Lesson 1Foundations of sepsis pathophysiology and clinical criteria (SIRS, qSOFA, SOFA, Sepsis-3)We go review sepsis biology and how di body respond, den link dem mechanisms to wetin doctors see for bedside like low blood pressure, fast heart rate, and organ problems. We go compare SIRS, qSOFA, SOFA, and Sepsis-3 criteria and how dem use for ED triage.
Host response to infection and organ dysfunctionHemodynamic changes and microcirculatory failureSIRS, qSOFA, SOFA: components and thresholdsSepsis-3 definition and septic shock criteriaLimitations of clinical scores in the EDLesson 2Safety, failure modes, and mitigation: false positives/negatives, model drift, data quality issues, adversarial inputsWe go identify safety risks like false positives, false negatives, model drift, and poor data quality. We go explore adversarial or unexpected inputs, strong monitoring, guardrails, human oversight, and processes for safe model updates.
False positives, false negatives, and harm modesData quality checks and anomaly detectionModel drift, recalibration, and retrainingAdversarial or unexpected input handlingHuman oversight, overrides, and governanceLesson 3Evaluation metrics and validation strategies for sepsis prediction: AUROC, AUPRC, calibration, lead time, decision curve analysisWe go define key performance metrics for sepsis prediction, including AUROC, AUPRC, calibration, and lead time. We go explain internal and external validation, temporal validation, and decision curve analysis for checking clinical utility.
AUROC, AUPRC, and class imbalanceCalibration curves and risk stratificationLead time and horizon-specific performanceInternal, external, and temporal validationDecision curve analysis and net benefitLesson 4Feature engineering and temporal modeling: time-series preprocessing, sliding windows, trend extractionWe go explain how to clean and align ED time-series data for modeling. We go cover resampling, handling irregular intervals, sliding windows, trend and variability features, and encoding interventions and clinical context over time.
Time alignment, resampling, and interpolationSliding windows and prediction horizonsTrend, variability, and derivative featuresEncoding interventions and care escalationHandling irregular and sparse time-seriesLesson 5Machine learning models for acute risk prediction: logistic regression, gradient boosted trees, RNNs, temporal convolutional networks, transformer-based time-seriesWe go compare modeling approaches for acute sepsis risk prediction, from logistic regression to gradient boosted trees and deep sequence models. We go highlight strengths, limitations, interpretability, and suitability for ED time constraints.
Logistic regression and regularization choicesGradient boosted trees and feature importanceRecurrent neural networks for sequencesTemporal convolutional networks for time-seriesTransformers for clinical time-series dataLesson 6Data modalities for real-time sepsis detection: vitals, labs, nursing notes, medication, waveformsWe go describe key real-time data streams in di ED, including vital signs, laboratory tests, medications, nursing notes, and physiologic waveforms. We go discuss sampling rates, reliability, and how each one signals evolving sepsis.
Vital signs and continuous monitoring feedsLaboratory panels, cultures, and turnaround timesMedication orders, fluids, and vasopressorsNursing notes, triage text, and flowsheetsWaveforms from monitors and bedside devicesLesson 7Integration with ED workflows and EHR systems: event streams, FHIR, HL7, SMART on FHIR apps, CDS HooksWe go describe how AI sepsis models integrate into ED workflows and EHRs. We go review event streams, HL7, FHIR resources, SMART on FHIR apps, and CDS Hooks, emphasizing usability, reliability, and minimal disruption to clinical practice.
Event-driven architectures and data streamsHL7 and FHIR resources for sepsis signalsSMART on FHIR apps for bedside decision supportCDS Hooks for context-aware recommendationsWorkflow mapping and usability testingLesson 8Clinical alert design and human factors: thresholds, alarm fatigue mitigation, escalating workflows, who receives alertsWe go cover principles of alert design for ED clinicians, including threshold selection, tiered alerts, and routing to appropriate roles. We go address alarm fatigue, alert timing, escalation pathways, and presenting explanations and context.
Choosing thresholds and alert tiersAlarm fatigue and suppression strategiesWho receives alerts and on which channelsEscalation workflows and handoff supportExplaining alerts and providing contextLesson 9Regulatory and evidence requirements for diagnostic AI: FDA/CMS considerations, clinical validation study design, prospective pilots, reporting standards (TRIPOD, CONSORT-AI)We go outline regulatory and evidence expectations for diagnostic AI in sepsis, including FDA pathways, CMS considerations, and clinical validation. We go review prospective pilots and reporting standards such as TRIPOD and CONSORT-AI.
FDA pathways for diagnostic support toolsCMS, reimbursement, and quality programsDesigning robust clinical validation studiesProspective pilots and phased rolloutsTRIPOD and CONSORT-AI reporting guidanceLesson 10Deployment frequency and latency considerations: near-real-time streaming vs batch scoring, handling missing and delayed dataWe go discuss deployment architectures for sepsis models, comparing near-real-time streaming with batch scoring. We go address latency budgets, handling missing or delayed data, backfilling, and monitoring data pipeline health in di ED.
Near-real-time streaming vs batch scoringLatency budgets and SLA definitionsImputation for missing and delayed inputsBackfilling, replay, and late-arriving dataMonitoring pipelines and system resilience
