Phase 3 introduces artificial intelligence and machine learning to create adaptive, personalized therapeutic experiences. Unlike static content delivery, ML-powered systems learn from each interaction to optimize content, timing, and approach for maximum clinical benefit.
| Application | ML Technique | Input Data | Predicted Outcome |
|---|---|---|---|
| Treatment Response Prediction | Random Forest, XGBoost | Demographics, symptoms, history | Likelihood of response to specific therapy |
| Content Personalization | Collaborative Filtering, Neural Networks | User engagement patterns, preferences | Optimal content recommendations |
| Relapse Prediction | LSTM, Time Series Analysis | Symptom trajectories, behavioral patterns | Probability of symptom recurrence |
| Sentiment Analysis | NLP, Transformer Models (BERT) | Text entries, journal content | Emotional state, crisis risk |
| Engagement Optimization | Reinforcement Learning | Notification timing, content type | Optimal intervention timing |
// ML Model for Treatment Selection
class TreatmentPredictionModel {
private model: RandomForestClassifier;
private featureExtractor: FeatureExtractor;
async predictOptimalTreatment(
patient: PatientData
): Promise {
// Extract features
const features = await this.featureExtractor.extract({
demographics: {
age: patient.age,
gender: patient.gender,
educationLevel: patient.educationLevel
},
clinical: {
phq9Score: patient.baselinePHQ9,
gad7Score: patient.baselineGAD7,
primaryDiagnosis: patient.diagnosis,
comorbidities: patient.comorbidities,
previousTreatments: patient.treatmentHistory
},
behavioral: {
engagementHistory: patient.platformEngagement,
preferredModality: patient.preferences.learningStyle,
deviceUsage: patient.deviceMetrics
},
social: {
socialSupport: patient.socialSupport,
employmentStatus: patient.employment,
livingArrangement: patient.living
}
});
// Predict treatment efficacy for each modality
const predictions = await this.model.predict(features);
// Results: probability of 50%+ symptom improvement
const results = {
CBT: predictions[0], // 0.78 (78% likelihood)
DBT: predictions[1], // 0.45
ACT: predictions[2], // 0.82 (best match!)
IPT: predictions[3], // 0.61
MBCT: predictions[4] // 0.55
};
// Select best treatment
const recommended = Object.entries(results)
.sort(([,a], [,b]) => b - a)[0];
return {
primaryRecommendation: recommended[0],
confidence: recommended[1],
alternativeOptions: this.getAlternatives(results),
reasoning: this.generateExplanation(features, recommended[0]),
expectedOutcome: this.predictOutcome(features, recommended[0])
};
}
private generateExplanation(
features: Features,
treatment: string
): string {
// Explainable AI - SHAP values for feature importance
const shapValues = this.model.explain(features, treatment);
const topFactors = shapValues
.sort((a, b) => Math.abs(b.value) - Math.abs(a.value))
.slice(0, 3)
.map(s => s.feature);
return `${treatment} recommended based on: ${topFactors.join(', ')}`;
}
}
// Training Pipeline
class ModelTrainingPipeline {
async trainTreatmentPredictionModel(): Promise {
// Load historical treatment outcome data
const data = await this.loadTrainingData({
minPatients: 10000,
outcomeDefinition: '50% symptom reduction',
followUpPeriod: 12 // weeks
});
// Feature engineering
const features = this.engineerFeatures(data);
// Train-test split (80-20)
const [trainSet, testSet] = this.splitData(features, 0.8);
// Hyperparameter tuning with cross-validation
const bestParams = await this.gridSearch({
n_estimators: [100, 200, 300],
max_depth: [10, 20, 30, null],
min_samples_split: [2, 5, 10],
class_weight: ['balanced', null]
}, trainSet, cv=5);
// Train final model
const model = new RandomForestClassifier(bestParams);
await model.fit(trainSet.X, trainSet.y);
// Evaluate performance
const metrics = await this.evaluate(model, testSet);
console.log('Model Performance:', {
accuracy: metrics.accuracy, // 0.85
precision: metrics.precision, // 0.83
recall: metrics.recall, // 0.81
f1Score: metrics.f1, // 0.82
auc: metrics.aucRoc // 0.89
});
// Validate on held-out set
const validation = await this.validateClinically(model);
if (validation.acceptable) {
return model.save();
} else {
throw new Error('Model failed clinical validation');
}
}
} Traditional digital therapeutics deliver the same content to all users in the same sequence. Adaptive systems dynamically adjust content difficulty, pacing, and modality based on individual learner profiles and real-time performance.
