医疗人机交互层技术实施方案

一、多模态交互体系

1. 医疗语音识别引擎

# 基于Wav2Vec2的医疗ASR系统
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
import torchaudio

class 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)
        
        # 医疗术语增强词典
        self.medical_terms = {
   
            "xianweijing": "纤维镜",
            "ganmeisu": "干酶素"
        }

    def transcribe(self, audio_path):
        # 语音预处理
        waveform, sample_rate = torchaudio.load(audio_path)
        waveform = self.resampler(waveform)
        
        # 语音识别
        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
        
        # 后处理优化
        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. 无障碍交互系统

// 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() {
   
    // 眼动追踪焦点管理
    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'));
      }
    });

    // 语音反馈增强
    speechSynthesis.addEventListener('start', () => {
   
      this.hapticDevice.vibrate('double');
    });
  }

  // 震动编码方案
  private vibrationPatterns = {
   
    confirm: [100, 50, 100],
    alert: [300, 100, 300],
    progress: [50, 50]
  };
}

3. 情境感知服务

// 基于蓝牙信标的情境感知
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) -> {
   
            // 实时定位处理
            Beacon nearest = Collections.min(beacons, 
                Comparator.comparingDouble(Beacon::getDistance));
            
            // 阶段预测
            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、智能容错机制

1. 对话状态跟踪(DST)

# 基于BERT的医疗对话状态跟踪
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 = {
   
            "初诊": ["主诉", "病史", "检查"],
            "复诊": ["报告解读", "治疗方案"]
        }
    
    def track_state(self, dialog_history):
        # 合并最近3轮对话
        context = "\n".join(dialog_history[-3:])
        
        # 状态分类
        inputs = self.tokenizer(
            context, 
            return_tensors="pt",
            truncation=True,
            max_length=512
        )
        outputs = self.model(