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modules/brain_mapper.py

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+import numpy as np
+import plotly.graph_objects as go
+import mne
+from typing import Dict, Optional, Tuple
+import plotly.express as px
+import networkx as nx
+
+class BrainMapper:
+    def __init__(self):
+        self.montage = mne.channels.make_standard_montage('standard_1020')
+        self._initialize_coordinates()
+    
+    def _initialize_coordinates(self):
+        """Initialize electrode coordinates from standard montage"""
+        pos = self.montage.get_positions()
+        self.coords = pos['ch_pos']
+        
+        # Extract x, y, z coordinates
+        self.ch_names = list(self.coords.keys())
+        self.x_coords = np.array([self.coords[ch][0] for ch in self.ch_names])
+        self.y_coords = np.array([self.coords[ch][1] for ch in self.ch_names])
+        self.z_coords = np.array([self.coords[ch][2] for ch in self.ch_names])
+    
+    def create_visualization(self, features: Dict, map_type: str = "2D Topographic") -> go.Figure:
+        """Create brain visualization based on the specified type"""
+        if map_type == "2D Topographic":
+            return self._create_topographic_map(features)
+        elif map_type == "3D Surface":
+            return self._create_3d_surface(features)
+        elif map_type == "Connectivity":
+            return self._create_connectivity_map(features)
+        else:
+            raise ValueError(f"Unsupported map type: {map_type}")
+    
+    def _create_topographic_map(self, features: Dict) -> go.Figure:
+        """Create 2D topographic map of brain activity"""
+        # Extract band powers for visualization
+        band_powers = features['band_powers']
+        
+        # Create figure with subplots for each frequency band
+        fig = go.Figure()
+        
+        for band_name, powers in band_powers.items():
+            # Create interpolated grid
+            xi = np.linspace(min(self.x_coords), max(self.x_coords), 100)
+            yi = np.linspace(min(self.y_coords), max(self.y_coords), 100)
+            xi, yi = np.meshgrid(xi, yi)
+            
+            # Add contour plot for each band
+            fig.add_trace(go.Contour(
+                x=xi[0],
+                y=yi[:, 0],
+                z=powers.reshape(xi.shape),
+                name=band_name,
+                colorscale='Viridis',
+                showscale=True,
+                visible=(band_name == 'alpha')  # Show alpha band by default
+            ))
+            
+            # Add scatter plot for electrode positions
+            fig.add_trace(go.Scatter(
+                x=self.x_coords,
+                y=self.y_coords,
+                mode='markers+text',
+                text=self.ch_names,
+                textposition="top center",
+                name='Electrodes',
+                marker=dict(size=10, color='black'),
+                visible=(band_name == 'alpha')
+            ))
+        
+        # Update layout
+        fig.update_layout(
+            title="Brain Activity Topographic Map",
+            xaxis_title="X Position",
+            yaxis_title="Y Position",
+            showlegend=True,
+            updatemenus=[{
+                'buttons': [
+                    {'label': band,
+                     'method': 'update',
+                     'args': [{'visible': [i == j for i in range(len(band_powers)*2) for _ in range(2)]}]}
+                    for j, band in enumerate(band_powers.keys())
+                ],
+                'direction': 'down',
+                'showactive': True,
+            }]
+        )
+        
+        return fig
+    
+    def _create_3d_surface(self, features: Dict) -> go.Figure:
+        """Create 3D surface plot of brain activity"""
+        # Create 3D surface using electrode positions
+        fig = go.Figure()
+        
+        # Add surface plot
+        fig.add_trace(go.Surface(
+            x=self.x_coords.reshape(-1, 1),
+            y=self.y_coords.reshape(-1, 1),
+            z=features['statistics']['mean'].reshape(-1, 1),
+            colorscale='Viridis',
+            name='Brain Activity'
+        ))
+        
+        # Add scatter plot for electrode positions
+        fig.add_trace(go.Scatter3d(
+            x=self.x_coords,
+            y=self.y_coords,
+            z=self.z_coords,
+            mode='markers+text',
+            text=self.ch_names,
+            marker=dict(size=5, color='red'),
+            name='Electrodes'
+        ))
+        
+        # Update layout
+        fig.update_layout(
+            title="3D Brain Activity Surface",
+            scene=dict(
+                xaxis_title="X Position",
+                yaxis_title="Y Position",
+                zaxis_title="Activity Level",
+                camera=dict(
