Uncertainty-Aware Foundation Models for Clinical Data | Paper Citation Record

Observation a8fa1661-6657-48e6-8960-dd0477cd211e · outbound

This paper cites Foundation model for advancing healthcare: challenges, opportunities and future directions.IEEE Reviews in Biomedical Engineering.

Uncertainty-Aware Foundation Models for Clinical Data Foundation model for advancing healthcare: challenges, opportunities and future directions.IEEE Reviews in Biomedical Engineering

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.253373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3b0333a4-09e0-4cfb-a031-956f679312e5 · outbound

This paper cites Foundation models in bioinformatics.National science review, 12(4):nwaf028.

Uncertainty-Aware Foundation Models for Clinical Data Foundation models in bioinformatics.National science review, 12(4):nwaf028

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.261954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d0b51908-f90d-4f16-8640-40ad59a735ce · outbound

This paper cites Foundation models defining a new era in vision: a survey and outlook.IEEE Transactions on Pattern Analysis and Machine Intelligence.

Uncertainty-Aware Foundation Models for Clinical Data Foundation models defining a new era in vision: a survey and outlook.IEEE Transactions on Pattern Analysis and Machine Intelligence

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.258232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 49f3198d-2623-4f64-9e54-80fc1bdea33e · outbound

This paper cites Foundation models for time series analysis: A tutorial and survey.

Uncertainty-Aware Foundation Models for Clinical Data Foundation models for time series analysis: A tutorial and survey

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.270453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 125c0f5e-55ce-4b38-bfa0-511699ad70f8 · outbound

This paper cites A foundation model for intensive care: Unlocking generalization across tasks and domains at scale.medRxiv, pages 2025–07.

Uncertainty-Aware Foundation Models for Clinical Data A foundation model for intensive care: Unlocking generalization across tasks and domains at scale.medRxiv, pages 2025–07

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.245384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation fb148d09-8a75-4579-9445-07a3927ceacc · outbound

This paper cites Foundation models for time series forecasting.International IT Journal of Research, ISSN: 3007-6706, 2(4):144–156.

Uncertainty-Aware Foundation Models for Clinical Data Foundation models for time series forecasting.International IT Journal of Research, ISSN: 3007-6706, 2(4):144–156

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.249496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0367cf93-a231-43aa-bb29-7beff55c313e · outbound

This paper cites A foundational vision transformer improves diagnostic performance for electrocardiograms.NPJ Digital Medicine, 6(1):108.

Uncertainty-Aware Foundation Models for Clinical Data A foundational vision transformer improves diagnostic performance for electrocardiograms.NPJ Digital Medicine, 6(1):108

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.236954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3a16b0de-71cb-4aae-9e95-e4aa5c283269 · outbound

This paper cites Foundation models in healthcare: Opportunities, risks & strategies forward.

Uncertainty-Aware Foundation Models for Clinical Data Foundation models in healthcare: Opportunities, risks & strategies forward

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.195464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d4b44ecc-9f19-428e-9073-53cee575356f · outbound

This paper cites Foundation models for electronic health records: representation dynamics and transferability.

Uncertainty-Aware Foundation Models for Clinical Data Foundation models for electronic health records: representation dynamics and transferability

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:48:02.953390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1296c0e3-a976-4761-95d3-cb44cd2736ec · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

Uncertainty-Aware Foundation Models for Clinical Data Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.191498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 51f11ece-d706-49d2-a6ad-7a998a5272b7 · outbound

This paper cites Multi-scale 3d deep convolutional neural network for hyperspectral image classification.

Uncertainty-Aware Foundation Models for Clinical Data Multi-scale 3d deep convolutional neural network for hyperspectral image classification

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.203764Z

Source-reported events for the cited work

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Observation ce51bf16-e022-4c33-ad32-7456cb2947a8 · outbound

This paper cites Masked autoencoders are scalable vision learners.

Uncertainty-Aware Foundation Models for Clinical Data Masked autoencoders are scalable vision learners

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.216738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8e987fe0-6d8c-481d-9561-7ddba4cee4dd · outbound

This paper cites Deep residual learning for image recognition.

Uncertainty-Aware Foundation Models for Clinical Data Deep residual learning for image recognition

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.241075Z

Source-reported events for the cited work

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Observation 1cd48d04-1b05-45f3-b8ac-4d79ce330493 · outbound

This paper cites Bag of tricks for image classification with convolutional neural networks.

Uncertainty-Aware Foundation Models for Clinical Data Bag of tricks for image classification with convolutional neural networks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.182080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 838a6824-e533-4483-9051-0c2202f0e6be · outbound

This paper cites Cross attention network for few-shot classification.Advances in neural information processing systems, 32.

Uncertainty-Aware Foundation Models for Clinical Data Cross attention network for few-shot classification.Advances in neural information processing systems, 32

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.178062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c89852c4-1166-415a-a51b-d54814ba1727 · outbound

This paper cites Crossvit: Cross-attention multi-scale vision trans- former for image classification.

Uncertainty-Aware Foundation Models for Clinical Data Crossvit: Cross-attention multi-scale vision trans- former for image classification

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.199387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a64b4c90-8e11-4a7a-a791-b5a09056e14a · outbound

This paper cites Ccnet: Criss-cross attention for semantic segmentation.

Uncertainty-Aware Foundation Models for Clinical Data Ccnet: Criss-cross attention for semantic segmentation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.186614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 01e09b65-0922-48db-997e-4cf5f9cf577c · outbound

This paper cites Serialized ehr make for good text representations.arXiv preprint arXiv:2510.13843.

Uncertainty-Aware Foundation Models for Clinical Data Serialized ehr make for good text representations.arXiv preprint arXiv:2510.13843

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:48:02.941249Z

Source-reported events for the cited work

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Observation 5e42d66d-5608-4505-996f-9dd0e984d5a2 · outbound

This paper cites Clio: Policy-aware foundation models for ehr as controlled dynamical systems.Authorea Preprints.

Uncertainty-Aware Foundation Models for Clinical Data Clio: Policy-aware foundation models for ehr as controlled dynamical systems.Authorea Preprints

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.173792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0948474d-a82e-4def-bc51-3b0c276ab368 · outbound

This paper cites Structured Semantics from Unstructured Notes: Language Model Approaches to EHR-Based Decision Support.

Uncertainty-Aware Foundation Models for Clinical Data Structured Semantics from Unstructured Notes: Language Model Approaches to EHR-Based Decision Support

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:48:02.947601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9754c001-544b-4a4d-888b-8ee890cc1002 · outbound

This paper cites ChronoFormer: Time-Aware Transformer Architectures for Structured Clinical Event Modeling.

Uncertainty-Aware Foundation Models for Clinical Data ChronoFormer: Time-Aware Transformer Architectures for Structured Clinical Event Modeling

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:48:02.968899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 199bccce-ebd5-4314-b7a3-a4199a480151 · outbound

This paper cites A Collection of Innovations in Medical AI for patient records in 2024.

Uncertainty-Aware Foundation Models for Clinical Data A Collection of Innovations in Medical AI for patient records in 2024

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T16:48:02.950387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1edd9a38-2f94-4c29-9600-4d5ad92db61f · outbound

This paper cites Latent physiology as language: A state-space foundation model for multimodal icu and ehr representation learning.

Uncertainty-Aware Foundation Models for Clinical Data Latent physiology as language: A state-space foundation model for multimodal icu and ehr representation learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.224461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e05d9cdb-19db-4866-8df2-25c1f0684be9 · outbound

This paper cites Text as an inductive bias: A novel foundation model for electronic health records.

Uncertainty-Aware Foundation Models for Clinical Data Text as an inductive bias: A novel foundation model for electronic health records

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.220695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 06848e10-bfab-47e3-b093-ef176d60865a · outbound

This paper cites Foundation models for physiological signals: Opportunities and challenges.

Uncertainty-Aware Foundation Models for Clinical Data Foundation models for physiological signals: Opportunities and challenges

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.207940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9a160979-0b8c-449c-a8a5-9237c286b3a4 · outbound

This paper cites Large-scale Training of Foundation Models for Wearable Biosignals.

Uncertainty-Aware Foundation Models for Clinical Data Large-scale Training of Foundation Models for Wearable Biosignals

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:48:02.965997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5afe3e8f-72a3-4e2c-a966-864ad8232e80 · outbound

This paper cites Gfmbench-api: A standardized interface for benchmarking genomic foundation models.bioRxiv, pages 2026–02.

Uncertainty-Aware Foundation Models for Clinical Data Gfmbench-api: A standardized interface for benchmarking genomic foundation models.bioRxiv, pages 2026–02

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.212335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2b3dc8d7-0b4e-46cb-97c2-f571a293ffe0 · outbound

This paper cites Mutbert: probabilistic genome representation improves genomics foundation models.bioinformatics, 41(Supplement_1):i294–i303.

Uncertainty-Aware Foundation Models for Clinical Data Mutbert: probabilistic genome representation improves genomics foundation models.bioinformatics, 41(Supplement_1):i294–i303

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.228607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation fc36c4b2-6439-4020-8cf8-f43c04205127 · outbound

This paper cites Sleepfm: Multi-modal representation learning for sleep across brain activity, ecg and respiratory signals.

Uncertainty-Aware Foundation Models for Clinical Data Sleepfm: Multi-modal representation learning for sleep across brain activity, ecg and respiratory signals

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.232472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 413d59b7-f482-483a-8c9e-579f4d91fecb · outbound

This paper cites Wearable-Based Real-time Freezing of Gait Detection in Parkinson's Disease Using Self-Supervised Learning.

Uncertainty-Aware Foundation Models for Clinical Data Wearable-Based Real-time Freezing of Gait Detection in Parkinson's Disease Using Self-Supervised Learning

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:48:02.971544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3d8896db-ac43-4cd9-b01b-ccdbd015bfd0 · outbound

This paper cites JEPA-DNA: Grounding Genomic Foundation Models through Joint-Embedding Predictive Architectures.

Uncertainty-Aware Foundation Models for Clinical Data JEPA-DNA: Grounding Genomic Foundation Models through Joint-Embedding Predictive Architectures

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-26T03:05:00.608793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a0577c20-0b8e-4d09-bd53-7d2c063d7e49 · outbound

This paper cites Clinical ModernBERT: An efficient and long context encoder for biomedical text.

Uncertainty-Aware Foundation Models for Clinical Data Clinical ModernBERT: An efficient and long context encoder for biomedical text

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:48:02.956212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9c68d5fd-3058-4472-8019-0373ab0ccd10 · outbound

This paper cites EHRMamba: Towards Generalizable and Scalable Foundation Models for Electronic Health Records.

Uncertainty-Aware Foundation Models for Clinical Data EHRMamba: Towards Generalizable and Scalable Foundation Models for Electronic Health Records

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:48:02.980218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d953198d-ddd2-4377-a949-10d72b9efc26 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Uncertainty-Aware Foundation Models for Clinical Data A simple framework for contrastive learning of visual representations

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.266274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4f9acd49-1522-4995-8f4a-f525bd791f69 · outbound

This paper cites Contrastive representation distillation.arXiv.

Uncertainty-Aware Foundation Models for Clinical Data Contrastive representation distillation.arXiv

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:58:14.209010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0d96d1b4-17df-4853-9770-000a13ea8c4d · outbound

This paper cites Contrastive learning of preferences with a contextual infonce loss.

Uncertainty-Aware Foundation Models for Clinical Data Contrastive learning of preferences with a contextual infonce loss

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:58:14.202922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b288ce7c-763e-405e-8270-f2abf67c5ee2 · outbound

This paper cites Clinical decision support using pseudo-notes from multiple streams of ehr data.npj Digital Medicine, 8(1):394, July 2025.

Uncertainty-Aware Foundation Models for Clinical Data Clinical decision support using pseudo-notes from multiple streams of ehr data.npj Digital Medicine, 8(1):394, July 2025

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.324537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9e885469-7a1d-4057-acc0-85383ffd1df1 · outbound

This paper cites Med-bert: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction.NPJ digital medicine, 4(1):86.

Uncertainty-Aware Foundation Models for Clinical Data Med-bert: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction.NPJ digital medicine, 4(1):86

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:58:14.231525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 62f8c192-0531-4b97-83dd-719b7191cf82 · outbound

This paper cites Using foundation models to prescribe patients proper antibiotics.

Uncertainty-Aware Foundation Models for Clinical Data Using foundation models to prescribe patients proper antibiotics

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:58:14.226367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4267da8d-ce2e-4e6a-a224-fe173e2dab90 · outbound

This paper cites The shaky foundations of large language models and foundation models for electronic health records.npj digital medicine, 6(1):135.

Uncertainty-Aware Foundation Models for Clinical Data The shaky foundations of large language models and foundation models for electronic health records.npj digital medicine, 6(1):135

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:58:14.217457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2c126144-834b-4ec9-a459-b84708b0ae8e · outbound

This paper cites Emergency Department Decision Support using Clinical Pseudo-notes.

Uncertainty-Aware Foundation Models for Clinical Data Emergency Department Decision Support using Clinical Pseudo-notes

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:48:02.962300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ef806fa0-9fd6-48c5-8bc8-47c9366d4d54 · outbound

This paper cites Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901.

Uncertainty-Aware Foundation Models for Clinical Data Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.357958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4cf76689-687f-468b-9d2a-eb4449d15219 · outbound

This paper cites Cehr-bert: Incorporating temporal information from structured ehr data to improve prediction tasks.

Uncertainty-Aware Foundation Models for Clinical Data Cehr-bert: Incorporating temporal information from structured ehr data to improve prediction tasks

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.353619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 374cb8a3-04c4-4b2f-a696-96f49c9f4142 · outbound

This paper cites CEHR-GPT: Generating Electronic Health Records with Chronological Patient Timelines.

Uncertainty-Aware Foundation Models for Clinical Data CEHR-GPT: Generating Electronic Health Records with Chronological Patient Timelines

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:48:02.959379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2b1d3575-7601-469b-9f03-eedc8274aaed · outbound

This paper cites Event stream gpt: a data pre-processing and modeling library for generative, pre-trained transformers over continuous-time sequences of complex events.

Uncertainty-Aware Foundation Models for Clinical Data Event stream gpt: a data pre-processing and modeling library for generative, pre-trained transformers over continuous-time sequences of complex events

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:58:14.257795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7697774e-7e20-4236-bdb4-4c64c5f78137 · outbound

This paper cites LLMs-Healthcare : Current Applications and Challenges of Large Language Models in various Medical Specialties.

Uncertainty-Aware Foundation Models for Clinical Data LLMs-Healthcare : Current Applications and Challenges of Large Language Models in various Medical Specialties

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Resolution: verified exact; local_arxiv, observed 2026-05-13T16:48:02.974305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1769fdc6-1a50-4f0e-a8b6-7b857bdd18a2 · outbound

This paper cites Llm4ts: Aligning pre-trained llms as data-efficient time-series forecasters.ACM Transactions on Intelligent Systems and Technology, 16(3):1–20.

Uncertainty-Aware Foundation Models for Clinical Data Llm4ts: Aligning pre-trained llms as data-efficient time-series forecasters.ACM Transactions on Intelligent Systems and Technology, 16(3):1–20

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:58:14.236246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e006cb9c-3faa-4113-af95-9d07dac373d9 · outbound

This paper cites Accurate predictions on small data with a tabular foundation model.Nature, 637(8045):319–326.

Uncertainty-Aware Foundation Models for Clinical Data Accurate predictions on small data with a tabular foundation model.Nature, 637(8045):319–326

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.316457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation cb35f4d3-4a38-462a-a8ab-11f0be2231e8 · outbound

This paper cites Text Serialization and Their Relationship with the Conventional Paradigms of Tabular Machine Learning.

Uncertainty-Aware Foundation Models for Clinical Data Text Serialization and Their Relationship with the Conventional Paradigms of Tabular Machine Learning

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:48:02.944246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e5e25650-49d8-4776-959a-21c28d75db9a · outbound

This paper cites Clinical text summarization: Adapting large language models can outperform human experts.Research Square.

Uncertainty-Aware Foundation Models for Clinical Data Clinical text summarization: Adapting large language models can outperform human experts.Research Square

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:58:14.254890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c2f41962-7d67-4e22-9301-a32995e5a6c8 · outbound

This paper cites Time-LLM: Time Series Forecasting by Reprogramming Large Language Models.

Uncertainty-Aware Foundation Models for Clinical Data Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-16T16:03:17.367885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 616b92d5-340b-4a47-94a5-0ded874ad131 · outbound

This paper cites Multimodal llms for health grounded in individual- specific data.

Uncertainty-Aware Foundation Models for Clinical Data Multimodal llms for health grounded in individual- specific data

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.299428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 91965739-6775-49c3-8237-4780f800b19a · outbound

This paper cites A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs.

Uncertainty-Aware Foundation Models for Clinical Data A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:48:02.991722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ffce0a57-284e-46ca-b835-5279cd1d696f · outbound

This paper cites Deep residual learning for image recognition.

Uncertainty-Aware Foundation Models for Clinical Data Deep residual learning for image recognition

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.361784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 489a11ba-ce36-4502-b1ed-a37c78e73277 · outbound

This paper cites A computer-aided detection system for the detection of lung nodules based on 3d-resnet.Applied Sciences, 9(24):5544.

Uncertainty-Aware Foundation Models for Clinical Data A computer-aided detection system for the detection of lung nodules based on 3d-resnet.Applied Sciences, 9(24):5544

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.287551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c5ce631f-2e0f-44fe-8eea-5ebf88a6ccd3 · outbound

This paper cites Introducing transfer learning to 3d resnet-18 for alzheimer’s disease detection on mri images.

Uncertainty-Aware Foundation Models for Clinical Data Introducing transfer learning to 3d resnet-18 for alzheimer’s disease detection on mri images

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:58:14.212992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3ee6df90-1f91-43e0-b0ab-ae3a456a2ce7 · outbound

This paper cites Automatic segmentation of head and neck (h&n) primary tumors in pet and ct images using 3d-inception-resnet model.

Uncertainty-Aware Foundation Models for Clinical Data Automatic segmentation of head and neck (h&n) primary tumors in pet and ct images using 3d-inception-resnet model

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.283422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7ce180b5-cda9-4e96-863c-7a14e16e5150 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Uncertainty-Aware Foundation Models for Clinical Data An image is worth 16x16 words: Transformers for image recognition at scale

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:58:14.251374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0a7487ee-0afa-42de-8dd9-5a74138eeba0 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Uncertainty-Aware Foundation Models for Clinical Data Swin transformer: Hierarchical vision transformer using shifted windows

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.320476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5e0a464a-b9a6-46a4-be8e-7769122822af · outbound

This paper cites Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images.

Uncertainty-Aware Foundation Models for Clinical Data Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.311879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ef3dff09-b252-46fc-9506-2f88f279df8a · outbound

This paper cites Abdomenatlas: A large-scale, detailed-annotated, & multi-center dataset for efficient transfer learning and open algorithmic benchmarking.Medical Image Analysis, 97:103285.

Uncertainty-Aware Foundation Models for Clinical Data Abdomenatlas: A large-scale, detailed-annotated, & multi-center dataset for efficient transfer learning and open algorithmic benchmarking.Medical Image Analysis, 97:103285

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:58:14.221789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d04633bc-a7df-4357-9c42-fffc7090129e · outbound

This paper cites MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset.

Uncertainty-Aware Foundation Models for Clinical Data MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset

Reference 62

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:48:03.001740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7adc6c35-8264-4fc0-a4f8-04157da01e3b · outbound

This paper cites Large-Scale 3D Medical Image Pre-training with Geometric Context Priors.

Uncertainty-Aware Foundation Models for Clinical Data Large-Scale 3D Medical Image Pre-training with Geometric Context Priors

Reference 63

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:48:02.982897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1c383437-5415-416e-87ce-b6917fe3a6d2 · outbound

This paper cites Emerging properties in self-supervised vision transformers.

Uncertainty-Aware Foundation Models for Clinical Data Emerging properties in self-supervised vision transformers

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.278860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8d741e8a-761a-4c7e-ba9c-8b2a9f9ba09a · outbound

This paper cites ibot: Image bert pre-training with online tokenizer.International Conference on Learning Representations (ICLR).

Uncertainty-Aware Foundation Models for Clinical Data ibot: Image bert pre-training with online tokenizer.International Conference on Learning Representations (ICLR)

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.349490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8fd717a3-b109-4777-a39e-f8992a8ccbd3 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Uncertainty-Aware Foundation Models for Clinical Data DINOv2: Learning Robust Visual Features without Supervision

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-05-13T16:48:02.988757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c97c4002-4340-4db4-b193-308d340b61ab · outbound

This paper cites Learning transferable visual models from natural language supervision.

Uncertainty-Aware Foundation Models for Clinical Data Learning transferable visual models from natural language supervision

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.291781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 389e0a6a-3cd1-4759-b175-f413abb06445 · outbound

This paper cites FlashAttention-2: Faster attention with better parallelism and work partitioning.

Uncertainty-Aware Foundation Models for Clinical Data FlashAttention-2: Faster attention with better parallelism and work partitioning

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.295750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation afd3dcac-288c-49f9-a53c-4fad2b6d02ba · outbound

This paper cites Unetr++: delving into efficient and accurate 3d medical image segmentation.IEEE Transactions on Medical Imaging.

Uncertainty-Aware Foundation Models for Clinical Data Unetr++: delving into efficient and accurate 3d medical image segmentation.IEEE Transactions on Medical Imaging

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.303459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation fd0b8d68-7bb2-4068-a46d-b7eefac8ff87 · outbound

This paper cites Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation.

Uncertainty-Aware Foundation Models for Clinical Data Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.274521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 31b57fe0-48b0-4c3e-8abc-200a6234bada · outbound

This paper cites OCTCube-M: A 3D multimodal optical coherence tomography foundation model for retinal and systemic diseases with cross-cohort and cross-device validation.

Uncertainty-Aware Foundation Models for Clinical Data OCTCube-M: A 3D multimodal optical coherence tomography foundation model for retinal and systemic diseases with cross-cohort and cross-device validation

Reference 71

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:48:02.985629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation eee9d790-91ab-4751-b596-24600160764c · outbound

This paper cites Wearable Accelerometer Foundation Models for Health via Knowledge Distillation.

Uncertainty-Aware Foundation Models for Clinical Data Wearable Accelerometer Foundation Models for Health via Knowledge Distillation

Reference 72

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T16:48:02.998344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3335b858-1b87-4c7d-891b-2b540f5cc81c · outbound

This paper cites Brandon Westover, and Jimeng Sun.

Uncertainty-Aware Foundation Models for Clinical Data Brandon Westover, and Jimeng Sun

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:58:14.240022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 68f6ddd9-7a92-4b29-986f-185bda8f4e18 · outbound

This paper cites Pearson Education India.

Uncertainty-Aware Foundation Models for Clinical Data Pearson Education India

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:58:14.247745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2093ca11-5a24-44c8-a76f-3e77eb5d55bc · outbound

This paper cites SIAM.

Uncertainty-Aware Foundation Models for Clinical Data SIAM

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:58:14.244225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8e2c24b2-c3ab-4138-8d9e-ffb9d812a4a5 · outbound

This paper cites Towards on-device foundation models for raw wearable signals.

Uncertainty-Aware Foundation Models for Clinical Data Towards on-device foundation models for raw wearable signals

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:53:16.307491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6ae8a80e-2c1b-4db7-a4d4-30f1e3d4a443 · outbound

This paper cites Himae: Hierarchical masked autoencoders discover resolution-specific structure in wearable time series.arXiv preprint arXiv:2510.25785, October.

Uncertainty-Aware Foundation Models for Clinical Data Himae: Hierarchical masked autoencoders discover resolution-specific structure in wearable time series.arXiv preprint arXiv:2510.25785, October

Reference 77

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:48:02.928080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 722df523-d5c6-4bb7-9469-9ebf96e57046 · outbound

This paper cites Meds: Building models and tools in a reproducible health ai ecosystem.

Uncertainty-Aware Foundation Models for Clinical Data Meds: Building models and tools in a reproducible health ai ecosystem

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:58:14.270551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 73ea0347-9315-461f-9e57-d3572b33ffb7 · outbound

This paper cites Meds decentralized, extensible validation (meds-dev) benchmark: Establishing reproducibility and comparability in ml for health.

Uncertainty-Aware Foundation Models for Clinical Data Meds decentralized, extensible validation (meds-dev) benchmark: Establishing reproducibility and comparability in ml for health

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:58:14.280827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 78bd0ce5-7a4e-4454-b8c8-7ea55c1774a2 · outbound

This paper cites Context Clues: Evaluating Long Context Models for Clinical Prediction Tasks on EHRs.

