Analyze Mental Health Trends and Risks

Mental Health Analyzer provides comprehensive mental health data analysis: tracking psychological state, identifying emotional patterns, monitoring crisis risk, and optimizing coping strategies. It has eight core modules: mental health assessment analysis (PHQ-9/GAD-7 score trends), emotional pattern recognition (common emotions, triggers, and coping-method effectiveness), therapy progress tracking (goal achievement and symptom improvement), crisis risk assessment (multi-level low/medium/high risk detection and warnings), sleep-mental correlation, exercise-mood correlation, nutrition-mental correlation, and chronic-disease-mental correlation.

It triggers automatically on /mental trend, /mental pattern, /mental therapy progress, /crisis assessment, and /mental report commands - use it when analyzing mood, anxiety, depression scores, therapy progress, or crisis risk, or when correlating mental health with sleep, exercise, or nutrition. It has explicit medical safety boundaries it will not cross: it does not diagnose mental illness, does not prescribe psychiatric medication, does not predict suicide risk or self-harm behavior, does not substitute for professional therapy, and does not handle acute psychiatric crises. What it does instead: identify mental health trends and patterns, assess crisis risk level and issue warnings, offer non-therapeutic coping-strategy suggestions, track therapy progress and goal completion, provide medical-referral suggestions and professional resource information, and analyze correlations between mental health and other health factors.

Reads a main mental-health tracker file plus daily mood-diary logs, first validating that enough data points exist - at least 3 PHQ-9/GAD-7 assessments or 7 days of mood diary - before analyzing. A ten-step pipeline covers: data validation; PHQ-9/GAD-7 score trend analysis including rate of change, severity-level shifts, and specific tracking of PHQ-9 item 9 (self-harm ideation); emotional pattern recognition across top emotions, time-of-day and day-of-week patterns, and volatility; trigger-factor analysis ranking the top 10 triggers by frequency and impact; coping-strategy effectiveness scoring; therapy-goal progress tracking; a multi-factor crisis risk-scoring algorithm (0-20+ points, weighing PHQ-9 item 9, rate of symptom worsening, proportion of high-intensity negative emotion, mood variance, warning signs like hopelessness or talk of death, social withdrawal, and functional impairment, mapped to low/medium/high risk with a corresponding urgency of medical referral); sleep-mental, exercise-mood, nutrition-mental, and chronic-disease-mental correlation analysis; and a structured Markdown report with a crisis-warning banner, trend charts, emotional pattern breakdown, trigger and coping-strategy tables, a therapy progress table, a risk-factor table, correlation findings with correlation coefficients, and an action plan - carrying a mandatory disclaimer that the report is for reference only and not a medical diagnosis. It also defines explicit error handling: a missing data file returns a message pointing the user to the mood-logging command that creates one, insufficient data (fewer than 3 assessments or 7 diary days) returns a warning stating exactly how much data exists versus what's needed rather than analyzing anyway, and a detected high-risk result returns an explicit crisis warning listing immediate actions - contact a crisis hotline, go to the nearest psychiatric emergency department, call emergency services, or reach a trusted person - rather than a routine report.

Correlates the core mental-health and mood-diary data against sleep-tracker, fitness-tracker, and nutrition-tracker data, plus diabetes-tracker, hypertension-tracker, and medication-tracker data where applicable, computing correlation coefficients such as sleep duration versus PHQ-9 score, and producing interactive ECharts-based HTML reports. For large datasets, such as over 6 months of mood-diary entries, it applies weekly/monthly aggregation, representative sampling, incremental analysis of only new data, and caching of intermediate results.

Users already tracking their own mental health data who want trend analysis, emotional pattern recognition, therapy-progress tracking, and an automated crisis risk check that routes to real emergency resources - a crisis hotline, the nearest psychiatric emergency department, or emergency services - rather than attempting diagnosis or treatment itself.