+                    up=dict(x=0, y=0, z=1),
+                    center=dict(x=0, y=0, z=0),
+                    eye=dict(x=1.5, y=1.5, z=1.5)
+                )
+            )
+        )
+        
+        return fig
+    
+    def _create_connectivity_map(self, features: Dict) -> go.Figure:
+        """Create brain connectivity visualization"""
+        # Extract connectivity matrix
+        connectivity = features['connectivity']['correlation']
+        
+        # Create graph
+        G = nx.from_numpy_array(connectivity)
+        pos = nx.spring_layout(G, k=1, iterations=50)
+        
+        # Create edge trace
+        edge_x = []
+        edge_y = []
+        for edge in G.edges():
+            x0, y0 = pos[edge[0]]
+            x1, y1 = pos[edge[1]]
+            edge_x.extend([x0, x1, None])
+            edge_y.extend([y0, y1, None])
+            
+        edge_trace = go.Scatter(
+            x=edge_x, y=edge_y,
+            line=dict(width=0.5, color='#888'),
+            hoverinfo='none',
+            mode='lines')
+        
+        # Create node trace
+        node_x = []
+        node_y = []
+        for node in G.nodes():
+            x, y = pos[node]
+            node_x.append(x)
+            node_y.append(y)
+            
+        node_trace = go.Scatter(
+            x=node_x, y=node_y,
+            mode='markers+text',
+            hoverinfo='text',
+            text=self.ch_names,
+            marker=dict(
+                showscale=True,
+                colorscale='YlOrRd',
+                size=10,
+                colorbar=dict(
+                    thickness=15,
+                    title='Node Connections',
+                    xanchor='left',
+                    titleside='right'
+                )
+            )
+        )
+        
+        # Color node points by the number of connections
+        node_adjacencies = []
+        for node, adjacencies in enumerate(G.adjacency()):
+            node_adjacencies.append(len(adjacencies[1]))
+        node_trace.marker.color = node_adjacencies
+        
+        # Create figure
+        fig = go.Figure(data=[edge_trace, node_trace],
+                       layout=go.Layout(
+                           title='Brain Connectivity Network',
+                           showlegend=False,
+                           hovermode='closest',
+                           margin=dict(b=20,l=5,r=5,t=40),
+                           xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
+                           yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)
+                       ))
+        
+        return fig 

modules/clinical_analyzer.py

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+import numpy as np
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
+from typing import Dict, List, Optional
+import torch
+import json
+
+class ClinicalAnalyzer:
+    def __init__(self):
+        self.initialize_models()
+        self.condition_patterns = self._load_condition_patterns()
+        
+    def initialize_models(self):
+        """Initialize transformer models for clinical analysis"""
+        try:
+            # Clinical text analysis model
+            self.clinical_tokenizer = AutoTokenizer.from_pretrained(
+                "microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext"
+            )
+            self.clinical_model = AutoModelForSequenceClassification.from_pretrained(
+                "microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext"
+            )
+            
+            # Mental health assessment pipeline
+            self.mental_health_pipeline = pipeline(
+                "text-classification",
+                model="microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext",
+                tokenizer="microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext"
+            )
+        except Exception as e:
+            print(f"Error initializing models: {str(e)}")
+            # Fallback to rule-based analysis if models fail to load
+            self.clinical_model = None
+            self.clinical_tokenizer = None
+            self.mental_health_pipeline = None
+    
+    def _load_condition_patterns(self) -> Dict:
+        """Load predefined patterns for mental health conditions"""
+        return {
+            'depression': {
+                'eeg_patterns': {
+                    'alpha_asymmetry': True,
+                    'theta_increase': True,
+                    'beta_decrease': True
+                },
+                'keywords': [
+                    'depressed mood', 'loss of interest', 'fatigue',
+                    'sleep disturbance', 'concentration problems'
+                ]
+            },
+            'anxiety': {
+                'eeg_patterns': {
+                    'beta_increase': True,
+                    'alpha_decrease': True,
