Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis | Paper Citation Record

Observation 9446654d-c130-48a6-ab09-9d3c13b08b1e · outbound

This paper cites Pediatric Sepsis - Part I: ‘Children are not small adults!,’.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Pediatric Sepsis - Part I: ‘Children are not small adults!,’

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:15.069405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 410bef16-eda0-403d-aa67-a01645f7d5c4 · outbound

This paper cites The Host Response to Sepsis and Developmental Impact,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis The Host Response to Sepsis and Developmental Impact,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:15.056609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d653bcb0-f158-4713-8d9e-eba6cef60fc6 · outbound

This paper cites Introduction to Pediatric Sepsis.,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Introduction to Pediatric Sepsis.,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:15.044299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e7ac5429-1b45-40a2-b428-a1112b2f97d2 · outbound

This paper cites Management of Neonates With Suspected or Proven Early-Onset Bacterial Sepsis,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Management of Neonates With Suspected or Proven Early-Onset Bacterial Sepsis,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:15.030883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3267e249-71df-4b6e-8835-73c71f21707b · outbound

This paper cites Pediatric severe sepsis in U.S. children’s hospitals,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Pediatric severe sepsis in U.S. children’s hospitals,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:15.017019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4a6551e6-9f59-43d2-9393-dcbef69409e8 · outbound

This paper cites Global epidemiology of pediatric severe sepsis: the sepsis prevalence, outcomes, and therapies study,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Global epidemiology of pediatric severe sepsis: the sepsis prevalence, outcomes, and therapies study,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:15.001774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a31891d5-01b2-4e34-886d-24344972e642 · outbound

This paper cites Pediatric severe sepsis: current trends and outcomes from the pediatric health information systems database*.,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Pediatric severe sepsis: current trends and outcomes from the pediatric health information systems database*.,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.988530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4c8cc049-19e6-4724-b4cb-78280c9fb922 · outbound

This paper cites an unresolved cited work.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-14T11:29:14.976155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6e1b1e37-67a3-458e-a415-4499e8a6d2d7 · outbound

This paper cites Epidemiology of pediatric hospitalizations at general hospitals and freestanding children’s hospitals in the United States,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Epidemiology of pediatric hospitalizations at general hospitals and freestanding children’s hospitals in the United States,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.963875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6c33cd06-69cd-4b81-ada5-2c70f70ec694 · outbound

This paper cites Early recognition and management of septic shock in children,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Early recognition and management of septic shock in children,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.951510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 58eaa459-56de-483d-8c04-7617721790dc · outbound

This paper cites Adherence to PALS Sepsis Guidelines and Hospital Length of Stay,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Adherence to PALS Sepsis Guidelines and Hospital Length of Stay,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.938787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5853526b-5cca-485e-b9e0-d6068e58efbb · outbound

This paper cites Protocolized Treatment Is Associated with Decreased Organ Dysfunction in Pediatric Severe Sepsis,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Protocolized Treatment Is Associated with Decreased Organ Dysfunction in Pediatric Severe Sepsis,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.920304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a3126f75-160d-4827-92b4-4827d9995255 · outbound

This paper cites Resuscitation Bundle in Pediatric Shock Decreases Acute Kidney Injury and Improves Outcomes,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Resuscitation Bundle in Pediatric Shock Decreases Acute Kidney Injury and Improves Outcomes,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.906997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation af9cb227-5623-46b5-ade3-58218a252f31 · outbound

This paper cites National estimates of emergency department visits for pediatric severe sepsis in the United States,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis National estimates of emergency department visits for pediatric severe sepsis in the United States,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.894697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5b293b21-0d09-44dd-a867-e16d1b1bebd8 · outbound

This paper cites Clinical practice parameters for hemodynamic support of pediatric and neonatal patients in septic shock,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Clinical practice parameters for hemodynamic support of pediatric and neonatal patients in septic shock,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.881994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5e0fb715-4834-41cf-838e-01c2aae49cad · outbound

This paper cites Continuum of care in pediatric sepsis: a prototypical acute care delivery model,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Continuum of care in pediatric sepsis: a prototypical acute care delivery model,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.869710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5dbfa8ac-772d-490b-8173-a7e8ac7777cf · outbound

This paper cites Systemic inflammatory response in the pediatric emergency department: a common phenomenon that does not predict severe illness,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Systemic inflammatory response in the pediatric emergency department: a common phenomenon that does not predict severe illness,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.856962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ef2a77a2-5f0e-4d08-8f4a-9b522c71b805 · outbound

This paper cites Designing a Pediatric Severe Sepsis Screening Tool,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Designing a Pediatric Severe Sepsis Screening Tool,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.843119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 03502b12-4ba6-431a-b8d5-449b8568ad24 · outbound

