Towards Foundation Models for Critical Care Time Series | Paper Citation Record

Observation 59dcc140-0212-479a-9ecc-c1a18eb8ccd3 · outbound

This paper cites Chronos: Learning the Language of Time Series.

Towards Foundation Models for Critical Care Time Series Chronos: Learning the Language of Time Series

Reference 1

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Observation ce8b72dc-e012-411a-a34e-5427d13b36fc · outbound

This paper cites Medical event data standard (meds): Facilitating machine learning for health.

Towards Foundation Models for Critical Care Time Series Medical event data standard (meds): Facilitating machine learning for health

Reference 2

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Observation dd42aff2-f9ea-4ae3-bc7f-e7c83a964376 · outbound

This paper cites xLSTM: Extended Long Short-Term Memory.

Towards Foundation Models for Critical Care Time Series xLSTM: Extended Long Short-Term Memory

Reference 3

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Observation 76313bfe-a2ae-4218-99b3-6063734ef18d · outbound

This paper cites ricu: R’s interface to intensive care data.

Towards Foundation Models for Critical Care Time Series ricu: R’s interface to intensive care data

Reference 4

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Observation 2a78261b-65a2-4327-bb15-d4c5f35aabc3 · outbound

This paper cites Multimodal clinical benchmark for emergency care (mc-bec): A comprehensive benchmark for evaluating foundation models in emergency medicine.

Towards Foundation Models for Critical Care Time Series Multimodal clinical benchmark for emergency care (mc-bec): A comprehensive benchmark for evaluating foundation models in emergency medicine

Reference 5

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Observation acec48bc-9451-493b-a8f9-36861c706458 · outbound

This paper cites Meditron-70b: Scaling medical pretraining for large language models, 2023.

Towards Foundation Models for Critical Care Time Series Meditron-70b: Scaling medical pretraining for large language models, 2023

Reference 6

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Observation 70f6cefd-a5e1-4380-b3f9-ae97083ef46a · outbound

This paper cites Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.

Towards Foundation Models for Critical Care Time Series Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Reference 7

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Observation acc601b2-903e-4cd5-a954-b9c7d3233c9a · outbound

This paper cites scgpt: toward building a foundation model for single-cell multi-omics using generative ai.

Towards Foundation Models for Critical Care Time Series scgpt: toward building a foundation model for single-cell multi-omics using generative ai

Reference 8

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Observation dfb2ea0b-38af-4086-a3b3-05a3b775beba · outbound

This paper cites Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality.

Towards Foundation Models for Critical Care Time Series Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 9

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Observation 5dd3e9c7-0d8f-4266-915c-0ca93ef88f74 · outbound

This paper cites A decoder-only foundation model for time-series forecasting.

Towards Foundation Models for Critical Care Time Series A decoder-only foundation model for time-series forecasting

Reference 10

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Observation 58a8b58a-f9e1-4d37-b53d-aaf15ec47523 · outbound

This paper cites PyTorch Lightning , March 2019.

Towards Foundation Models for Critical Care Time Series PyTorch Lightning , March 2019

Reference 11

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Observation e9a59cfd-2940-486b-84b4-e710ba13facc · outbound

This paper cites Faltys, M.

Towards Foundation Models for Critical Care Time Series Faltys, M

Reference 12

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Observation 33963e46-fb40-49f5-9c50-c7eca4ca05e2 · outbound

This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

Towards Foundation Models for Critical Care Time Series The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 13

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Observation 7ce08988-3d17-470d-b1aa-a3542a7070f5 · outbound

This paper cites Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals.

Towards Foundation Models for Critical Care Time Series Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals

Reference 14

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Observation 1035e708-ebc5-4317-a38b-0c6c68ee28bf · outbound

This paper cites Revisiting Deep Learning Models for Tabular Data.

Towards Foundation Models for Critical Care Time Series Revisiting Deep Learning Models for Tabular Data

Reference 15

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Observation 6455f304-1a9a-4ae9-86fe-4512af8ae185 · outbound

This paper cites MOMENT: A Family of Open Time-series Foundation Models.

Towards Foundation Models for Critical Care Time Series MOMENT: A Family of Open Time-series Foundation Models

Reference 16

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Observation 61fa2c84-ff93-45d9-a566-73aae35fc5a0 · outbound

This paper cites Ehr foundation models improve robustness in the presence of temporal distribution shift.

Towards Foundation Models for Critical Care Time Series Ehr foundation models improve robustness in the presence of temporal distribution shift

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:19:37.892783Z

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Observation f99b0772-a343-441b-9582-5deaccdec74b · outbound

This paper cites A multi-center study on the adaptability of a shared foundation model for electronic health records.

Towards Foundation Models for Critical Care Time Series A multi-center study on the adaptability of a shared foundation model for electronic health records

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:19:37.879950Z

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Observation 47ddc71b-5bd2-4aac-ba20-e2631ce0ea08 · outbound

This paper cites Multitask learning and benchmarking with clinical time series data.

Towards Foundation Models for Critical Care Time Series Multitask learning and benchmarking with clinical time series data

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:19:37.867752Z

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Observation bbdcb571-055e-440b-b8d1-c4f2f881f1b6 · outbound

This paper cites Set Functions for Time Series.

Towards Foundation Models for Critical Care Time Series Set Functions for Time Series

Reference 20

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Observation e40c88e3-3073-4238-99e4-fea0345921b2 · outbound

This paper cites u ser, Xinrui Lyu, Martin Faltys, Aliz \'e e Pace, Marine Hoche, Stephanie Hyland, Hugo Y \`e che, Manuel Burger, Tobias M Merz, and Gunnar R \.

Towards Foundation Models for Critical Care Time Series u ser, Xinrui Lyu, Martin Faltys, Aliz \'e e Pace, Marine Hoche, Stephanie Hyland, Hugo Y \`e che, Manuel Burger, Tobias M Merz, and Gunnar R \

Reference 21

Resolution: verified exact; doi, observed 2026-08-12T13:19:37.209998Z

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Observation 466d95a2-a9ca-40a0-9351-9b6da55cc4b7 · outbound

This paper cites Early prediction of circulatory failure in the intensive care unit using machine learning.

Towards Foundation Models for Critical Care Time Series Early prediction of circulatory failure in the intensive care unit using machine learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:19:37.855882Z

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Observation c277d0dc-7b62-4ddb-aa9e-8b5bb55f075c · outbound

This paper cites MIMIC-IV" (version 2.2).

Towards Foundation Models for Critical Care Time Series MIMIC-IV" (version 2.2)

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:19:37.843918Z

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Observation adab3e0b-91d0-4682-974c-a1c1a2640250 · outbound

This paper cites MIMIC-III Clinical Database , 2016 a.

Towards Foundation Models for Critical Care Time Series MIMIC-III Clinical Database , 2016 a

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:19:37.832131Z

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Observation 435be39e-b716-4754-9248-a9dbfc7b28cd · outbound

This paper cites Mimic-iv-ed demo, 2023 a.

Towards Foundation Models for Critical Care Time Series Mimic-iv-ed demo, 2023 a

Reference 25

Resolution: verified exact; doi, observed 2026-08-12T13:19:37.194990Z

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Observation 01adf988-d8dd-4a6b-ad39-78984488af51 · outbound

This paper cites Mimic-iii, a freely accessible critical care database.

Towards Foundation Models for Critical Care Time Series Mimic-iii, a freely accessible critical care database

Reference 26

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Observation e8fcf7aa-a3c6-438f-9ac3-fd5d8280b9b9 · outbound

This paper cites Mimic-iv, a freely accessible electronic health record dataset.

Towards Foundation Models for Critical Care Time Series Mimic-iv, a freely accessible electronic health record dataset

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T13:19:36.980556Z

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Observation 68b2b987-2c04-4bb3-acc9-5e555dc85351 · outbound

This paper cites LightGBM : A highly efficient gradient boosting decision tree.

Towards Foundation Models for Critical Care Time Series LightGBM : A highly efficient gradient boosting decision tree

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:19:37.807607Z

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Observation 470c0ecd-512c-42d8-91e4-101b37359328 · outbound

This paper cites Prediction of emergency department patient disposition decision for proactive resource allocation for admission.

Towards Foundation Models for Critical Care Time Series Prediction of emergency department patient disposition decision for proactive resource allocation for admission

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:19:37.795147Z

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Observation 8cc77ca0-19f9-4d39-943e-e4f66e153a6f · outbound

This paper cites Paediatric intensive care database, 2019.