+                    'high_coherence': True
+                },
+                'keywords': [
+                    'anxiety', 'worry', 'restlessness', 'tension',
+                    'panic', 'nervousness'
+                ]
+            },
+            'ptsd': {
+                'eeg_patterns': {
+                    'alpha_suppression': True,
+                    'theta_increase': True,
+                    'beta_asymmetry': True
+                },
+                'keywords': [
+                    'trauma', 'flashbacks', 'nightmares', 'avoidance',
+                    'hypervigilance', 'startle response'
+                ]
+            }
+        }
+    
+    def analyze(self, features: Dict, clinical_notes: str) -> Dict:
+        """Perform comprehensive clinical analysis"""
+        analysis_results = {
+            'eeg_analysis': self._analyze_eeg_patterns(features),
+            'text_analysis': self._analyze_clinical_text(clinical_notes),
+            'condition_probabilities': self._calculate_condition_probabilities(
+                features, clinical_notes
+            ),
+            'severity_assessment': self._assess_severity(features, clinical_notes),
+            'recommendations': self._generate_recommendations(features, clinical_notes)
+        }
+        return analysis_results
+    
+    def _analyze_eeg_patterns(self, features: Dict) -> Dict:
+        """Analyze EEG patterns for clinical significance"""
+        eeg_analysis = {}
+        
+        # Analyze band power distributions
+        band_powers = features['band_powers']
+        eeg_analysis['band_power_analysis'] = {
+            band: {
+                'mean': float(np.mean(powers)),
+                'std': float(np.std(powers)),
+                'clinical_significance': self._assess_band_significance(band, powers)
+            }
+            for band, powers in band_powers.items()
+        }
+        
+        # Analyze connectivity patterns
+        connectivity = features['connectivity']
+        eeg_analysis['connectivity_analysis'] = {
+            'global_connectivity': float(np.mean(connectivity['correlation'])),
+            'asymmetry_index': self._calculate_asymmetry_index(features)
+        }
+        
+        return eeg_analysis
+    
+    def _analyze_clinical_text(self, clinical_notes: str) -> Dict:
+        """Analyze clinical notes using NLP"""
+        if not clinical_notes:
+            return {'error': 'No clinical notes provided'}
+        
+        try:
+            if self.mental_health_pipeline:
+                # Use transformer model for analysis
+                results = self.mental_health_pipeline(clinical_notes)
+                text_analysis = {
+                    'sentiment': results[0]['label'],
+                    'confidence': float(results[0]['score'])
+                }
+            else:
+                # Fallback to keyword-based analysis
+                text_analysis = self._keyword_based_analysis(clinical_notes)
+            
+            # Extract symptoms and severity
+            text_analysis['identified_symptoms'] = self._extract_symptoms(clinical_notes)
+            text_analysis['risk_factors'] = self._identify_risk_factors(clinical_notes)
+            
+            return text_analysis
+            
+        except Exception as e:
+            return {'error': f'Text analysis failed: {str(e)}'}
+    
+    def _calculate_condition_probabilities(
+        self, features: Dict, clinical_notes: str
+    ) -> Dict:
+        """Calculate probabilities for different mental health conditions"""
+        probabilities = {}
+        
+        for condition, patterns in self.condition_patterns.items():
+            # Calculate EEG pattern match score
+            eeg_score = self._calculate_eeg_pattern_match(
+                features, patterns['eeg_patterns']
+            )
+            
+            # Calculate text pattern match score
+            text_score = self._calculate_text_pattern_match(
+                clinical_notes, patterns['keywords']
+            )
+            
+            # Combine scores with weighted average
+            combined_score = 0.6 * eeg_score + 0.4 * text_score
+            probabilities[condition] = float(combined_score)
+        
+        return probabilities
+    
+    def _assess_severity(self, features: Dict, clinical_notes: str) -> Dict:
+        """Assess the severity of identified conditions"""
+        severity = {
+            'overall_severity': self._calculate_overall_severity(features, clinical_notes),
+            'domain_severity': {
+                'cognitive': self._assess_cognitive_severity(features),
+                'emotional': self._assess_emotional_severity(features, clinical_notes),
+                'behavioral': self._assess_behavioral_severity(clinical_notes)
+            },
+            'risk_level': self._assess_risk_level(features, clinical_notes)
