AKI is the current clinical demonstrator. This PhD asks how a risk model can move from a valid prospective target, to a faithful representation of the evolving patient and clinical context, to an operationally meaningful signal that supports structured review without pretending that prognosis is the same as preventability or treatment response.
One briefing, with a fast supervisor-level overview and deeper epidemiology / phenotype detail underneath.
Predict clinically meaningful deterioration while preserving prospective information boundaries.
The treating ICU / acute-care team reviews prospectively observable context and routes onward when warranted.
Persistent / severe trajectory outcomes remain secondary; the operational endpoint is observation-process dependent.
Candidate.8 is canonical and non-executable; patient-level target/model execution remains gated.
The thesis is organised around three linked methodological problems. Operationalisability is evaluated throughout rather than added as a final deployment chapter.
Translate a clinical construct into a prospectively valid EHR prediction target with explicit risk-set, timing, component, missingness and terminal-event semantics.
Main AKI issueClinical AKI ≠ EHR-ascertainable AKI ≠ operational prediction endpoint.
Move beyond a flat list of predictors to dynamic renal, physiological, treatment, exposure and observation-process states.
Main AKI issueContext changes the meaning of the same observed value or medicine.
Test conventional representations first. Knowledge-informed or neuro-symbolic methods enter only if they address a specific limitation that remains.
Research design principleMethod follows the clinical and methodological requirement, not novelty.
The detailed review portal is folded into this briefing here: who enters the risk set, when prediction is issued, what event is forecast, how follow-up works, and where uncertainty remains.
Current prospectively ascertainable Stage 0 or Stage 1 can remain eligible for first future Stage ≥2 prediction.
Rolling entry opens from ICU +6 h. q6 is the accepted primary model risk-refresh cadence; q12 is the prespecified cadence sensitivity.
Forecast the first prospectively ascertainable KDIGO Stage ≥2 event; persistent / severe trajectory remains secondary.
The primary horizon is (t, t+24 h]; 48 h is secondary. A prespecified sensitivity excludes events in the first 6 h after prediction.
Leaving the index ICU stops new prediction issuance, but an already-issued horizon continues under hospital follow-up. Death and discharge remain distinct terminal states.
Accepted partial design decisions within the current prospective AKI target framework.
Confirmed current UO/q6 development counts; this is not the final accepted AKI modelling cohort.
The phenotype must distinguish clinical KDIGO criteria from what was actually observable in the EHR at the prediction time. Baseline SCr availability, UO evaluability, KRT attribution, event-time versus availability-time, revisions, terminal events and zero-opportunity outcome ascertainment can all change what the label means.
The complete target is not globally accepted in CURRENT_AUTHORITY.yaml; candidate.8 remains canonical, aligned and non-executable. Global feature and model fields are also null. The page therefore distinguishes accepted partial decisions from candidate/specification state.
This replaces a standalone “Medication-aware design” section. Medication is one clinically important context domain within a wider phenotype system.
Background susceptibility can alter future AKI risk without being the acute mechanism itself.
CKD → risk factor for AKI. Keep chronic baseline state separate from acute change; one elevated creatinine does not establish CKD.
Adequately measured low UO can establish AKI stage; missing UO cannot be treated as normal UO.
Low MAP/BP is haemodynamic information, not a deterministic label for intravascular depletion.
Temporal precedence is a patient fact, not causal attribution or preventability.
Absence of a valid assessment opportunity must remain explicit rather than silently becoming a negative label.
Depletion may support withholding/review, while congestion may make diuretic treatment appropriate: the same medicine can imply opposite actions.
The operational phenotype depends on measurement coverage, timestamps and observation policy.
A value is usable only when its clinical event and its information availability are valid for the prediction occasion. This affects both the target and predictor-side context.
A test, observation, drug administration, procedure or treatment state occurs.
The EHR can expose the information later than the biological or collection event.
Only information validly available by t may enter the model or current-state phenotype.
The outcome is sought prospectively after the prediction time.