Towards Foundation Models for Critical Care Time Series Paediatric intensive care database, 2019

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:19:37.781655Z

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Observation a7f68b30-b995-4c39-a561-f8665e25b18c · outbound

This paper cites Decoupled Weight Decay Regularization.

Towards Foundation Models for Critical Care Time Series Decoupled Weight Decay Regularization

Reference 31

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Observation 0db653e9-6bc2-43fa-a402-9b498ee6ee5f · outbound

This paper cites u ser, Philip Hartout, Thomas Gumbsch, Martin Faltys, Tobias M Merz, Gunnar R \.

Towards Foundation Models for Critical Care Time Series u ser, Philip Hartout, Thomas Gumbsch, Martin Faltys, Tobias M Merz, Gunnar R \

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:19:37.768941Z

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Observation f15015e9-c7ec-4e8d-a22d-2be2298ecd2d · outbound

This paper cites Foundation models for generalist medical artificial intelligence.

Towards Foundation Models for Critical Care Time Series Foundation models for generalist medical artificial intelligence

Reference 33

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Observation 37d3c2ba-fd54-4959-bb04-052a1d8c33aa · outbound

This paper cites Predicting sepsis using deep learning across international sites: a retrospective development and validation study.

Towards Foundation Models for Critical Care Time Series Predicting sepsis using deep learning across international sites: a retrospective development and validation study

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:19:37.744739Z

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Observation 2300e29f-e61b-4789-92a4-5bc00d16b925 · outbound

This paper cites TorchMetrics - Measuring Reproducibility in PyTorch , February 2022.

Towards Foundation Models for Critical Care Time Series TorchMetrics - Measuring Reproducibility in PyTorch , February 2022

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:19:37.732926Z

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Observation b697df39-2c4e-4038-925d-314d16bb3002 · outbound

This paper cites Introducing the blendedicu dataset, the first harmonized, international intensive care dataset.

Towards Foundation Models for Critical Care Time Series Introducing the blendedicu dataset, the first harmonized, international intensive care dataset

Reference 36

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Observation 71460d93-2e63-46c6-a036-41d89a667d86 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

Towards Foundation Models for Critical Care Time Series Pytorch: An imperative style, high-performance deep learning library

Reference 37

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This paper cites The eicu collaborative research database, a freely available multi-center database for critical care research.

Towards Foundation Models for Critical Care Time Series The eicu collaborative research database, a freely available multi-center database for critical care research

Reference 38

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Observation 2b03325d-6076-4ce8-951b-aaec18295201 · outbound

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Towards Foundation Models for Critical Care Time Series Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-08-12T13:19:37.708814Z

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Observation e86896e8-8b9e-46fa-9552-1bb653f2062a · outbound

This paper cites The impact of multi-institution datasets on the generalizability of machine learning prediction models in the icu.

Towards Foundation Models for Critical Care Time Series The impact of multi-institution datasets on the generalizability of machine learning prediction models in the icu

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:19:37.697172Z

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Observation db2c4a3d-5406-4275-ad1c-8c9f5efc8078 · outbound

This paper cites Salzburg intensive care database (sicdb), a freely accessible intensive care database.

Towards Foundation Models for Critical Care Time Series Salzburg intensive care database (sicdb), a freely accessible intensive care database

Reference 41

Resolution: verified exact; doi, observed 2026-08-12T13:19:37.180535Z

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Observation 1151e314-cfb1-4ef6-9656-f6c3cf270a0e · outbound

This paper cites Benchmarking machine learning models on multi-centre eicu critical care dataset.

Towards Foundation Models for Critical Care Time Series Benchmarking machine learning models on multi-centre eicu critical care dataset

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:19:37.685282Z

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Observation 51b5c15b-4d65-4348-8360-31f21d714c4c · outbound

This paper cites Large language models encode clinical knowledge.

Towards Foundation Models for Critical Care Time Series Large language models encode clinical knowledge

Reference 43

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This paper cites Soenksen, Yu Ma, Cynthia Zeng, Leonard Boussioux, Kimberly Villalobos Carballo, Liangyuan Na, Holly M.