Uncertainty-Aware Foundation Models for Clinical Data Context Clues: Evaluating Long Context Models for Clinical Prediction Tasks on EHRs

Reference 80

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:48:02.931380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 95ba5e1d-5667-488b-9bea-65ee7a0764f6 · outbound

This paper cites CORE-BEHRT: A Carefully Optimized and Rigorously Evaluated BEHRT.

Uncertainty-Aware Foundation Models for Clinical Data CORE-BEHRT: A Carefully Optimized and Rigorously Evaluated BEHRT

Reference 81

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:48:02.934376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f9609f3b-aea0-4ad1-bfa2-676a6b96545c · outbound

This paper cites Learning the natural history of human disease with generative transformers.

Uncertainty-Aware Foundation Models for Clinical Data Learning the natural history of human disease with generative transformers

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:58:14.278187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6d963eb6-a00f-4416-b160-1172a12e928e · outbound

This paper cites Self-supervised contrastive pre-training for time series via time-frequency consistency.

Uncertainty-Aware Foundation Models for Clinical Data Self-supervised contrastive pre-training for time series via time-frequency consistency

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:58:14.286608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 78b98cd6-2955-474b-b6cc-6672c3d30848 · outbound

This paper cites A comprehensive survey on contrastive learning.

Uncertainty-Aware Foundation Models for Clinical Data A comprehensive survey on contrastive learning

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:58:14.265496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c4aec3c1-0ccc-4a58-a830-1f07abf5a0dd · outbound

This paper cites A survey on contrastive self-supervised learning.Technologies, 9(1):2.

Uncertainty-Aware Foundation Models for Clinical Data A survey on contrastive self-supervised learning.Technologies, 9(1):2

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:58:14.268057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 052e7439-86f6-43c4-913b-2b0c2e83072b · outbound

This paper cites Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling.

Uncertainty-Aware Foundation Models for Clinical Data Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling

Reference 86

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T16:48:02.938103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0a95a5bf-09ef-4ac2-8c42-d72e212e52fe · outbound

This paper cites Exchangeable Sequence Models Quantify Uncertainty Over Latent Concepts.

Uncertainty-Aware Foundation Models for Clinical Data Exchangeable Sequence Models Quantify Uncertainty Over Latent Concepts

Reference 87

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:48:02.924465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7ef24ab6-708e-4584-9d50-4ceb75e81321 · outbound

This paper cites Lind, Eric Monteiro, and Anis Yazidi.

Uncertainty-Aware Foundation Models for Clinical Data Lind, Eric Monteiro, and Anis Yazidi

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:58:14.275869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 08676d79-4b46-4435-b659-9deb45c2b505 · outbound

This paper cites Aleatoric and epistemic uncertainty in machine learning: An introduc- tion to concepts and methods.Machine learning, 110(3):457–506.

Uncertainty-Aware Foundation Models for Clinical Data Aleatoric and epistemic uncertainty in machine learning: An introduc- tion to concepts and methods.Machine learning, 110(3):457–506

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:58:14.283459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 24687158-9bc8-47b9-b8df-68978740d8db · outbound

This paper cites Towards trustworthy ai in healthcare: Epistemic uncertainty estimation for clinical decision support.Journal of Personalized Medicine, 15(2):58.

Uncertainty-Aware Foundation Models for Clinical Data Towards trustworthy ai in healthcare: Epistemic uncertainty estimation for clinical decision support.Journal of Personalized Medicine, 15(2):58

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:58:14.273427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1066c322-3270-4da3-a526-d9c755c8f33f · outbound

This paper cites Mimic-iii, a freely accessible critical care database.Scientific data, 3(1):1–9.

Uncertainty-Aware Foundation Models for Clinical Data Mimic-iii, a freely accessible critical care database.Scientific data, 3(1):1–9

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:58:14.260369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation cffd63d0-369b-46cc-86dc-70aae19153c4 · outbound

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

Uncertainty-Aware Foundation Models for Clinical Data Mimic-iv, a freely accessible electronic health record dataset

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T05:58:14.263103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers