Lesson 1Foundations of sepsis pathophysiology and clinical criteria (SIRS, qSOFA, SOFA, Sepsis-3)This lesson reviews sepsis biology and the body's response, linking these to bedside signs like low blood pressure, fast heart rate, and organ failure. It compares SIRS, qSOFA, SOFA, and Sepsis-3 criteria and their role in emergency department 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 inputsThis lesson identifies safety risks such as false alarms, missed cases, model drift, and poor data quality. It explores handling unexpected inputs, robust monitoring, safety barriers, human oversight, and safe update processes.
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 analysisThis lesson defines key metrics for sepsis prediction like AUROC, AUPRC, calibration, and lead time ahead. It explains internal/external validation, time-based validation, and decision curve analysis for assessing clinical usefulness.
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 extractionThis lesson explains cleaning and aligning emergency department time-series data for modelling. It covers resampling, irregular intervals, sliding windows, trend and variation 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-seriesThis lesson compares approaches for predicting acute sepsis risk, from logistic regression to boosted trees and deep sequence models. It highlights strengths, limits, explainability, and fit for emergency department time pressures.
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, waveformsThis lesson describes key real-time data streams in emergency departments, including vital signs, lab tests, medications, nursing notes, and waveforms. It discusses sampling rates, reliability, and how each signals developing 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 HooksThis lesson describes integrating AI sepsis models into emergency workflows and electronic records. It reviews event streams, HL7, FHIR resources, SMART apps, and CDS Hooks, stressing ease of use, reliability, and minimal disruption.
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 alertsThis lesson covers alert design principles for emergency clinicians, including threshold choices, tiered alerts, and routing to right roles. It addresses alert fatigue, timing, escalation paths, and providing 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)This lesson outlines regulatory and evidence needs for sepsis diagnostic AI, including FDA pathways, CMS views, and clinical validation. It reviews prospective pilots and standards like TRIPOD and CONSORT-AI for reporting.
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 dataThis lesson discusses deployment setups for sepsis models, comparing near-real-time streaming to batch scoring. It covers latency limits, missing/delayed data handling, backfilling, and monitoring data pipelines in emergency settings.
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