+        }
+        return severity
+    
+    def _generate_recommendations(self, features: Dict, clinical_notes: str) -> List[str]:
+        """Generate clinical recommendations based on analysis"""
+        recommendations = []
+        
+        # Analyze severity and conditions
+        severity = self._assess_severity(features, clinical_notes)
+        conditions = self._calculate_condition_probabilities(features, clinical_notes)
+        
+        # Generate general recommendations
+        if severity['overall_severity'] > 0.7:
+            recommendations.append("Immediate clinical intervention recommended")
+        elif severity['overall_severity'] > 0.4:
+            recommendations.append("Regular clinical monitoring recommended")
+        
+        # Condition-specific recommendations
+        for condition, probability in conditions.items():
+            if probability > 0.6:
+                recommendations.extend(
+                    self._get_condition_specific_recommendations(condition)
+                )
+        
+        return recommendations
+    
+    def _calculate_eeg_pattern_match(self, features: Dict, patterns: Dict) -> float:
+        """Calculate how well EEG features match condition patterns"""
+        match_scores = []
+        
+        for pattern, expected in patterns.items():
+            if pattern == 'alpha_asymmetry':
+                score = self._check_alpha_asymmetry(features)
+            elif pattern == 'beta_increase':
+                score = self._check_beta_increase(features)
+            elif pattern == 'theta_increase':
+                score = self._check_theta_increase(features)
+            else:
+                score = 0.5  # Default score for unknown patterns
+            
+            match_scores.append(score if expected else 1 - score)
+        
+        return np.mean(match_scores) if match_scores else 0.0
+    
+    def _calculate_text_pattern_match(self, text: str, keywords: List[str]) -> float:
+        """Calculate how well clinical notes match condition keywords"""
+        if not text:
+            return 0.0
+        
+        text = text.lower()
+        matched_keywords = sum(1 for keyword in keywords if keyword.lower() in text)
+        return matched_keywords / len(keywords)
+    
+    def _calculate_asymmetry_index(self, features: Dict) -> float:
+        """Calculate brain asymmetry index from EEG features"""
+        try:
+            # Calculate alpha asymmetry between left and right hemispheres
+            alpha_powers = features['band_powers']['alpha']
+            left_channels = alpha_powers[:len(alpha_powers)//2]
+            right_channels = alpha_powers[len(alpha_powers)//2:]
+            
+            asymmetry = np.log(np.mean(right_channels)) - np.log(np.mean(left_channels))
+            return float(asymmetry)
+        except:
+            return 0.0
+    
+    def _assess_band_significance(self, band: str, powers: np.ndarray) -> str:
+        """Assess clinical significance of frequency band power"""
+        mean_power = np.mean(powers)
+        if band == 'alpha':
+            if mean_power < 0.3:
+                return "Significantly reduced alpha power"
+            elif mean_power > 0.7:
+                return "Elevated alpha power"
+        elif band == 'beta':
+            if mean_power > 0.7:
+                return "Elevated beta power - possible anxiety"
+        elif band == 'theta':
+            if mean_power > 0.6:
+                return "Elevated theta power - possible cognitive issues"
+        
+        return "Within normal range"
+    
+    def _get_condition_specific_recommendations(self, condition: str) -> List[str]:
+        """Get specific recommendations for identified conditions"""
+        recommendations = {
+            'depression': [
+                "Consider cognitive behavioral therapy",
+                "Evaluate need for antidepressant medication",
+                "Recommend regular physical activity",
+                "Implement sleep hygiene practices"
+            ],
+            'anxiety': [
+                "Consider anxiety-focused psychotherapy",
+                "Evaluate need for anti-anxiety medication",
+                "Recommend relaxation techniques",
+                "Practice mindfulness meditation"
+            ],
+            'ptsd': [
+                "Consider trauma-focused therapy",
+                "Evaluate need for PTSD-specific medication",
+                "Implement grounding techniques",
+                "Develop safety and coping plans"
+            ]
+        }
+        return recommendations.get(condition, []) 

modules/eeg_processor.py

@@ -0,0 +1,138 @@
+import mne
+import numpy as np
+from scipy import signal
+from typing import Dict, List, Tuple, Optional
+
+class EEGProcessor:
+    def __init__(self):
