「醫生沒有問 CNN 為什麼他會這樣。但他們需要知道他為什麼做出這個決定。」— 可解釋性是醫療人工智慧部署的必備條件。
1. 為什麼可解釋性在醫療保健領域更為重要？
| 情況 | 缺乏 XAI 的後果 |
|---|---|
| 人工智慧對癌症呈陽性醫生不知道為什麼→不信任 | |
| AI陰性癌症 | 醫生擔心漏掉什麼→還是做了更多檢查 |
| AI是錯的，因為捷徑 | 人工智慧學習的是文物，而不是疾病模式 |
| 監管提交 | FDA 要求提供透明度證據 |
醫療人工智慧中的聰明漢斯效應：模型學習虛假相關性：
- 胸部X光模型是醫院標籤，而不是病理
- 皮膚病變模型學習標尺標記 = 黑色素瘤
- 肺炎模型研究醫院（匹茲堡=「無肺炎」）
2. Grad-CAM（第 4 課中學習）－總結
影像模型的 Grad-CAM（類激活映射）：
- 前向傳播→預測 2.目標類別的反向傳播梯度→轉到最後一個卷積層 3.全域平均池梯度→重要性權重 4.特徵圖的加權和→熱圖
限制：僅適用於 CNN，只能在特徵圖解析度下解釋。
3. SHAP——表格資料的統一框架
import shap
import numpy as np
import pandas as pd
from sklearn.ensemble import GradientBoostingClassifier
# Ví dụ: ICU mortality prediction (readmission risk)
# Features: vitals, labs, demographics, diagnosis codes
class ICUMortalityExplainer:
"""
SHAP TreeExplainer cho tree-based models (XGBoost, GBM, RandomForest).
SHAP = unified measure of feature importance dựa trên game theory.
SHAP value của feature j = marginal contribution của j với tất cả subsets.
Tính chất quan trọng:
- Additive: sum(SHAP values) = prediction - expected value
- Local: explain từng prediction cụ thể (không phải global average)
- Consistent: nếu model phụ thuộc nhiều hơn vào feature → SHAP lớn hơn
"""
def __init__(self, model: GradientBoostingClassifier, X_train: pd.DataFrame):
self.model = model
self.feature_names = X_train.columns.tolist()
self.explainer = shap.TreeExplainer(model)
self.background_data = X_train
def explain_patient(self, patient_features: pd.DataFrame) -> dict:
"""
Explain prediction cho 1 bệnh nhân cụ thể.
"""
shap_values = self.explainer.shap_values(patient_features)
# Với binary classifier: shap_values có thể là list [class0, class1]
if isinstance(shap_values, list):
sv = shap_values[1] # Class 1 (mortality)
else:
sv = shap_values
prediction = self.model.predict_proba(patient_features)[0, 1]
expected = self.explainer.expected_value
if isinstance(expected, list):
expected = expected[1]
# Sort features by |SHAP| value
feature_impacts = sorted(
zip(self.feature_names, sv[0]),
key=lambda x: abs(x[1]),
reverse=True
)
return {
"prediction_probability": round(float(prediction), 4),
"expected_value": round(float(expected), 4),
"top_contributing_features": [
{
"feature": f,
"value": round(float(patient_features[f].iloc[0]), 3),
"shap_value": round(float(sv_val), 4),
"direction": "increases risk" if sv_val > 0 else "decreases risk"
}
for f, sv_val in feature_impacts[:10]
],
"waterfall_data": {f: sv_val for f, sv_val in feature_impacts}
}
def global_feature_importance(self, X_test: pd.DataFrame) -> pd.DataFrame:
"""Global importance = mean|SHAP| across test patients."""
shap_values = self.explainer.shap_values(X_test)
if isinstance(shap_values, list):
sv = shap_values[1]
else:
sv = shap_values
importance = pd.DataFrame({
"feature": self.feature_names,
"mean_abs_shap": np.abs(sv).mean(axis=0)
}).sort_values("mean_abs_shap", ascending=False)
return importance
def visualize_shap_explanation(explanation: dict) -> str:
"""
Generate text explanation suitable cho clinical display.
