Paper Citation Record · LEDGER

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning

As of 7 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2607.16681.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.16681 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T20:21:14.151522Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

verified exact0
verified fuzzy0
unresolved35
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6b9eef57-9247-4d6e-947c-384afe3b5adc · outbound

This paper cites Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the Global Burden of Disease Study,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the Global Burden of Disease Study,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:11.072744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a046c906-3958-4b18-8712-224faf7f9923 · outbound

This paper cites Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:11.176557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08b338ff-7275-44e2-9bb6-c48c42fbc7e2 · outbound

This paper cites Learning to detect sepsis with a multitask Gaussian process RNN classifier,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning Learning to detect sepsis with a multitask Gaussian process RNN classifier,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:11.361189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5365a893-133b-472e-b331-c1ba1fe081dc · outbound

This paper cites Early recognition of sepsis with Gaussian process temporal convolutional networks and dynamic time warping,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning Early recognition of sepsis with Gaussian process temporal convolutional networks and dynamic time warping,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:11.482852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e3e6b8e-97bc-4c93-8fde-e6f243f03476 · outbound

This paper cites Optimizing embedding space with sub-categorical supervised pre-training,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning Optimizing embedding space with sub-categorical supervised pre-training,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:11.577462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1707010a-8566-4b27-b614-9362662dbd1f · outbound

This paper cites Prediction of sepsis in the intensive care unit with minimal electronic health record data: A machine learning approach,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning Prediction of sepsis in the intensive care unit with minimal electronic health record data: A machine learning approach,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:11.677327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c47895ff-a0fe-41ff-acd8-aeb75272aa03 · outbound

This paper cites The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unantic- ipated intensive care unit admission, and death,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unantic- ipated intensive care unit admission, and death,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:11.801437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f4c6203-b003-4c5e-8ebc-21bf01bddb4b · outbound

This paper cites The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3),.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3),

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:11.929672Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 745b3f26-3f87-4292-8a47-04c8ca5f0e46 · outbound

This paper cites MIMIC-III, a freely accessible critical care database,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning MIMIC-III, a freely accessible critical care database,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:12.053008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9284f0a5-32d4-4cac-902a-4f8beed4f8b3 · outbound

This paper cites Early prediction of sepsis in the intensive care unit using the GRU-D-MGP-TCN model,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning Early prediction of sepsis in the intensive care unit using the GRU-D-MGP-TCN model,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:12.127520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 032f9d12-2a8d-4ce2-bb5b-54ec736d4f10 · outbound

This paper cites Advancing early detection of sepsis with temporal convolutional networks using ECG signals,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning Advancing early detection of sepsis with temporal convolutional networks using ECG signals,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:12.239785Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28cddd2d-fd46-491c-a27c-4f16c0f98bc8 · outbound

This paper cites Multi-branching temporal convolutional network for sepsis prediction,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning Multi-branching temporal convolutional network for sepsis prediction,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:12.355416Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dab85e27-75de-41df-8cf2-4074fa328844 · outbound

This paper cites PoEMS: Policy network-based early warning monitoring system for sepsis in intensive care units,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning PoEMS: Policy network-based early warning monitoring system for sepsis in intensive care units,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:12.460260Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ec67028-8d9f-4b54-80a1-1995ddc687cc · outbound

This paper cites Improving early sepsis onset prediction through federated learning,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning Improving early sepsis onset prediction through federated learning,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:12.521814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2484132b-d583-4b68-84c9-51346d9cfd5e · outbound

This paper cites Impact of a deep learning sepsis prediction model on quality of care and survival,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning Impact of a deep learning sepsis prediction model on quality of care and survival,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:12.598071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e37e38cc-9f4f-4ed9-a7de-7b0d1f7e1f9c · outbound

This paper cites Development and prospective implementation of a large language model based system for early sepsis prediction,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning Development and prospective implementation of a large language model based system for early sepsis prediction,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:12.668603Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bcbca597-07fa-4f32-9157-8933186d89e4 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning Representation Learning with Contrastive Predictive Coding

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:12.754199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8478e5d-7ff3-4edd-a00d-f443046c4355 · outbound

This paper cites VICReg: Variance-invariance- covariance regularization for self-supervised learning,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning VICReg: Variance-invariance- covariance regularization for self-supervised learning,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:12.836185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a03c0322-6966-476a-9f0e-f7503e2c13c2 · outbound