+        self.sfreq = 250  # Default sampling frequency
+        self.freq_bands = {
+            'delta': (0.5, 4),
+            'theta': (4, 8),
+            'alpha': (8, 13),
+            'beta': (13, 30),
+            'gamma': (30, 50)
+        }
+    
+    def preprocess(self, raw: mne.io.Raw) -> mne.io.Raw:
+        """Preprocess raw EEG data"""
+        # Set montage if not present
+        if raw.get_montage() is None:
+            raw.set_montage('standard_1020')
+        
+        # Basic preprocessing pipeline
+        raw_processed = raw.copy()
+        
+        # Filter data
+        raw_processed.filter(l_freq=0.5, h_freq=50.0)
+        raw_processed.notch_filter(freqs=50)  # Remove power line noise
+        
+        # Detect and interpolate bad channels
+        raw_processed.interpolate_bads()
+        
+        # Apply ICA for artifact removal
+        ica = mne.preprocessing.ICA(n_components=0.95, random_state=42)
+        ica.fit(raw_processed)
+        
+        # Detect and remove eye blinks
+        eog_indices, eog_scores = ica.find_bads_eog(raw_processed)
+        if eog_indices:
+            ica.exclude = eog_indices
+            ica.apply(raw_processed)
+        
+        return raw_processed
+    
+    def extract_features(self, raw: mne.io.Raw) -> Dict:
+        """Extract relevant features from preprocessed EEG data"""
+        features = {}
+        
+        # Get data and times
+        data, times = raw.get_data(return_times=True)
+        
+        # Calculate power spectral density
+        psds, freqs = mne.time_frequency.psd_welch(
+            raw,
+            fmin=0.5,
+            fmax=50.0,
+            n_fft=int(raw.info['sfreq'] * 4),
+            n_overlap=int(raw.info['sfreq'] * 2)
+        )
+        
+        # Extract band powers
+        features['band_powers'] = self._calculate_band_powers(psds, freqs)
+        
+        # Calculate connectivity metrics
+        features['connectivity'] = self._calculate_connectivity(data)
+        
+        # Extract statistical features
+        features['statistics'] = self._calculate_statistics(data)
+        
+        return features
+    
+    def _calculate_band_powers(self, psds: np.ndarray, freqs: np.ndarray) -> Dict:
+        """Calculate power in different frequency bands"""
+        band_powers = {}
+        
+        for band_name, (fmin, fmax) in self.freq_bands.items():
+            # Find frequencies that fall within band
+            freq_mask = (freqs >= fmin) & (freqs <= fmax)
+            
+            # Calculate average power in band
+            band_power = np.mean(psds[:, freq_mask], axis=1)
+            band_powers[band_name] = band_power
+        
+        return band_powers
+    
+    def _calculate_connectivity(self, data: np.ndarray) -> Dict:
+        """Calculate connectivity metrics between channels"""
+        n_channels = data.shape[0]
+        connectivity = {
+            'correlation': np.corrcoef(data),
+            'coherence': np.zeros((n_channels, n_channels))
+        }
+        
+        # Calculate coherence between all channel pairs
+        for i in range(n_channels):
+            for j in range(i + 1, n_channels):
+                f, coh = signal.coherence(data[i], data[j], fs=self.sfreq)
+                connectivity['coherence'][i, j] = np.mean(coh)
+                connectivity['coherence'][j, i] = connectivity['coherence'][i, j]
+        
+        return connectivity
+    
+    def _calculate_statistics(self, data: np.ndarray) -> Dict:
+        """Calculate statistical features for each channel"""
+        stats = {
+            'mean': np.mean(data, axis=1),
+            'std': np.std(data, axis=1),
+            'skewness': self._calculate_skewness(data),
+            'kurtosis': self._calculate_kurtosis(data),
+            'hjorth': self._calculate_hjorth_parameters(data)
+        }
+        return stats
+    
+    def _calculate_skewness(self, data: np.ndarray) -> np.ndarray:
+        """Calculate skewness for each channel"""
+        return np.array([signal.skew(channel) for channel in data])
+    
+    def _calculate_kurtosis(self, data: np.ndarray) -> np.ndarray:
+        """Calculate kurtosis for each channel"""
+        return np.array([signal.kurtosis(channel) for channel in data])
+    
+    def _calculate_hjorth_parameters(self, data: np.ndarray) -> Dict:
+        """Calculate Hjorth parameters (activity, mobility, complexity)"""
+        activity = np.var(data, axis=1)
+        
+        # First derivative variance
+        diff1 = np.diff(data, axis=1)
+        mobility = np.sqrt(np.var(diff1, axis=1) / activity)
+        
+        # Second derivative variance
+        diff2 = np.diff(diff1, axis=1)
+        complexity = np.sqrt(np.var(diff2, axis=1) / np.var(diff1, axis=1)) / mobility
+        
+        return {
+            'activity': activity,