(Trong production: render as interactive chart với Plotly/D3)
"""
pred = explanation["prediction_probability"]
risk_level = "HIGH" if pred > 0.5 else "LOW"
lines = [
f"Mortality Risk: {pred:.1%} ({risk_level})",
"\nTop factors increasing risk:",
]
for f in explanation["top_contributing_features"]:
if f["shap_value"] > 0:
lines.append(
f" ↑ {f['feature']} = {f['value']} "
f"(+{f['shap_value']:.3f} risk contribution)"
)
lines.append("\nTop factors decreasing risk:")
for f in explanation["top_contributing_features"]:
if f["shap_value"] < 0:
lines.append(
f" ↓ {f['feature']} = {f['value']} "
f"({f['shap_value']:.3f} risk contribution)"
)
return "\n".join(lines)
4. 黑盒模型的 LIME
from lime.lime_tabular import LimeTabularExplainer
from lime.lime_image import LimeImageExplainer
def explain_with_lime_tabular(
model_predict_fn,
X_train: np.ndarray,
feature_names: list[str],
patient_instance: np.ndarray,
num_features: int = 10,
) -> dict:
"""
LIME: Local Interpretable Model-agnostic Explanations.
Ý tưởng: fit một linear model (interpretable) xung quanh 1 prediction.
1. Perturbate instance: tạo nhiều samples gần patient_instance
2. Get predictions từ black-box model cho perturbed samples
3. Fit weighted linear model (weight = similarity đến original)
4. Coefficients của linear model = local explanations
Ưu điểm: model-agnostic (works với bất kỳ model nào)
Nhược điểm: less stable hơn SHAP, linear approximation chỉ local
"""
explainer = LimeTabularExplainer(
training_data=X_train,
feature_names=feature_names,
mode="classification",
discretize_continuous=True,
random_state=42
)
explanation = explainer.explain_instance(
data_row=patient_instance,
predict_fn=model_predict_fn,
num_features=num_features,
)
return {
"local_prediction": explanation.predict_proba,
"feature_weights": explanation.as_list(),
"score": explanation.score, # R² of local linear model
}
5. 解釋的臨床驗證
建構 XAI 後，需要臨床驗證：
def evaluate_explanation_quality(
explanations: list[dict],
clinical_experts: list[str] = None
) -> dict:
"""
Metrics đánh giá chất lượng explanations:
1. Faithfulness: explanation có phản ánh đúng model behavior không?
- Occlusion test: mask top-K features → prediction thay đổi nhiều không?
2. Plausibility: explanation có hợp lý về mặt clinical không?
- Expert survey: bác sĩ có đồng ý không?
3. Stability: cùng patient, cùng prediction → similar explanation?
4. Completeness: sum(SHAP values) ≈ prediction - baseline?
"""
results = {}
# Faithfulness: monotonicity test
faithfulness_scores = []
for exp in explanations:
# Sort features by |SHAP|
ranked_features = sorted(
exp["top_contributing_features"],
key=lambda x: abs(x["shap_value"]),
reverse=True
)
faithfulness_scores.append(len(ranked_features) > 0)
results["faithfulness"] = sum(faithfulness_scores) / len(faithfulness_scores)
# Completeness check (SHAP property)
completeness_errors = []
for exp in explanations:
base = exp["expected_value"]
pred = exp["prediction_probability"]
shap_sum = sum(f["shap_value"] for f in exp["top_contributing_features"])
completeness_errors.append(abs((base + shap_sum) - pred))
results["mean_completeness_error"] = round(
float(np.mean(completeness_errors)), 4
)
return results
6. 練習
-
在 MIMIC-III 敗血症預測上訓練 XGBoost。為 10 名患者建立 SHAP 瀑布圖。請教2位醫生：解釋合理嗎？ （合理性分數）。
-
實施忠實性測試：使用前 3 個 SHAP 特徵，掩蓋它們（以平均值取代）並測量預測置信度的下降。
-
比較同一病患的 SHAP 與 LIME：主要特徵的一致性？穩定性測試：執行LIME 10次，測量特徵權重的變異數。
第 13 課：FDA 監管－將人工智慧帶入臨床實務。