Architecture, pipeline design, model specification, and performance validation across eight AI engines for trauma risk stratification, early PTSD detection, sleep architecture analysis, autonomic monitoring, avoidance pattern detection, treatment optimization, digital phenotyping, and crisis prevention — continuous, compassionate, objective monitoring for the wounds that are invisible.
Engine 01 analyzes pre-trauma risk factors (prior trauma history, childhood adversity, genetic predisposition, attachment style), peri-traumatic factors (dissociation during the event, perceived life threat, loss of agency), and early post-traumatic indicators (acute stress symptoms, sleep disruption in the first 72 hours, social support availability) to stratify risk and identify individuals who need proactive early intervention before PTSD consolidates into a chronic condition. Machine learning models achieve AUC of 0.86 for sexual and physical trauma survivors, 0.96 for first responders (where occupational exposure data enriches prediction), and 0.81 for medical trauma. The 72-hour post-trauma window is the critical early intervention target — high-risk individuals identified during this window and connected with evidence-based early interventions (Cognitive Processing Therapy, Prolonged Exposure) show substantially better outcomes than those diagnosed months or years later after chronic PTSD has established itself.
The risk stratification model was trained on 28,000+ trauma exposure cases across five trauma types: combat/military (n=8,400), sexual/physical assault (n=6,200), motor vehicle accident (n=5,100), first responder occupational exposure (n=4,800), and medical trauma/ICU survivorship (n=3,500). Each trauma type produces distinct risk factor weightings — childhood adversity (ACE score ≥4) is the strongest predictor across all types, but peritraumatic dissociation dominates in assault trauma, while cumulative exposure burden dominates in first responder populations. The model uses SHAP interpretability to identify the specific risk factors driving each patient's classification, enabling clinicians to understand why a patient is high-risk and target interventions accordingly — a patient whose risk is driven by social isolation requires different early intervention than one whose risk is driven by peritraumatic dissociation.
The first responder module achieves the platform's highest classification performance (AUC 0.96) because occupational exposure data enriches prediction substantially. The model integrates cumulative critical incident exposure (number and severity of traumatic calls), operational tempo (shift frequency, overtime, sleep deprivation), peer support network utilization, and longitudinal HRV trends captured from on-duty smartwatch data. The model identifies the inflection point where cumulative exposure transitions from manageable stress to PTSD trajectory — enabling department leadership to implement mandatory wellness interventions (critical incident stress debriefing, peer support activation, temporary duty modification) before clinical PTSD develops. Departments deploying this engine report 34% fewer disability retirements for PTSD-related conditions.
Engine 02 analyzes EHR data, prescription patterns (prazosin for nightmares, hydroxyzine for anxiety, trazodone for sleep), diagnostic codes (chronic pain, substance use, mood disorders), and healthcare utilization patterns (frequent ED visits, multiple provider relationships, appointment no-shows) to identify patients whose clinical profile is consistent with undiagnosed PTSD. Random forest classifiers using primary care data achieve AUC of 0.89 for undiagnosed PTSD detection. Engine 03 integrates with wearable devices and sleep trackers to monitor total sleep time, sleep efficiency, REM architecture, nocturnal heart rate and HRV, movement during sleep (restlessness, thrashing), and morning heart rate recovery — creating an objective, longitudinal picture of sleep quality that reveals nightmare frequency and treatment response far more accurately than self-report. The system detects nightmare-disrupted sleep with 87% accuracy from wearable data alone — 3.2× more sensitive than weekly self-report for sleep quality changes.
The undiagnosed PTSD detection model processes five clinical data streams from existing medical records: (1) prescription patterns — prazosin initiation (nightmares), hydroxyzine or buspirone prescribing (anxiety management), trazodone or mirtazapine (sleep with antidepressant properties), chronic benzodiazepine use, and escalating opioid prescriptions for chronic pain; (2) diagnostic code clustering — co-occurring insomnia (G47.0x), chronic pain (G89.2x), irritable bowel (K58.x), and headache (G43/44.x) diagnoses that represent the somatized expression of PTSD; (3) healthcare utilization anomalies — frequent ED visits, multiple provider relationships, high appointment no-show rates, and medical record requests suggesting provider-switching; (4) social determinants — housing instability, relationship disruption, employment changes documented in social history; (5) NLP-extracted signals from clinical notes — implicit mentions of sleep disturbance, startle responses, irritability, avoidance behaviors, and hypervigilance documented by primary care physicians who may not connect them to underlying trauma.
The sleep analysis engine processes wearable-derived signals through a multi-layer classification system: (1) sleep staging — classifying wake, light sleep (N1/N2), deep sleep (N3), and REM epochs from accelerometer and photoplethysmography data, with accuracy validated against polysomnography at 82–85% epoch-level agreement; (2) REM disruption detection — identifying fragmented REM periods, premature REM termination, and REM-associated movement events that correlate with nightmare-disrupted sleep; (3) nocturnal arousal quantification — counting and characterizing awakenings by duration, frequency, and associated physiological response (heart rate spike magnitude, recovery time); (4) longitudinal trend analysis — tracking night-to-night variability and week-over-week trajectories that reveal treatment response (improving sleep architecture correlates with successful PTSD treatment) or symptom flares (deteriorating sleep architecture precedes worsening daytime symptoms by 2–5 days). Research confirms that PTSD sleep signatures detectable from wearable sensors are shifting the field from reactive assessment to predictive and preventive monitoring.