+            'mobility': mobility,
+            'complexity': complexity
+        } 

modules/treatment_planner.py

@@ -0,0 +1,364 @@
+import numpy as np
+from typing import Dict, List, Optional
+import json
+from datetime import datetime, timedelta
+
+class TreatmentPlanner:
+    def __init__(self):
+        self.treatment_protocols = self._load_treatment_protocols()
+        self.intervention_strategies = self._load_intervention_strategies()
+    
+    def _load_treatment_protocols(self) -> Dict:
+        """Load predefined treatment protocols"""
+        return {
+            'depression': {
+                'psychotherapy': {
+                    'primary': 'Cognitive Behavioral Therapy (CBT)',
+                    'alternatives': [
+                        'Interpersonal Therapy (IPT)',
+                        'Behavioral Activation (BA)',
+                        'Mindfulness-Based Cognitive Therapy (MBCT)'
+                    ],
+                    'duration': '12-16 weeks',
+                    'frequency': 'Weekly'
+                },
+                'medication': {
+                    'classes': [
+                        'SSRIs',
+                        'SNRIs',
+                        'NDRIs',
+                        'Atypical antidepressants'
+                    ],
+                    'duration': '6-12 months minimum',
+                    'monitoring': 'Every 2-4 weeks initially'
+                },
+                'lifestyle': [
+                    'Regular exercise (30 minutes daily)',
+                    'Sleep hygiene improvement',
+                    'Social engagement activities',
+                    'Stress reduction techniques'
+                ]
+            },
+            'anxiety': {
+                'psychotherapy': {
+                    'primary': 'Cognitive Behavioral Therapy (CBT)',
+                    'alternatives': [
+                        'Exposure Therapy',
+                        'Acceptance and Commitment Therapy (ACT)',
+                        'Dialectical Behavior Therapy (DBT)'
+                    ],
+                    'duration': '8-12 weeks',
+                    'frequency': 'Weekly'
+                },
+                'medication': {
+                    'classes': [
+                        'SSRIs',
+                        'SNRIs',
+                        'Buspirone',
+                        'Beta-blockers'
+                    ],
+                    'duration': 'As needed',
+                    'monitoring': 'Every 2-4 weeks initially'
+                },
+                'lifestyle': [
+                    'Relaxation techniques',
+                    'Mindfulness meditation',
+                    'Regular exercise',
+                    'Stress management'
+                ]
+            },
+            'ptsd': {
+                'psychotherapy': {
+                    'primary': 'Trauma-Focused CBT',
+                    'alternatives': [
+                        'EMDR',
+                        'Prolonged Exposure Therapy',
+                        'Cognitive Processing Therapy'
+                    ],
+                    'duration': '12-16 weeks',
+                    'frequency': 'Weekly'
+                },
+                'medication': {
+                    'classes': [
+                        'SSRIs',
+                        'SNRIs',
+                        'Prazosin',
+                        'Antipsychotics'
+                    ],
+                    'duration': '12 months minimum',
+                    'monitoring': 'Every 2-4 weeks initially'
+                },
+                'lifestyle': [
+                    'Stress management techniques',
+                    'Sleep hygiene',
+                    'Grounding exercises',
+                    'Social support engagement'
+                ]
+            }
+        }
+    
+    def _load_intervention_strategies(self) -> Dict:
+        """Load intervention strategies based on severity and symptoms"""
+        return {
+            'mild': {
+                'focus': 'Lifestyle modifications and psychoeducation',
+                'monitoring': 'Monthly check-ins',
+                'escalation_criteria': [
+                    'Symptom worsening',
+                    'Functional impairment',
+                    'Lack of improvement after 4-6 weeks'
+                ]
+            },
+            'moderate': {
+                'focus': 'Psychotherapy with optional medication',
+                'monitoring': 'Bi-weekly check-ins',
+                'escalation_criteria': [
+                    'Severe symptom exacerbation',
+                    'Development of risk factors',
+                    'Poor response to treatment'
+                ]
+            },
+            'severe': {
+                'focus': 'Intensive treatment with medication and therapy',
