Lesson 1Foundations of sepsis pathophysiology and clinical criteria (SIRS, qSOFA, SOFA, Sepsis-3)Looks at how sepsis works in the body and the reaction, then connects to signs doctors see like low blood pressure, fast heart, and organ problems. Compares SIRS, qSOFA, SOFA, Sepsis-3 ways and how to use in sorting patients at emergency.
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 inputsPoints out dangers like wrong alarms, misses, model changes over time, bad data. Looks at tricky inputs, strong watching, safety stops, doctor checks, and safe ways to update models.
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 analysisExplains main ways to measure sepsis prediction like AUROC, AUPRC, how well calibrated, time ahead, and decision curves for real use check.
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 extractionShows cleaning and lining up emergency time data for models. Covers re-sampling, uneven times, moving windows, trends, changes, and noting treatments and 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-seriesCompares ways to predict sudden sepsis risk, from simple logistic to boosted trees and deep time models. Notes good points, limits, explaining, and fit for quick emergency times.
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, waveformsDescribes live data flows in emergency like vital signs, blood tests, nurse notes, drugs, body signals. Talks sampling speed, trust, how each shows growing 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 HooksShows how AI sepsis tools fit into emergency work and records systems. Reviews event flows, HL7, FHIR parts, SMART apps, CDS Hooks, stressing easy use, trust, no big changes to doctor work.
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 alertsCovers ways to make alerts for emergency doctors, picking levels, steps of alerts, right people. Handles too many alarms, timing, steps up, showing reasons and background.
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)Outlines rules and proof needs for sepsis diagnosis AI, FDA paths, CMS thoughts, validation checks. Reviews forward tests and report ways like TRIPOD, 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 dataTalks setup ways for sepsis models, live stream vs group check. Handles time limits, missing late data, filling back, watching data flow health in emergency.
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