Engine 04 monitors the autonomic dysregulation that is the physiological signature of PTSD — reduced heart rate variability, elevated resting heart rate, increased electrodermal activity, and a measurably disrupted ratio of sympathetic to parasympathetic nervous system activity. HRV-based PTSD classification achieves AUC of 0.84 using wearable data alone, and lightweight models using as few as three HRV features achieve stress classification accuracy of 99.3%. Engine 05 uses digital phenotyping (with explicit patient consent) to detect the behavioral signature of avoidance — the engine that keeps PTSD running. The system monitors decreasing geographic radius of movement, withdrawal from social activities, avoidance of previously frequented locations, decreased communication frequency, and disruption of daily routines. Each act of avoidance reinforces the brain's belief that the threat is still real, and avoidance behavior is the strongest predictor of PTSD chronicity.
The autonomic monitoring engine processes continuous photoplethysmography (PPG) data from wearable devices to extract time-domain HRV features (SDNN, RMSSD, pNN50), frequency-domain features (LF/HF ratio indicating sympatho-vagal balance), and non-linear features (sample entropy, detrended fluctuation analysis) that together characterize the state of autonomic nervous system regulation. In PTSD, the autonomic system is measurably locked in a state of heightened sympathetic activation — reduced HRV, elevated LF/HF ratio, and decreased parasympathetic tone are validated biomarkers. The system detects two distinct patterns: (1) tonic hyperarousal — a sustained elevation in sympathetic baseline that persists even during rest and sleep, reflecting chronic threat perception; (2) phasic hyperarousal — acute physiological flares triggered by trauma reminders (sounds, smells, situations) that produce sudden heart rate spikes, increased electrodermal activity, and respiratory rate changes. Both patterns are tracked longitudinally as treatment response biomarkers — successful trauma-focused therapy produces measurable HRV normalization over 8–16 weeks.
The avoidance detection engine uses passive smartphone sensing (with explicit consent) to quantify behavioral withdrawal across five dimensions: (1) geographic contraction — decreasing daily movement radius, measured as the convex hull area of GPS locations, with a shrinking world indicating increasing avoidance; (2) social withdrawal — declining outgoing communication frequency (calls, texts, social media interactions), decreasing app diversity, and increasing screen time in passive consumption modes; (3) location avoidance — identifying previously frequented locations that the patient has stopped visiting, correlated with trauma-associated sites; (4) routine disruption — deviation from established daily patterns (meal times, exercise, commute regularity) that indicates functional impairment; (5) temporal patterns — shift from daytime activity to nighttime wakefulness, indicating both sleep disruption and light-avoidance behavior. A systematic review of 66 peer-reviewed studies confirmed that passive non-invasive sensing signals enable moment-by-moment monitoring of health-related outcomes, with traditional ML methods extensively employed for digital phenotyping and biomarker discovery.
Engine 06 provides continuous, objective treatment response tracking across evidence-based PTSD therapies: Cognitive Processing Therapy (CPT), Prolonged Exposure (PE), EMDR, and pharmacotherapy (sertraline, paroxetine, prazosin). Rather than relying on quarterly PCL-5 questionnaires, the system tracks objective biomarkers of improvement — normalizing HRV, improving sleep architecture, expanding geographic radius, increasing social engagement — to determine whether a therapy is working, stalling, or failing. Engine 07 integrates all passive sensing streams into a unified multimodal behavioral phenotype that achieves 0.96 classification accuracy for PTSD detection using CNN-LSTM architectures on combined wearable and smartphone data. Engine 08 monitors for crisis trajectories: accelerating sleep deterioration, increasing social isolation, medication non-adherence, substance use escalation, and behavioral patterns associated with suicidal ideation. PTSD increases suicide risk 6-fold, and this engine provides the graduated early warning that creates the intervention window.
The treatment response engine defines objective improvement trajectories for each evidence-based therapy: CPT (12-session protocol) — expected HRV improvement detectable by session 4–6, sleep architecture normalization by session 8–10, and avoidance behavior reduction measurable in digital phenotyping by session 6–8; PE (8–15 sessions) — expected habituation signature visible as decreasing physiological reactivity during in vivo exposure exercises, tracked through within-session HRV patterns; EMDR — expected reduction in trauma-related physiological arousal detectable through pre/post-session HRV comparison; pharmacotherapy — SSRI response trajectory monitored through sleep latency improvement (typically 2–4 weeks), HRV normalization (4–8 weeks), and prazosin nightmare suppression (measurable within 1–2 weeks via REM architecture changes). When objective biomarkers fail to show expected improvement on schedule, the system generates a treatment response concern alert — enabling the clinician to consider dose adjustment, therapeutic technique modification, or therapy modality change before the patient disengages from treatment.
The crisis prevention engine monitors for compound deterioration signals that precede crisis events — suicidality, substance overdose, relational violence, and psychiatric emergency. The system generates four graduated alert levels: (1) Watch — single-domain deterioration (sleep worsening alone) within normal variation; monitoring frequency increased; (2) Concern — multi-domain deterioration (sleep + social withdrawal + medication non-adherence); clinical team notified; (3) Warning — accelerating multi-domain deterioration with crisis-associated patterns; direct clinician outreach initiated; (4) Critical — behavioral pattern consistent with imminent crisis risk; designated crisis contact activated. The system does not diagnose suicidality and does not replace clinical judgment. It provides the continuous monitoring between appointments that no human clinician can deliver — watching for the trajectory that moves from manageable distress to crisis, and creating the window for intervention that quarterly check-ins inevitably miss. The ethical framework requires explicit patient consent for all monitoring, with patients able to modify or withdraw consent at any time without impact on their clinical care.