+                'monitoring': 'Weekly check-ins',
+                'escalation_criteria': [
+                    'Crisis development',
+                    'Safety concerns',
+                    'Treatment resistance'
+                ]
+            }
+        }
+    
+    def generate_plan(self, analysis_results: Dict) -> Dict:
+        """Generate a comprehensive treatment plan based on analysis results"""
+        try:
+            # Extract relevant information from analysis
+            conditions = analysis_results['condition_probabilities']
+            severity = analysis_results['severity_assessment']
+            
+            # Generate treatment plan components
+            treatment_plan = {
+                'summary': self._generate_plan_summary(conditions, severity),
+                'primary_interventions': self._select_primary_interventions(
+                    conditions, severity
+                ),
+                'therapy_recommendations': self._generate_therapy_recommendations(
+                    conditions, severity
+                ),
+                'medication_considerations': self._generate_medication_recommendations(
+                    conditions, severity
+                ),
+                'lifestyle_modifications': self._generate_lifestyle_recommendations(
+                    conditions
+                ),
+                'monitoring_plan': self._create_monitoring_plan(severity),
+                'crisis_plan': self._create_crisis_plan(severity),
+                'timeline': self._create_treatment_timeline(
+                    conditions, severity
+                )
+            }
+            
+            return treatment_plan
+            
+        except Exception as e:
+            return {
+                'error': f'Error generating treatment plan: {str(e)}',
+                'recommendations': self._generate_fallback_recommendations()
+            }
+    
+    def _generate_plan_summary(
+        self, conditions: Dict, severity: Dict
+    ) -> Dict:
+        """Generate a summary of the treatment plan"""
+        primary_condition = max(conditions.items(), key=lambda x: x[1])[0]
+        severity_level = self._determine_severity_level(severity['overall_severity'])
+        
+        return {
+            'primary_condition': primary_condition,
+            'severity_level': severity_level,
+            'treatment_approach': self.intervention_strategies[severity_level]['focus'],
+            'estimated_duration': self._estimate_treatment_duration(
+                primary_condition, severity_level
+            )
+        }
+    
+    def _select_primary_interventions(
+        self, conditions: Dict, severity: Dict
+    ) -> List[str]:
+        """Select primary interventions based on conditions and severity"""
+        interventions = []
+        severity_level = self._determine_severity_level(severity['overall_severity'])
+        
+        for condition, probability in conditions.items():
+            if probability > 0.4:  # Consider conditions with significant probability
+                protocol = self.treatment_protocols[condition]
+                
+                # Add primary therapy
+                interventions.append(
+                    f"Primary therapy: {protocol['psychotherapy']['primary']}"
+                )
+                
+                # Add medication if moderate to severe
+                if severity_level in ['moderate', 'severe']:
+                    interventions.append(
+                        f"Consider medication: {', '.join(protocol['medication']['classes'][:2])}"
+                    )
+        
+        return interventions
+    
+    def _generate_therapy_recommendations(
+        self, conditions: Dict, severity: Dict
+    ) -> Dict:
+        """Generate specific therapy recommendations"""
+        therapy_plan = {}
+        severity_level = self._determine_severity_level(severity['overall_severity'])
+        
+        for condition, probability in conditions.items():
+            if probability > 0.3:
+                protocol = self.treatment_protocols[condition]['psychotherapy']
+                therapy_plan[condition] = {
+                    'primary_therapy': protocol['primary'],
+                    'alternatives': protocol['alternatives'][:2],
+                    'frequency': protocol['frequency'],
+                    'duration': protocol['duration']
+                }
+        
+        return therapy_plan
+    
+    def _generate_medication_recommendations(
+        self, conditions: Dict, severity: Dict
+    ) -> Dict:
+        """Generate medication recommendations"""
+        medication_plan = {}
+        severity_level = self._determine_severity_level(severity['overall_severity'])
+        
+        if severity_level in ['moderate', 'severe']:
+            for condition, probability in conditions.items():
+                if probability > 0.4:
+                    protocol = self.treatment_protocols[condition]['medication']
+                    medication_plan[condition] = {
+                        'recommended_classes': protocol['classes'][:2],
+                        'duration': protocol['duration'],
+                        'monitoring': protocol['monitoring']
+                    }
+        
+        return medication_plan
+    
+    def _generate_lifestyle_recommendations(self, conditions: Dict) -> List[str]:
+        """Generate lifestyle modification recommendations"""
+        recommendations = set()
+        
+        for condition, probability in conditions.items():
+            if probability > 0.3:
+                recommendations.update(
+                    self.treatment_protocols[condition]['lifestyle']
+                )
+        
+        return list(recommendations)
+    
+    def _create_monitoring_plan(self, severity: Dict) -> Dict:
+        """Create a monitoring plan based on severity"""
+        severity_level = self._determine_severity_level(severity['overall_severity'])
+        strategy = self.intervention_strategies[severity_level]
+        
+        return {
+            'frequency': strategy['monitoring'],
+            'focus_areas': [
+                'Symptom severity',
+                'Treatment response',
+                'Side effects',
+                'Functional improvement'
+            ],
+            'escalation_criteria': strategy['escalation_criteria']
+        }
+    
+    def _create_crisis_plan(self, severity: Dict) -> Dict:
+        """Create a crisis intervention plan"""
+        return {
+            'warning_signs': [
+                'Suicidal ideation',
+                'Severe anxiety attacks',
+                'Dissociative episodes',
+                'Severe mood changes'
+            ],
+            'emergency_contacts': [
+                'Primary therapist',
+                'Crisis hotline',
+                'Emergency services (911)',
+                'Trusted support person'
+            ],
+            'immediate_actions': [
+                'Contact emergency services if in immediate danger',
+                'Use prescribed crisis medication if available',
+                'Apply learned coping strategies',
+                'Reach out to support system'
+            ]
+        }
+    
+    def _create_treatment_timeline(
+        self, conditions: Dict, severity: Dict
+    ) -> List[Dict]:
+        """Create a timeline for treatment implementation"""
+        timeline = []
+        start_date = datetime.now()
+        
+        # Initial phase
+        timeline.append({
+            'phase': 'Initial Assessment and Stabilization',
+            'duration': '1-2 weeks',
+            'start_date': start_date.strftime('%Y-%m-%d'),
+            'focus': 'Assessment and immediate interventions'
+        })
+        
+        # Acute phase
+        acute_start = start_date + timedelta(weeks=2)
+        timeline.append({
+            'phase': 'Acute Treatment',
+            'duration': '8-12 weeks',
+            'start_date': acute_start.strftime('%Y-%m-%d'),
+            'focus': 'Primary interventions and symptom reduction'
+        })
+        
+        # Continuation phase
+        continuation_start = acute_start + timedelta(weeks=12)
+        timeline.append({
+            'phase': 'Continuation',
+            'duration': '4-6 months',
+            'start_date': continuation_start.strftime('%Y-%m-%d'),
+            'focus': 'Maintaining improvements and preventing relapse'
+        })
+        
+        return timeline
+    
+    def _determine_severity_level(self, severity_score: float) -> str:
+        """Determine severity level from score"""
+        if severity_score > 0.7:
+            return 'severe'
+        elif severity_score > 0.4:
+            return 'moderate'
+        else:
+            return 'mild'
+    
+    def _estimate_treatment_duration(
+        self, condition: str, severity_level: str
+    ) -> str:
+        """Estimate treatment duration based on condition and severity"""
+        base_duration = {
+            'mild': 3,
+            'moderate': 6,
+            'severe': 12
+        }
+        
+        months = base_duration[severity_level]
+        return f"{months}-{months+3} months"
+    
+    def _generate_fallback_recommendations(self) -> List[str]:
+        """Generate basic recommendations when full plan generation fails"""
+        return [
+            "Seek professional mental health evaluation",
+            "Consider psychotherapy",
+            "Maintain regular sleep schedule",
+            "Practice stress management techniques",
+            "Engage in regular physical activity",
+            "Build and maintain social support network"
+        ]