Towards Foundation Models for Critical Care Time Series Soenksen, Yu Ma, Cynthia Zeng, Leonard Boussioux, Kimberly Villalobos Carballo, Liangyuan Na, Holly M

Reference 44

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Observation ff75ebc9-f4d3-4fe5-bf6a-fdc87709e2f2 · outbound

This paper cites Democratizing ehr analyses with fiddle: a flexible data-driven preprocessing pipeline for structured clinical data.

Towards Foundation Models for Critical Care Time Series Democratizing ehr analyses with fiddle: a flexible data-driven preprocessing pipeline for structured clinical data

Reference 45

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Observation d39d8513-a17f-4d57-b3c9-663dc1b9087e · outbound

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Towards Foundation Models for Critical Care Time Series Unresolved cited work

Reference 46

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Observation adc2772d-f0d2-4895-875b-eadfc6b3cd3d · outbound

This paper cites Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Series.

Towards Foundation Models for Critical Care Time Series Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Series

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-12T13:19:37.073213Z

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Observation e718c9c1-45ba-4e14-9c4c-046babde1616 · outbound

This paper cites Yet Another ICU Benchmark: A Flexible Multi-Center Framework for Clinical ML.

Towards Foundation Models for Critical Care Time Series Yet Another ICU Benchmark: A Flexible Multi-Center Framework for Clinical ML

Reference 48

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This paper cites Visualizing data using t-sne.

Towards Foundation Models for Critical Care Time Series Visualizing data using t-sne

Reference 49

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Observation b019488c-9bf2-4d68-bcd9-9db00a2b5a44 · outbound

This paper cites Attention Is All You Need.

Towards Foundation Models for Critical Care Time Series Attention Is All You Need

Reference 50

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Observation e8850186-f48e-4a91-9725-cfba4784b2ed · outbound

This paper cites Virchow: A Million-Slide Digital Pathology Foundation Model.

Towards Foundation Models for Critical Care Time Series Virchow: A Million-Slide Digital Pathology Foundation Model

Reference 51

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Observation c7ddbbc8-a2e7-461c-a8bb-3fc0b43cf21f · outbound

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Towards Foundation Models for Critical Care Time Series Unresolved cited work

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-12T13:19:37.098091Z

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Observation 69824d7b-ebb5-4dd3-8256-6bdad929e673 · outbound

This paper cites Ehrshot: An ehr benchmark for few-shot evaluation of foundation models.

Towards Foundation Models for Critical Care Time Series Ehrshot: An ehr benchmark for few-shot evaluation of foundation models

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:19:37.647645Z

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Observation 6b707f4d-224b-4546-b532-30ab9bc55d04 · outbound

This paper cites EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Models.

Towards Foundation Models for Critical Care Time Series EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Models

Reference 54

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Observation 7210b1e7-6c19-4b16-ac15-4ec37fc041bb · outbound

This paper cites The shaky foundations of large language models and foundation models for electronic health records.

Towards Foundation Models for Critical Care Time Series The shaky foundations of large language models and foundation models for electronic health records

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:19:37.633049Z

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Observation a06dd421-9fa2-4b6b-b2d2-995d35e9be0b · outbound

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Towards Foundation Models for Critical Care Time Series Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-08-12T13:19:37.621063Z

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Observation f35f94c5-5ca3-4355-b226-d63a1eabb065 · outbound

This paper cites Pyhealth: A deep learning toolkit for healthcare applications.

Towards Foundation Models for Critical Care Time Series Pyhealth: A deep learning toolkit for healthcare applications

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:19:37.609755Z

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

Observation 18cbf87f-053e-472d-9a01-08f41a88e8d7 · outbound

This paper cites u ser, Xinrui Lyu, Martin Faltys, and Gunnar R\.

Towards Foundation Models for Critical Care Time Series u ser, Xinrui Lyu, Martin Faltys, and Gunnar R\

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:19:37.597753Z

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 bd7baa5e-9f40-4b75-9ba3-2c688bdb5f85 · outbound

This paper cites Dynamic Survival Analysis for Early Event Prediction.

Towards Foundation Models for Critical Care Time Series Dynamic Survival Analysis for Early Event Prediction

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-08-12T13:19:37.245304Z

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 faa0a2d9-14f1-4628-b46b-ea2ae077dce6 · outbound

This paper cites One fits all: Power general time series analysis by pretrained lm.

Towards Foundation Models for Critical Care Time Series One fits all: Power general time series analysis by pretrained lm

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:19:37.583604Z

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