This paper cites Designing a pediatric severe sepsis screening tool.,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Designing a pediatric severe sepsis screening tool.,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.829302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 33c9e77f-60c7-4704-a61d-50b5c54f9459 · outbound

This paper cites The third international consensus definitions for sepsis and septic shock (sepsis -3),.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis The third international consensus definitions for sepsis and septic shock (sepsis -3),

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.815674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6bc7c2bd-3e83-4263-ab6a-fe1aea9efb44 · outbound

This paper cites Prognostic accuracy of age -adapted SOFA, SIRS, PELOD-2, and qSOFA for in-hospital mortality among children with suspected infection admitted to the intensive care unit,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Prognostic accuracy of age -adapted SOFA, SIRS, PELOD-2, and qSOFA for in-hospital mortality among children with suspected infection admitted to the intensive care unit,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.802912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ba09a28a-b9b3-4cc6-ae56-571fd3b76e54 · outbound

This paper cites Medical decision support using machine learning for early detection of late-onset neonatal sepsis,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Medical decision support using machine learning for early detection of late-onset neonatal sepsis,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.790135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 88c0b591-47dc-4e46-b7aa-90221420df00 · outbound

This paper cites Pediatric Severe Sepsis Prediction Using Machine Learning,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Pediatric Severe Sepsis Prediction Using Machine Learning,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.777250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 05fc212a-e1d7-4f74-9e30-93345b0bbe82 · outbound

This paper cites Raising concerns about the Sepsis-3 definitions,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Raising concerns about the Sepsis-3 definitions,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.764498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f36ce1e6-2c42-4799-a3ad-9f88d096ced3 · outbound

This paper cites Predictive Learning in the Presence of Heterogeneity and Limited,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Predictive Learning in the Presence of Heterogeneity and Limited,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.751234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d7f3c6b0-b893-40ed-badd-3df36844f712 · outbound

This paper cites The role of artificial intelligence in precision medicine,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis The role of artificial intelligence in precision medicine,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.738713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f2d2ac7a-6476-410f-9aa2-068b425216b6 · outbound

This paper cites Precision medicine for all? Challenges and opportunities for a precision medicine approach to critical illness,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Precision medicine for all? Challenges and opportunities for a precision medicine approach to critical illness,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.725973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7b557359-7285-473f-a3c5-1d9d9727e5c4 · outbound

This paper cites Latent Class Analysis: An Alternative Perspective on Subgroup Analysis in Prevention and Treatment,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Latent Class Analysis: An Alternative Perspective on Subgroup Analysis in Prevention and Treatment,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.712857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 106a2f5b-40aa-4314-bf66-9baf530dc826 · outbound

This paper cites A latent profile analysis of college students’ achievement goal orientation,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis A latent profile analysis of college students’ achievement goal orientation,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.700064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4eed358a-9722-4c33-839f-5480fa561d49 · outbound

This paper cites An introduction to latent variable mixture modeling (Part 1): Overview and cross-sectional latent class and latent profile analyses,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis An introduction to latent variable mixture modeling (Part 1): Overview and cross-sectional latent class and latent profile analyses,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.686134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 27dd70cc-5791-42ea-bdd7-6a0aa185d501 · outbound

This paper cites Six subphenotypes in septic shock: Latent class analysis of the PROWESS Shock study,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Six subphenotypes in septic shock: Latent class analysis of the PROWESS Shock study,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.672552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation dc076aaf-b035-4573-9322-702e983a99fd · outbound

This paper cites Identification of subclasses of sepsis that showed different clinical outcomes and responses to amount of fluid resuscitation: a latent profile analysis,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Identification of subclasses of sepsis that showed different clinical outcomes and responses to amount of fluid resuscitation: a latent profile analysis,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.659549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 76ba6103-ed5f-44f3-b11b-0505d2a18328 · outbound

This paper cites Identification of three classes of acute respiratory distress syndrome using latent class analysis,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Identification of three classes of acute respiratory distress syndrome using latent class analysis,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.646363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b210dab0-03eb-4f8d-9669-779928e4a76e · outbound

This paper cites Incorporating Prior Domain Knowledge Into Inductive Machine Learning,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Incorporating Prior Domain Knowledge Into Inductive Machine Learning,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.632890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b77acd19-53a9-42a1-88e6-e44356aa36ea · outbound

This paper cites Integrating prior knowledge into deep learning,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Integrating prior knowledge into deep learning,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.619848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 11444c7d-ee77-461c-9961-f520dabc1811 · outbound

