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醫學NER：疾病、藥物、症狀。 BioBERT、PubMedBERT。關係提取。臨床文本分類。 ICD 編碼自動化。

健康與醫療保健中的人工智慧：實戰應用

第 3 部分：臨床 NLP 和基因組學 AI

亞洲開發網

80% 以上的醫療數據都是文字形式—臨床記錄、實驗室報告、出院總結。本文是開發該寶藏的關鍵。

1. 為什麼臨床NLP與常規NLP不同？

臨床文件：

"Bn nam 58t, tiền sử THA, ĐTĐ typ 2, vào viện vì ĐAU NGỰC trái lan lên vai,
kèm khó thở, không sốt. SpO2 94%, M 110 bpm, ECG bình thường.
TnI 0.8 ng/mL (ref < 0.04)."

問題：

密集縮寫：Hypertension、ECG、ECG、TnI
陰性：「不發燒」－需檢測陰性
時間性：“過去”與“當前”
追蹤上下文：ECG 中的「正常」≠ 正常 SpO2

2. 使用 BioBERT 實現 NER Health

2.1。領域模型

型號	訓練資料	最適合
BioBERT	考試研究+PMC	研究 NER
PubMedBERT	PubMed（草稿）	最佳 BioNLP
臨床BERT	MIMIC-III	臨床筆記
Bio_ClinicalBERT	PubMed + MIMIC	普通醫學NLP

2.2。代幣分類（NER）

from transformers import (
    AutoTokenizer, AutoModelForTokenClassification,
    TrainingArguments, Trainer, DataCollatorForTokenClassification
)
import torch

LABEL_LIST = [
    "O",
    "B-DISEASE", "I-DISEASE",
    "B-DRUG", "I-DRUG",
    "B-SYMPTOM", "I-SYMPTOM",
    "B-ANATOMICAL", "I-ANATOMICAL",
    "B-LAB_VALUE", "I-LAB_VALUE",
]
LABEL2ID = {l: i for i, l in enumerate(LABEL_LIST)}
ID2LABEL = {i: l for l, i in LABEL2ID.items()}

MODEL_NAME = "dmis-lab/biobert-base-cased-v1.2"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForTokenClassification.from_pretrained(
    MODEL_NAME,
    num_labels=len(LABEL_LIST),
    id2label=ID2LABEL,
    label2id=LABEL2ID
)

def tokenize_and_align_labels(examples):
    """Align NER labels với BERT wordpieces."""
    tokenized = tokenizer(
        examples["tokens"],
        truncation=True,
        is_split_into_words=True,
        max_length=512
    )

    all_labels = []
    for i, label_ids in enumerate(examples["ner_tags"]):
        word_ids = tokenized.word_ids(batch_index=i)
        previous_word_idx = None
        label_ids_aligned = []

        for word_idx in word_ids:
            if word_idx is None:
                label_ids_aligned.append(-100)
            elif word_idx != previous_word_idx:
                label_ids_aligned.append(label_ids[word_idx])
            else:
                # Wordpiece continuation: dùng I- label
                current_label = LABEL_LIST[label_ids[word_idx]]
                if current_label.startswith("B-"):
                    inside_label = "I-" + current_label[2:]
                    label_ids_aligned.append(LABEL2ID.get(inside_label, -100))
                else:
                    label_ids_aligned.append(-100)
            previous_word_idx = word_idx

        all_labels.append(label_ids_aligned)

    tokenized["labels"] = all_labels
    return tokenized

2.3。處理否定和暫時性

NEGATION_TERMS = [
    "không", "chưa", "phủ nhận", "không ghi nhận", "âm tính",
    "no", "not", "without", "denies", "absent", "negative", "ruled out"
]

TEMPORAL_PATTERNS = {
    "past": ["tiền sử", "trước đây", "cũ", "đã", "history of", "prior"],
    "family": ["gia đình", "bố mẹ", "family history"],
    "current": ["hiện tại", "hiện", "currently", "presenting with"],
}

def detect_negation_scope(text: str, entity_start: int, window: int = 5) -> bool:
    """Check 5 words trước entity xem có negation term không."""
    tokens = text[:entity_start].split()
    window_tokens = [t.lower() for t in tokens[-window:]]
    return any(neg in window_tokens for neg in NEGATION_TERMS)

def classify_temporality(text: str, entity_start: int) -> str:
    before = text[:entity_start].lower()[-80:]
    for category, patterns in TEMPORAL_PATTERNS.items():
        if any(p in before for p in patterns):
            return category
    return "current"

def extract_entities_with_context(text: str, ner_pipeline) -> list[dict]:
    """NER + negation + temporality."""
    raw_entities = ner_pipeline(text, aggregation_strategy="max")
    enriched = []
    for e in raw_entities:
        enriched.append({
            "text": e["word"],
            "label": e["entity_group"],
            "confidence": round(e["score"], 3),
            "is_negated": detect_negation_scope(text, e["start"]),
            "temporality": classify_temporality(text, e["start"]),
        })
    return enriched

3. ICD-10 自動編碼

from transformers import AutoModelForSequenceClassification, pipeline

class ICD10AutoCoder:
    """
    Multi-label classification: discharge summary → ICD-10 codes.
    Dataset: MIMIC-III (top 50 codes)
    Metric: micro-AUC, P@8, R@8
    """
    def __init__(self, model_path: str, icd_list: list[str]):
        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
        self.model = AutoModelForSequenceClassification.from_pretrained(model_path)
        self.icd_list = icd_list
        self.model.eval()

    def predict(self, discharge_summary: str, top_k: int = 10) -> list[dict]:
        inputs = self.tokenizer(
            discharge_summary,
            return_tensors="pt",
            max_length=4096,   # Dùng Longformer/BigBird cho long docs
            truncation=True,
        )
        with torch.no_grad():
            logits = self.model(**inputs).logits
            probs = torch.sigmoid(logits).squeeze()

        top_k_indices = torch.topk(probs, k=min(top_k, len(self.icd_list))).indices
        return [
            {
                "icd_code": self.icd_list[i],
                "confidence": round(float(probs[i]), 3)
            }
            for i in top_k_indices.tolist()
        ]

4.NER評估

from seqeval.metrics import classification_report, f1_score

def evaluate_ner(all_true_labels: list, all_pred_labels: list):
    """
    seqeval dùng entity-level F1 (strict span match).
    Khác với token-level accuracy — entity-level khắt khe hơn nhiều.
    """
    print(classification_report(all_true_labels, all_pred_labels))
    micro_f1 = f1_score(all_true_labels, all_pred_labels, average="micro")
    macro_f1 = f1_score(all_true_labels, all_pred_labels, average="macro")
    print(f"Micro F1: {micro_f1:.4f} | Macro F1: {macro_f1:.4f}")

5. 練習

1.下載I2B2 2010 NER資料集。微調 ClinicalBERT 和 PubMedBERT。比較每種實體類型（疾病/藥物/治療）的 F1 分數。 2. 實作否定評估：註 100 個句子，統計 False Positive（預測為否定但沒有）和 False Negative。 3. 在 MIMIC-III 上建構 ICD-10 top-50 分類器。目標精度@5 > 0.70。

第 8 課：使用 RAG + LLM 建造 醫療問答聊天機器人。

第 7 課：臨床 NLP — 分析醫療紀錄