This paper cites Unsupervised represen- tation learning for time series with temporal neighborhood coding,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning Unsupervised represen- tation learning for time series with temporal neighborhood coding,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:12.915670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c6ab589-aa18-4ac9-8de8-8c69b866310e · outbound

This paper cites Online sepsis prediction using vital signs and multiscale temporal-aware contrastive learning: Model development and validation study,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning Online sepsis prediction using vital signs and multiscale temporal-aware contrastive learning: Model development and validation study,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:12.957036Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 103d1b29-70f8-4b3e-828c-d70f7ea7543b · outbound

This paper cites Cross-modal contrastive learning for predicting sepsis onset in Medical Internet of Things (MIoT),.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning Cross-modal contrastive learning for predicting sepsis onset in Medical Internet of Things (MIoT),

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:13.048045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d1ae10a-0d8b-4312-849b-8fec1e5099e2 · outbound

This paper cites VISReg: Variance-Invariance-Sketching Regularization for JEPA training.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning VISReg: Variance-Invariance-Sketching Regularization for JEPA training

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:13.104589Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aba277e5-a34e-42d0-a598-c14ded62783c · outbound

This paper cites Early prediction of sepsis from clinical data: The PhysioNet/Computing in Cardiology Challenge 2019,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning Early prediction of sepsis from clinical data: The PhysioNet/Computing in Cardiology Challenge 2019,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:13.202120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad164782-2f34-49dc-81ec-43bf8c229a2d · outbound

This paper cites The SOFA (Sepsis-related Organ Failure As- sessment) score to describe organ dysfunction/failure,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning The SOFA (Sepsis-related Organ Failure As- sessment) score to describe organ dysfunction/failure,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:13.263416Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2c2c464-992a-4d33-9d3a-97bd6143868c · outbound

This paper cites An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:13.315829Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31607b25-0cd8-4a58-887d-bcab751fc55c · outbound

This paper cites XGBoost: A scalable tree boosting system,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning XGBoost: A scalable tree boosting system,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:13.406572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 548e854b-0c1f-4b0c-b8fa-eb97ae0d0a9a · outbound

This paper cites The precision-recall plot is more informa- tive than the ROC plot when evaluating binary classifiers on imbalanced datasets,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning The precision-recall plot is more informa- tive than the ROC plot when evaluating binary classifiers on imbalanced datasets,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:13.469467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e531e0b4-a177-4012-bf58-b66778b1bf64 · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning Communication-efficient learning of deep networks from decentralized data,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:13.570268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff21a574-0de6-40e2-b112-b06a68fa858a · outbound

This paper cites Federated optimization in heterogeneous networks,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning Federated optimization in heterogeneous networks,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:13.641510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71349c85-0043-42f9-aaf0-30738d15992c · outbound

This paper cites Federated learning based on dynamic regularization,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning Federated learning based on dynamic regularization,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:13.702239Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 346c5027-071c-4463-9dcd-1c04da0c9335 · outbound

This paper cites Measuring the effects of non- identical data distribution for federated visual classification,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning Measuring the effects of non- identical data distribution for federated visual classification,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:13.805424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ea35a66-5f96-49d7-9289-9e9fa65366b4 · outbound

This paper cites A federated learning framework with knowledge graph and temporal transformer for early sepsis prediction in multi-center ICUs,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning A federated learning framework with knowledge graph and temporal transformer for early sepsis prediction in multi-center ICUs,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:13.897144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0a2c3f8-9f0f-4ee5-b85d-c485de5af31e · outbound

This paper cites Optimizing sepsis mortality prediction using hybrid federated learning and explainable AI framework,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning Optimizing sepsis mortality prediction using hybrid federated learning and explainable AI framework,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:13.989625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 724d8946-5c1e-43d2-9202-10fe35c8d807 · outbound

This paper cites Cross-hospital sepsis early detection via semi-supervised optimal transport with self-paced ensemble,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning Cross-hospital sepsis early detection via semi-supervised optimal transport with self-paced ensemble,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:14.079316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9a2b217-0cff-4e38-afb9-854fb6eb719c · outbound

This paper cites Machine learning predicts sepsis deterioration trajec- tories,.

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning Machine learning predicts sepsis deterioration trajec- tories,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-01T20:21:14.151522Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.