300)
+        return round(random.uniform(110, 300), 1)
+    else:
+        # 非糖尿病: 正常偏低 (70-130)
+        return round(random.uniform(70, 130), 1)
+
+
+def gen_creatinine(condition: str) -> float:
+    """根据诊断生成合理肌酐值 (心衰/糖尿病肾功能略差)。"""
+    base = random.uniform(0.5, 1.2)
+    if condition == "428.0":
+        base = random.uniform(0.9, 2.5)   # 心衰肾功能可能受损
+    elif condition == "250.00":
+        base = random.uniform(0.6, 2.0)   # 糖尿病肾病风险
+    return round(min(base, 3.0), 2)
+
+
+def gen_procedure(condition: str) -> str:
+    """简单流程码: 用诊断码简化替代 ICD-9-CM 流程 (教学用途)。"""
+    # 常见配套检查流程 (ICD-9-CM 示例)
+    procedures = {
+        "250.00": "89.52",   # 心电图? 此处仅为示例码
+        "401.9": "89.65",    # 动脉血气/血压监测示例
+        "414.01": "88.72",   # 诊断性心导管
+        "428.0": "89.52",    # 心电图
+        "530.81": "87.62",   # 上消化道造影示例
+        "V70.0": "89.01",    # 常规体检问诊
+    }
+    return procedures[condition]
+
+
+rows = []
+visit_id_counter = 1
+
+for pid_idx, condition in enumerate(PRIMARY_CONDITIONS, start=1):
+    patient_id = f"P{pid_idx:03d}"
+    age = random.randint(30, 85)
+    gender = "M" if pid_idx <= 5 else "F"  # 前 5 位 M, 后 5 位 F
+
+    n_visits = random.randint(1, 4)
+
+    # 就诊日期: 在 2022-01 ~ 2023-12 内递增分布
+    start = date(2022, 1, 1)
+    end = date(2023, 12, 31)
+    total_days = (end - start).days
+
+    visit_dates = []
+    # 生成 n_visits 个递增日期
+    offsets = sorted(random.sample(range(total_days + 1), n_visits))
+    for off in offsets:
+        visit_dates.append(start + timedelta(days=off))
+
+    for vi in range(n_visits):
+        visit_id = f"V{visit_id_counter:04d}"
+        visit_id_counter += 1
+
+        # 诊断码: 主诊断 + 可能的伴随诊断, 用 '|' 分隔
+        diag_list = [pick_primary_diagnosis(condition)]
+        diag_list += pick_secondary_diagnoses(condition)
+        # 去重保持顺序
+        seen = set()
+        diag_codes = []
+        for d in diag_list:
+            if d not in seen:
+                seen.add(d)
+                diag_codes.append(d)
+        diagnosis_code = "|".join(diag_codes)
+
+        drugs = pick_drugs(condition)
+        drug_code = "|".join(drugs) if drugs else ""
+
+        rows.append({
+            "patient_id": patient_id,
+            "visit_id": visit_id,
+            "visit_date": visit_dates[vi].isoformat(),
+            "age": age,
+            "gender": gender,
+            "diagnosis_code": diagnosis_code,
+            "procedure_code": gen_procedure(condition),
+            "drug_code": drug_code,
+            "lab_glucose": gen_glucose(condition),
+            "lab_creatinine": gen_creatinine(condition),
+        })
+
+# ---- 保存 CSV ----
+fieldnames = [
+    "patient_id", "visit_id", "visit_date", "age", "gender",
+    "diagnosis_code", "procedure_code", "drug_code",
+    "lab_glucose", "lab_creatinine",
+]
+
+with open("synthetic_ehr.csv", "w", newline="", encoding="utf-8") as f:
+    writer = csv.DictWriter(f, fieldnames=fieldnames)
+    writer.writeheader()
+    writer.writerows(rows)
+
+# ---- 打印前 10 行确认 ----
+print("生成完成: synthetic_ehr.csv")
+print("总记录数:", len(rows))
+print("-" * 100)
+print(",".join(fieldnames))
+for r in rows[:10]:
+    print(",".join(str(r[k]) for k in fieldnames))

examples/synthetic_ehr/synthetic_ehr.csv

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+patient_id,visit_id,visit_date,age,gender,diagnosis_code,procedure_code,drug_code,lab_glucose,lab_creatinine
+P001,V0001,2022-01-26,70,M,250.00,89.52,860975|003127,129.5,1.36
+P002,V0002,2022-01-31,67,M,250.00|401.9,89.52,860975,246.0,1.19
+P002,V0003,2022-02-02,67,M,250.00|401.9,89.52,860975,254.2,1.19
+P002,V0004,2022-04-06,67,M,250.00|401.9,89.52,860975,129.4,1.1
+P002,V0005,2022-08-12,67,M,250.00|401.9,89.52,860975|003127,197.3,1.89
+P003,V0006,2023-01-06,35,M,250.00,89.52,860975|003127,153.3,0.71
+P003,V0007,2023-09-26,35,M,250.00|401.9,89.52,860975|003127,230.8,1.12
+P003,V0008,2023-10-06,35,M,250.00|401.9,89.52,860975,233.1,0.84
+P004,V0009,2022-06-17,76,M,250.00|401.9,89.52,,240.1,1.69
+P004,V0010,2023-04-19,76,M,250.00|401.9,89.52,860975|003127,160.9,1.92
+P005,V0011,2022-08-06,75,M,401.9,89.65,551110,84.8,0.89
+P005,V0012,2023-05-27,75,M,401.9|250.00,89.65,003127,94.0,0.65
+P005,V0013,2023-11-03,75,M,401.9,89.65,003127|551110,72.8,0.58
+P006,V0014,2023-03-09,70,F,401.9,89.65,003127,98.1,0.68
+P006,V0015,2023-11-28,70,F,401.9,89.65,551110,123.1,1.03
+P007,V0016,2022-04-25,79,F,414.01|401.9,88.72,003127,113.2,1.18
+P007,V0017,2022-10-28,79,F,414.01,88.72,003127|551110,107.5,1.09
+P007,V0018,2023-03-22,79,F,414.01|401.9,88.72,003127|551110,79.7,1.17
+P008,V0019,2023-09-06,63,F,428.0|401.9,89.52,003127,125.7,2.23
+P009,V0020,2022-03-01,49,F,530.81,87.62,,75.1,0.84
+P009,V0021,2022-09-04,49,F,530.81,87.62,,116.0,0.59
+P010,V0022,2022-09-29,60,F,V70.0,89.01,,95.4,0.65
+P010,V0023,2023-06-25,60,F,V70.0,89.01,,113.8,0.64