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Paper Citation Record · LEDGER

Towards Foundation Models for Critical Care Time Series

As of 13 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 0 inbound Pith citation observations for arXiv:2411.16346.

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

pith.paper-citation-record.v1
2411.16346 v1

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:19:37.134505Z

measured 60 of 60 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

60 of 60 outbound references displayed

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  • verified fuzzy23
  • unresolved33
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

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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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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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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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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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

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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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

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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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

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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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

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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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

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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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

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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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

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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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

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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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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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

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-12T13:19:36.989285Z digest=sha256:cd35689c9bd6e1713ff7c736b256dca5cdfaed344a99a270ccfccb4cb7c234a1

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

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raw_fallback, observed 2026-08-12T13:19:37.781655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-12T13:19:36.993653Z digest=sha256:4c8baba731d22fd2f88c1ef28ca6794eed3cfa8adf11ef88bff785b90d570914

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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source=arxiv_source observed=2026-08-12T13:19:36.998117Z digest=sha256:783fac3051659a92cf97c6cc3005aee671df1821169e1a71113f08ea574ccfef

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

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raw_fallback, observed 2026-08-12T13:19:37.768941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-12T13:19:37.003087Z digest=sha256:dae01780e364a97059194db81cf2232497e5c00d9377880f86abcf47aa7995b3

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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raw_fallback, observed 2026-08-12T13:19:37.757256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-12T13:19:37.007356Z digest=sha256:728ce3cb5714647569ed48d51c799b381de732e8a9e3f881a78044c139239c3f

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

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raw_fallback, observed 2026-08-12T13:19:37.744739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-12T13:19:37.011651Z digest=sha256:11e735350880c08933241bbc38391b84e73d5c01e6e98189a24ed0d6e1bb9f0d

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

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raw_fallback, observed 2026-08-12T13:19:37.732926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-12T13:19:37.016233Z digest=sha256:73b1f2b5bd580e756a5724da5618952cb7d577fdb91686543014c4eb47efa740

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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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T13:19:37.021013Z digest=sha256:dff9060227efb7b253469b1f1b59260b3d435e875dd4af88ddcf992fa3dba9b0

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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unresolved
no resolver link, observed 2026-08-12T13:19:37.025549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T13:19:37.025549Z digest=sha256:d58026d54c150156d9d5cc085ce8a15b7262934e812a41fe70d37a11cf0bbb8f

Observation b254df23-b79c-4217-a2cf-645e96840b94 · outbound

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

Resolution
unresolved
no resolver link, observed 2026-08-12T13:19:37.030072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T13:19:37.030072Z digest=sha256:d27ffd6307de355984eb042efeb6c398341e81085bea1d64a4b3cf43e033bffd

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

This paper cites an unresolved cited work.

Towards Foundation Models for Critical Care Time Series Unresolved cited work

Reference 39

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-12T13:19:37.034559Z digest=sha256:c2529415e037d86b04e16af8280369b369bdde5ee59f4fbcaa9d808dc90107b3

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-12T13:19:37.039194Z digest=sha256:3a0789cfabdcb94bbc943d0ada44e5b4f979c60c964bc44f41b4ce77b94fb6cf

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

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-12T13:19:37.043825Z digest=sha256:b0f24bd0def14ecb1d58f377296de621ccdd074ec2388256d3b9ac371ca3e785

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-12T13:19:37.048270Z digest=sha256:7f1cf0c2339fd44681869cce6964b086456261b89435f11ead9756f09eb35e41

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

Resolution
unresolved
no resolver link, observed 2026-08-12T13:19:37.053078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T13:19:37.053078Z digest=sha256:8577f81130eecc96ac212464f79b18b7f9ce07ac1d092bfb3f4bdff1588deee8

Observation d135adf1-9a31-45b3-81e9-98bbca7162ae · outbound

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

Resolution
unresolved
no resolver link, observed 2026-08-12T13:19:37.057688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T13:19:37.057688Z digest=sha256:77a4c4491d29a3dfb70fbaa0d80b2b32db26143f8f05479c17955b99a6114aaa

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

Resolution
unresolved
no resolver link, observed 2026-08-12T13:19:37.062176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T13:19:37.062176Z digest=sha256:f2962011d05ec511e6cb0b8237323d32599fd4f44757b36d792a95ab2285c927

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

This paper cites an unresolved cited work.

Towards Foundation Models for Critical Care Time Series Unresolved cited work

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-12T13:19:37.066312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T13:19:37.066312Z digest=sha256:8e2dfd59b3986fd58297294cc2243ff24f8b0334d5535dd286225b583b5d47f3

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T13:19:37.073213Z digest=sha256:821b632243860a31f2d306bd626745919a5567b58488f262541e00952e9451f3

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

Resolution
unresolved
no resolver link, observed 2026-08-12T13:19:37.078973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T13:19:37.078973Z digest=sha256:6095904e7fdef0631acbc113a0e9c5b232b571237bb876901a7bc271663227ed

Observation 12dabe76-f544-4ac5-bd98-fa8873ac459b · outbound

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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unresolved
no resolver link, observed 2026-08-12T13:19:37.083858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T13:19:37.083858Z digest=sha256:5dcf816b6f8eb7955f051575ffd1f156c168ef43b9e6481285a309c41a4e48d0

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

Resolution
unresolved
no resolver link, observed 2026-08-12T13:19:37.088281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T13:19:37.088281Z digest=sha256:745b29bf782d459c4772271362937176dd88c37ade63b4dd123e560456a34a21

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

Resolution
unresolved
no resolver link, observed 2026-08-12T13:19:37.093585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T13:19:37.093585Z digest=sha256:5f0e760b0fd8e7973ca4000248d295fc3612e81ec8651e496b2af8cb3a6430c1

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

This paper cites an unresolved cited work.

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T13:19:37.098091Z digest=sha256:00d3ded4c04314fbd871a31ad59b31f67692fb0706e80ce7ae7f23085bdb50d3

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-12T13:19:37.102700Z digest=sha256:6b2ba237d4d8e20bfd6bd178bd4766cf2fa24d14628f33c5d8abc639864aa113

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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unresolved
no resolver link, observed 2026-08-12T13:19:37.107022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T13:19:37.107022Z digest=sha256:6c5a777f610be9a689193ade5d304d8189ef4772364728f8bcf093873566a7d6

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-12T13:19:37.111981Z digest=sha256:0d0388f759f69714865374f2dc5b17fe5a48eea04bce1e75d57d0a89d3f183bf

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

This paper cites an unresolved cited work.

Towards Foundation Models for Critical Care Time Series Unresolved cited work

Reference 56

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-12T13:19:37.116456Z digest=sha256:96fd7e3e6b2e7c23fef3042d019661b393ff4223cdea7127a17b0816aeacfdc7

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-12T13:19:37.120915Z digest=sha256:d77a40a1621ee5b99d31ad760e32ae220d310d709e1014c69e059d8170972647

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 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-12T13:19:37.125291Z digest=sha256:cc26787ef08566d0cd50bd07355af884d30e8575fce5ae8cefe789ca04f7c59f

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 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-12T13:19:37.129986Z digest=sha256:4d3bb99c9d3fc033415ce5820b357d420149fed85eba24fe2da1624a371fd88b

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 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-12T13:19:37.134505Z digest=sha256:953974dc4da8886eb513986d78d69c84c6394b9991efa6258a08d61d9a6d026b

Pith citing papers

No inbound Pith citation observations are available.