Observed non-event is separated from zero valid opportunity to determine the endpoint.
Each has different observation and timing properties. One observed qualifying component may establish AKI while another remains unavailable.
MAP, lactate, fluids and vasopressors may support haemodynamic interpretation but do not by themselves establish shock, hypovolaemia or a renal mechanism.
An EHR phenotype and its apparent missingness can shift when another hospital measures or documents differently.
The modelling framework is not medication-specific. Context domains are added one at a time, with each block requiring its own clinical rationale, prospective-observability check and specification before comparison.
Start with a strong renal and physiological representation using only prospectively valid observed patient information.
Question: what can the evolving clinical state already explain?
Add one clinically justified context block. Current priority is observed haemodynamic/treatment state, then infection/acute deterioration, then medication/pharmacology, then procedure/surgery/contrast.
Each domain is tested separately before combining domains, so any incremental value remains interpretable.
Test whether the same context becomes more informative when represented relative to renal trajectory, physiology, treatment state and time rather than as a flat feature list.
Only after fair same-information comparisons should more complex or external-knowledge mechanisms be considered.
The accepted evaluation architecture avoids reducing success to AUROC alone and keeps retrospective predictive claims separate from clinical-outcome benefit.
Primary horizon; 48 h retained as secondary/sensitivity.
Primary discrimination estimand for the central contextual representation contrast; paired ΔAUROC is key secondary evidence.
Reliability curve, calibration-in-the-large/intercept where compatible, slope and Brier score.
Compare PPV, sensitivity and workload at prespecified review-burden operating points rather than inventing an “optimal” cutoff.
Patient/stay-level first-alert PPV, sensitivity, lead time, unique patients alerted and repeated-alert burden.
Paired subject-cluster bootstrap replicates for final uncertainty, preserving within-subject repeated observations.
A gate-based plan is more defensible than promising a fixed number of papers or methods in advance.
Finish the remaining executable integration and validation gates for the prospectively ascertainable Stage ≥2 phenotype.
Establish time-updated physiology performance and the first governed medication/context increments with prespecified evaluation.
Prioritise later phenotype/context blocks only after evidence, observability and specification review.
Introduce explicit external knowledge or neuro-symbolic mechanisms only when ordinary learning leaves a concrete residual problem.
These are discussion prompts, not hidden requests to reopen already accepted project decisions.
Is prospective phenotype and ascertainment methodology sufficiently substantial as the first empirical chapter, or should it be framed primarily as enabling methodology for the later context study?
For the haemodynamic / treatment-state study, which observed representation should be the main incremental test: continuous MAP/BP trajectories, support-state transitions, lactate/perfusion summaries, or a deliberately minimal combination?
What empirical failure of simpler temporal/context models would be sufficient justification for TraCeR-style, Transformer or neuro-symbolic complexity?
How far should the PhD push beyond discrimination/calibration into first-alert burden, lead time, transportability and workflow-facing evaluation before prospective clinical validation becomes future work?
The page separates live project authority, the Evidence Matrix, project literature notes and synthesis.
Source access: Project governance files are stored in a private GitHub repository and require authorised access.
HOLD; target/features/model globally unaccepted.Consulted 00_Read Me, C16_Clinical Phenotypes, C17_Phenotype Relationships and P19_Feature–Phenotype Crosswalk. The Matrix is an evidence-navigation/synthesis layer, not scientific authority.
REV-PHENO-01 — AKI operational definition, prospective EHR ascertainment and component-availability distinctions.REV-ASCERT-01 — outcome unascertainability, informative observation and uncertain endpoints.REV-INTUSE-07 — intended-use synthesis and clinical ownership / action architecture.REV-MED-08 — prediction relevance and system placement of medication knowledge; action knowledge ≠ prediction knowledge.Private reflective sources were not consulted. The phenotype relationship map is a supervisor-facing synthesis of the live Matrix and governed project evidence; it is not an executable ontology, feature contract or causal model.