| Dimension | Adaptation Strategy | Data Sources | Example Adjustment |
|---|---|---|---|
| Difficulty Level | Item Response Theory | Quiz performance, time spent | Simplify cognitive exercises if struggling |
| Pacing | User-controlled with suggestions | Completion rates, engagement | Suggest slower pace if feeling overwhelmed |
| Content Modality | Preference learning | Media engagement patterns | Prefer video vs. text vs. audio |
| Exercise Selection | Collaborative filtering | Similar user preferences | Recommend exercises helpful to similar users |
| Timing | Reinforcement learning | Notification response rates | Send reminders at optimal times |
| Language Complexity | Readability analysis | Reading level, comprehension | Adjust vocabulary to literacy level |
class AdaptiveContentEngine {
private userModel: UserLearningModel;
private contentLibrary: ContentRepository;
async getNextActivity(
userId: string,
sessionContext: SessionContext
): Promise {
// Get user's current state
const userState = await this.userModel.getCurrentState(userId);
// Factors to consider:
// 1. Learning objectives not yet mastered
const unmastered = this.identifyGaps(
userState.learningObjectives,
userState.assessmentResults
);
// 2. User's cognitive load (avoid overwhelm)
const cognitiveLoad = this.estimateCognitiveLoad(
userState.recentPerformance,
sessionContext.timeOfDay,
sessionContext.sessionNumber
);
// 3. Preferred learning modalities
const preferences = userState.modalityPreferences;
// 4. Activities that helped similar users
const collaborative = await this.getCollaborativeRecommendations(
userId,
unmastered[0] // highest priority gap
);
// 5. Spaced repetition for retention
const dueForReview = this.getSpacedRepetitionItems(
userState.itemHistory
);
// Score candidate activities
const candidates = await this.contentLibrary.search({
objectives: unmastered,
maxDifficulty: this.getDifficultyThreshold(cognitiveLoad),
modalities: preferences.top3,
excludeRecent: userState.recentActivities
});
const scored = candidates.map(activity => ({
activity,
score: this.scoreActivity(
activity,
unmastered,
preferences,
collaborative,
dueForReview,
cognitiveLoad
)
}));
// Select highest-scoring activity
const selected = scored.sort((a, b) => b.score - a.score)[0];
// Log for model improvement
await this.logRecommendation({
userId,
activity: selected.activity,
context: sessionContext,
reasoning: selected.reasoning
});
return selected.activity;
}
private scoreActivity(
activity: Activity,
gaps: LearningGap[],
preferences: Preferences,
collaborative: Recommendation[],
dueForReview: Item[],
cognitiveLoad: number
): number {
let score = 0;
// Address highest-priority gap: +50 points
if (activity.addresses(gaps[0])) score += 50;
// Matches preferred modality: +30 points
if (preferences.includes(activity.modality)) score += 30;
// Recommended by collaborative filtering: +20 points
const collab = collaborative.find(c => c.activityId === activity.id);
if (collab) score += 20 * collab.confidence;
// Spaced repetition due: +25 points
if (dueForReview.some(i => activity.reviews(i))) score += 25;
// Appropriate difficulty: +15 points (or penalty)
const difficultyMatch = this.assessDifficultyMatch(
activity.difficulty,
cognitiveLoad
);
score += difficultyMatch * 15;
// Novelty bonus: +10 points (avoid boredom)
if (!activity.recentlyShown) score += 10;
return score;
}
} NLP enables automated analysis of patient text entries (journal entries, chat messages, thought records) to detect emotional states, identify concerning patterns, and provide real-time support.
class NLPSentimentAnalyzer {
private bertModel: TransformerModel;
private emotionClassifier: EmotionModel;
private crisisDetector: CrisisModel;
async analyzeText(text: string): Promise {
// Parallel processing of multiple models
const [sentiment, emotions, crisis, topics] = await Promise.all([
this.analyzeSentiment(text),
this.detectEmotions(text),
this.assessCrisisRisk(text),
this.extractTopics(text)
]);
return {
sentiment: {
polarity: sentiment.polarity, // -1 to +1
score: sentiment.confidence, // 0 to 1
label: sentiment.label // positive/negative/neutral
},
emotions: {
joy: emotions.joy,
sadness: emotions.sadness,
anger: emotions.anger,
fear: emotions.fear,
surprise: emotions.surprise,
disgust: emotions.disgust
},
crisis: {
riskLevel: crisis.level, // none/low/medium/high/severe
confidence: crisis.confidence,
triggers: crisis.detectedKeywords,
recommendedActions: crisis.actions
},
topics: topics.map(t => ({
topic: t.label,
confidence: t.score,
keywords: t.keywords
})),
metadata: {
wordCount: text.split(/\s+/).length,
readingLevel: this.calculateReadability(text),
processingTime: Date.now()
}
};
}
private async analyzeSentiment(text: string): Promise {
// Use pre-trained BERT model fine-tuned on mental health text
const inputs = await this.bertModel.tokenize(text);
const outputs = await this.bertModel.predict(inputs);
// Get sentiment logits
const [negative, neutral, positive] = outputs.logits;
// Apply softmax
const probs = this.softmax([negative, neutral, positive]);
// Determine polarity (-1 to +1 scale)
const polarity = (probs[2] - probs[0]);
return {
polarity,
confidence: Math.max(...probs),
label: probs[2] > probs[0] ?
(probs[2] > probs[1] ? 'positive' : 'neutral') :
'negative',
distribution: {
negative: probs[0],
neutral: probs[1],
positive: probs[2]
}
};
}
private async detectEmotions(text: string): Promise {
// Multi-label emotion classification
const features = await this.extractFeatures(text);
const predictions = await this.emotionClassifier.predict(features);
return {
joy: predictions[0],
sadness: predictions[1],
anger: predictions[2],
fear: predictions[3],
surprise: predictions[4],
disgust: predictions[5],
primary: this.getPrimaryEmotion(predictions)
};
}
} By analyzing patterns in patient data over time, ML models can predict treatment outcomes weeks or months in advance, enabling proactive interventions when patients are at risk of poor outcomes.
| Predicted Outcome | Prediction Horizon | Model Accuracy | Clinical Utility |
|---|---|---|---|
| Treatment Response | 12 weeks | 85% AUC | Guide treatment selection |
| Dropout Risk | 2 weeks | 82% AUC | Retention interventions |
| Relapse Probability | 1-3 months | 78% AUC | Maintenance planning |
| Crisis Events | 1-7 days | 89% sensitivity | Preventive outreach |
| Optimal Discharge Timing | Ongoing | 81% accuracy | Resource optimization |