This paper cites Semantically Enhanced Dynamic Bayesian Network or Detecting Sepsis Mortality Risk in ICU Patients with Infection,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Semantically Enhanced Dynamic Bayesian Network or Detecting Sepsis Mortality Risk in ICU Patients with Infection,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.606277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 19662fe6-aa4d-485e-ba5e-3a4d157fe839 · outbound

This paper cites Estimating the Dimension of a Model,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Estimating the Dimension of a Model,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.593016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 846ee368-da01-43e3-a0cd-0316e8ddf85f · outbound

This paper cites Model-based clustering, discriminant analysis, and density estimation,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Model-based clustering, discriminant analysis, and density estimation,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.579730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fd65a87d-2673-45d2-a060-1a9b205cdf1c · outbound

This paper cites A new family of power transformations to improve normality or symmetry,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis A new family of power transformations to improve normality or symmetry,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.565664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5da8321d-af82-4054-846e-d4ba5ccc53a2 · outbound

This paper cites Gradient boosting machines, a tutorial,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Gradient boosting machines, a tutorial,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.551935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 559fbb26-5de4-488d-a32d-06521a0cae8d · outbound

This paper cites Random forests,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Random forests,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.538284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation af938381-8458-4de1-81c7-25f93b547af0 · outbound

This paper cites SMOTE: Synthetic minority over -sampling technique,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis SMOTE: Synthetic minority over -sampling technique,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.525541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e26a378e-ea18-4c99-95c9-dbe1f415eb89 · outbound

This paper cites Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians.,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians.,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.513023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation be712a7e-b745-4fa5-a6df-838d71d588a2 · outbound

This paper cites Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.500470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 98dd73fb-6463-444c-9c7e-295dfdff66ab · outbound

This paper cites Fluid resuscitation in human sepsis: Time to rewrite history?,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Fluid resuscitation in human sepsis: Time to rewrite history?,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.489008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 99078669-b395-4ac7-8de8-c41e8d1e766d · outbound

This paper cites Interventions for Pediatric Sepsis and Their Impact on Outcomes: A Brief Review,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Interventions for Pediatric Sepsis and Their Impact on Outcomes: A Brief Review,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.476614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bfac9ffb-b878-462d-b1b7-114443579a15 · outbound

This paper cites Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.463473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e74d0848-793f-4151-9301-07f2b2695003 · outbound

This paper cites K-means-based consensus clustering: A unified view,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis K-means-based consensus clustering: A unified view,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.450164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f3eda982-23e1-4ac3-a068-f20a217385d0 · outbound

This paper cites A Comparison of Segment Retention Criteria for Finite Mixture Logit Models,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis A Comparison of Segment Retention Criteria for Finite Mixture Logit Models,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.437150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c11f3881-3782-4935-876b-46ef1c87d7b3 · outbound

This paper cites Mixture Models: Latent Profile and Latent Class Analysis,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Mixture Models: Latent Profile and Latent Class Analysis,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.424190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8e40cdb9-5842-4582-83a5-4931c786894b · outbound

This paper cites Exploiting missing clinical data in Bayesian network modeling for predicting m edical problems.,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Exploiting missing clinical data in Bayesian network modeling for predicting m edical problems.,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.411063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 56a51ea3-9d10-4f9f-a9e6-d57e787a9c71 · outbound

This paper cites Data management by using R: big data clinical research series,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Data management by using R: big data clinical research series,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.396247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d734df74-d50d-44ee-9f53-0776d15dff12 · outbound

This paper cites Association between early lactate levels and 30-day mortality in clinically suspected sepsis in children,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Association between early lactate levels and 30-day mortality in clinically suspected sepsis in children,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.383041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 068297e1-14e0-4c7a-808d-8ec3787d6f76 · outbound

This paper cites Clinical practice parameters for hemodynamic support of pediatric and neonatal septic shock: 2007 update from the American College of Critical Care Medicine,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Clinical practice parameters for hemodynamic support of pediatric and neonatal septic shock: 2007 update from the American College of Critical Care Medicine,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.369696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1bfc851c-6f31-4fcd-8879-3f11c2dd1abf · outbound

This paper cites Pediatric Sepsis Biomarker Risk Model-II: Redefining the Pediatric Sepsis Biomarker Risk Model with Septic Shock Phenotype,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Pediatric Sepsis Biomarker Risk Model-II: Redefining the Pediatric Sepsis Biomarker Risk Model with Septic Shock Phenotype,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:29:14.354880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 643c056f-19e5-4107-88ec-6d1bd8f16397 · outbound

This paper cites Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data.,.

Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data.,

Reference 56

Resolution: malformed identifier; raw_fallback, observed 2026-08-14T11:29:14.339980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers