醫療人機交互層技術實施方案

一、多模態交互體系

1. 醫療語音識別引擎

# 基于Wav2Vec2的醫療ASR系統
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
import torchaudioclass MedicalASR:def __init__(self):self.processor = Wav2Vec2Processor.from_pretrained("medical-wav2vec2-base-zh-CN")self.model = Wav2Vec2ForCTC.from_pretrained("medical-wav2vec2-base-zh-CN")self.resampler = torchaudio.transforms.Resample(orig_freq=48000, new_freq=16000)# 醫療術語增強詞典self.medical_terms = {"xianweijing": "纖維鏡","ganmeisu": "干酶素"}def transcribe(self, audio_path):# 語音預處理waveform, sample_rate = torchaudio.load(audio_path)waveform = self.resampler(waveform)# 語音識別inputs = self.processor(waveform.squeeze().numpy(), sampling_rate=16000,return_tensors="pt",padding="longest")with torch.no_grad():logits = self.model(inputs.input_values,attention_mask=inputs.attention_mask).logits# 后處理優化pred_ids = torch.argmax(logits, dim=-1)text = self.processor.batch_decode(pred_ids)[0]return self._correct_medical_terms(text)def _correct_medical_terms(self, text):for term, correct in self.medical_terms.items():text = text.replace(term, correct)return text

2. 無障礙交互系統

// 基于Web Accessibility的交互方案
class AccessibilityInterface {private eyeTracker: EyeTracker;private hapticDevice: HapticController;constructor() {this.eyeTracker = new TobiiEyeX();this.hapticDevice = new HapticGlove();}setupInteraction() {// 眼動追蹤焦點管理document.addEventListener('eyegaze', (event) => {const target = document.elementFromPoint(event.detail.x, event.detail.y);if (target?.classList.contains('focusable')) {this.hapticDevice.vibrate('soft');target.dispatchEvent(new MouseEvent('hover'));}});// 語音反饋增強speechSynthesis.addEventListener('start', () => {this.hapticDevice.vibrate('double');});}// 震動編碼方案private vibrationPatterns = {confirm: [100, 50, 100],alert: [300, 100, 300],progress: [50, 50]};
}

3. 情境感知服務

// 基于藍牙信標的情境感知
public class ContextAwareService {private BeaconDetector beaconDetector;private PatientStatusPredictor predictor;public ContextAwareService() {this.beaconDetector = new AltBeaconScanner();this.predictor = new RandomForestPredictor();}public void startMonitoring(String patientId) {beaconDetector.registerListener((beacons) -> {// 實時定位處理Beacon nearest = Collections.min(beacons, Comparator.comparingDouble(Beacon::getDistance));// 階段預測double[] features = extractFeatures(nearest);int predictedStage = predictor.predictStage(features);updateUI(patientId, nearest.getLocation(), predictedStage);});}private double[] extractFeatures(Beacon beacon) {return new double[] {beacon.getDistance(),beacon.getRssi(),System.currentTimeMillis() / 1000.0};}
}

二、智能容錯機制

1. 對話狀態跟蹤(DST)

# 基于BERT的醫療對話狀態跟蹤
class MedicalDST:def __init__(self):self.tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese-medical")self.model = AutoModelForSequenceClassification.from_pretrained("bert-base-chinese-medical",num_labels=len(DIALOG_STATES))self.state_machine = {"初診": ["主訴", "病史", "檢查"],"復診": ["報告解讀", "治療方案"]}def track_state(self, dialog_history):# 合并最近3輪對話context = "\n".join(dialog_history[-3:])# 狀態分類inputs = self.tokenizer(context, return_tensors="pt",truncation=True,max_length=512)outputs = self.model(