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Paper Citation Record · LEDGER

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide

As of 22 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2501.10240.

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

pith.paper-citation-record.v1
2501.10240 v2

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T19:21:55.715745Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

46 of 46 outbound references displayed

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External citation measurements

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Outbound references

Observation 85b0617c-bfa4-4a76-b53f-3ebeb97d74ed · outbound

This paper cites Modeling longitudinal biomarker data from multiple assays that have different known detection limits.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Modeling longitudinal biomarker data from multiple assays that have different known detection limits

Reference 1

Resolution
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Observation c7ba01a2-49ce-424b-9be3-019d6532b425 · outbound

This paper cites Comparison of static and dynamic random forests models for EHR data in the presence of competing risks: predicting central line-associated bloodstream infection.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Comparison of static and dynamic random forests models for EHR data in the presence of competing risks: predicting central line-associated bloodstream infection

Reference 2

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Observation 9c0d5848-857c-4ec9-bd6e-431b0d34e4a3 · outbound

This paper cites Lessons and tips for designing a machine learning study using ehr data.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Lessons and tips for designing a machine learning study using ehr data

Reference 3

Resolution
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Source-reported events for the cited work

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Observation ca765c7f-4cfd-4840-9ef8-a745d56eedcb · outbound

This paper cites Increasing trust in real-world evidence through evaluation of observational data quality.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Increasing trust in real-world evidence through evaluation of observational data quality

Reference 4

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 60cc5125-2fc7-4ef7-8961-9e2f737a1631 · outbound

This paper cites Guiding principles to address the impact of algorithm bias on racial and ethnic disparities in health and health care.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Guiding principles to address the impact of algorithm bias on racial and ethnic disparities in health and health care

Reference 5

Resolution
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Source-reported events for the cited work

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Observation 762b1f4c-3a77-4a03-a1a6-d637b6aa38ce · outbound

This paper cites A guide to sharing open healthcare data under the general data protection regulation.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide A guide to sharing open healthcare data under the general data protection regulation

Reference 6

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 337be6e9-83f6-47b1-8cde-fcd4d08e851c · outbound

This paper cites Table 0; documenting the steps to go from clinical database to research dataset.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Table 0; documenting the steps to go from clinical database to research dataset

Reference 7

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 0ee86db3-6c84-4992-8aac-6d8fe194adf5 · outbound

This paper cites Preprocessing structured clinical data for predictive modeling and decision support.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Preprocessing structured clinical data for predictive modeling and decision support

Reference 8

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 857a70a1-d58e-4ca4-9c0f-1c08381d385a · outbound

This paper cites A comparison of regression models for static and dynamic prediction of a prognostic outcome during admission in electronic health care records.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide A comparison of regression models for static and dynamic prediction of a prognostic outcome during admission in electronic health care records

Reference 9

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Source-reported events for the cited work

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Observation 9cb0cf9e-b64f-45a2-b65e-7b874ce87414 · outbound

This paper cites A review of challenges and opportunities in machine learning for health.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide A review of challenges and opportunities in machine learning for health

Reference 10

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation d503fd66-e8cd-4307-9624-693039f47624 · outbound

This paper cites Five analytic challenges in working with electronic health records data to support clinical trials with some solutions.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Five analytic challenges in working with electronic health records data to support clinical trials with some solutions

Reference 11

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation fd9a9a29-a01f-4244-93a7-ef6cc3ae18cd · outbound

This paper cites Improved reporting of selection processes in clinical database research.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Improved reporting of selection processes in clinical database research

Reference 12

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 0d48927b-edba-469a-a134-e02f1c3b48f1 · outbound

This paper cites An extensive data processing pipeline for mimic-iv.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide An extensive data processing pipeline for mimic-iv

Reference 13

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 9d79b152-1384-40d0-bb44-ff4d8ba043ff · outbound

This paper cites Minimar (minimum information for medical ai reporting): Developing reporting standards for artificial intelligence in health care.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Minimar (minimum information for medical ai reporting): Developing reporting standards for artificial intelligence in health care

Reference 14

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 8f45a004-2dd2-48a8-a9da-02976a95a3ea · outbound

This paper cites Challenges and recommendations for high quality research using electronic health records.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Challenges and recommendations for high quality research using electronic health records

Reference 15

Resolution
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Source-reported events for the cited work

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Reference 16

Resolution
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Observation aa331fb4-b233-4654-9f7f-aec2a53ea986 · outbound

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

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Mimic-iii, a freely accessible critical care database

Reference 17

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation e3992074-20bb-4df8-95ce-2113e942a14d · outbound

This paper cites Reproducibility in critical care: a mortality prediction case study.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Reproducibility in critical care: a mortality prediction case study

Reference 18

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 9196bb7c-0a43-403b-bbad-2cb2ea3acc9f · outbound

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

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Mimic-iv, a freely accessible electronic health record dataset

Reference 19

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation c273ee69-e36c-4626-a721-36ee57052fc1 · outbound

This paper cites Dynamic-deephit: A deep learning approach for dynamic survival analysis with competing risks based on longitudinal data.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Dynamic-deephit: A deep learning approach for dynamic survival analysis with competing risks based on longitudinal data

Reference 20

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 94c4b342-776c-4eb1-95e6-f00a9c27e25d · outbound

This paper cites Clinical implementation of predictive models embedded within electronic health record systems: a systematic review.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Clinical implementation of predictive models embedded within electronic health record systems: a systematic review

Reference 21

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation d8e42938-f936-4f02-98b4-90227963f7fe · outbound

This paper cites Electronic health record data quality assessment and tools: a systematic review.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Electronic health record data quality assessment and tools: a systematic review

Reference 22

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation d42348fe-1371-4b3e-ae79-6e8dfd2decaa · outbound

This paper cites Changing predictor measurement procedures affected the performance of prediction models in clinical examples.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Changing predictor measurement procedures affected the performance of prediction models in clinical examples

Reference 23

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation bf539b69-7e62-4773-8e2b-3d1fce25cd66 · outbound

This paper cites Lifting hospital electronic health record data treasures: challenges and opportunities.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Lifting hospital electronic health record data treasures: challenges and opportunities

Reference 24

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 9661cfc1-401a-4bb6-903d-3392198a6ca0 · outbound

This paper cites A data preparation framework for cleaning electronic health records and assessing cleaning outcomes for secondary analysis.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide A data preparation framework for cleaning electronic health records and assessing cleaning outcomes for secondary analysis

Reference 25

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 56be62d0-b4b4-4dcb-b383-212c92f39570 · outbound

This paper cites The ai life cycle: a holistic approach to creating ethical ai for health decisions.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide The ai life cycle: a holistic approach to creating ethical ai for health decisions

Reference 26

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 760c4eb7-f0b5-4b3a-a8bb-d0724cbdd98b · outbound

This paper cites Evaluation of data quality of multisite electronic health record data for secondary analysis.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Evaluation of data quality of multisite electronic health record data for secondary analysis

Reference 27

Resolution
verified fuzzy
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Source-reported events for the cited work

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Reference 28

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 8e595218-3011-4c53-ad05-9a4460f20ba6 · outbound

This paper cites Measuring diagnoses: Icd code accuracy.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Measuring diagnoses: Icd code accuracy

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:21:56.259451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation fc6200d8-c3a7-4cc1-9d22-7b5afc0a6ab8 · outbound

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

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide The eicu collaborative research database, a freely available multi-center database for critical care research

Reference 30

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 3c4c937b-5626-4d7c-9cd3-bf498a4ba948 · outbound

This paper cites Assessment of prediction tasks and time window selection in temporal modeling of electronic health record data: a systematic review.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Assessment of prediction tasks and time window selection in temporal modeling of electronic health record data: a systematic review

Reference 31

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 89326123-16f0-46ef-affb-14fd1a76c7ee · outbound

This paper cites Leveraging electronic health records for data science: common pitfalls and how to avoid them.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Leveraging electronic health records for data science: common pitfalls and how to avoid them

Reference 32

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation c686caec-5f71-43c5-931e-8da63c66b4f8 · outbound

This paper cites The metric-framework for assessing data quality for trustworthy ai in medicine: a systematic review.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide The metric-framework for assessing data quality for trustworthy ai in medicine: a systematic review

Reference 33

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-10T19:21:55.642798Z digest=sha256:fc40b4ebe1ea9ed6d9c89210b842380aaf907f29ad8758f985483b1c6dd2b9b1

Observation f372c969-f89e-4121-bea9-d65dbebbf6c2 · outbound

This paper cites Barriers to achieving economies of scale in analysis of ehr data.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Barriers to achieving economies of scale in analysis of ehr data

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:21:56.118954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-10T19:21:55.646463Z digest=sha256:48c6f5fc92d7ec3e1e341e96ff054946dd0d697c4cf7dda46adb6ae84303970e

Observation ab72ba6b-8fe1-41ea-aff5-73e2dd76125c · outbound

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

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Democratizing ehr analyses with fiddle: a flexible data-driven preprocessing pipeline for structured clinical data

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:21:56.095125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-10T19:21:55.649979Z digest=sha256:2b1108ca1218092dc30f70d9cdf896e8ccb74333cd75ec048aaa6bd305ff87f0

Observation 8dd00a35-9fc6-4e85-898c-dca142296733 · outbound

This paper cites an unresolved cited work.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Unresolved cited work

Reference 36

Resolution
unresolved
raw_fallback, observed 2026-08-10T19:21:56.079808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-10T19:21:55.655222Z digest=sha256:fc00a228cd361b3a77b167dce9bca4111b2e0910e1dbfc66bc12a10c40896ca9

Observation 660f0686-606f-4fb4-b0c2-37cb1afb30b3 · outbound

This paper cites Assessing the suitability of general practice electronic health records for clinical prediction model development: a data quality assessment.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Assessing the suitability of general practice electronic health records for clinical prediction model development: a data quality assessment

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:21:56.047641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-10T19:21:55.664754Z digest=sha256:18aaae2c078403ac12527101d7c5259a0132e134416518fb428a4830ca9e7d09

Observation 30b3eedd-b918-48d8-9e2b-01dd625a9b37 · outbound

This paper cites A clinically applicable approach to continuous prediction of future acute kidney injury.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide A clinically applicable approach to continuous prediction of future acute kidney injury

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:21:56.024957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-10T19:21:55.669447Z digest=sha256:afd00d0ac3cd0c45efd47bd1ad38a39ea2c3d1d0af40104dc159f0e70c368526

Observation 8ddd05f8-4caa-458b-a98d-4293f4335118 · outbound

This paper cites Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:21:56.012404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-10T19:21:55.673517Z digest=sha256:7eb2ec4ea163d6ecf5b7c6e1dcefb7b4ae47725bc0334107d0c8075d4545208c

Observation 119a8164-6658-4707-96f5-2b9a82c32d65 · outbound

This paper cites Data resource profile: the dutch national intensive care evaluation (nice) registry of admissions to adult intensive care units.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Data resource profile: the dutch national intensive care evaluation (nice) registry of admissions to adult intensive care units

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:21:55.994754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-10T19:21:55.677954Z digest=sha256:012172ddbcbbfe8cc5334932b3073761476a14e0ccfe9e3683019d422274742c

Observation 1248be26-54ee-4652-90b1-d118586c8838 · outbound

This paper cites Use and abuse of computer-stored medical records.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Use and abuse of computer-stored medical records

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:21:55.955840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-10T19:21:55.684758Z digest=sha256:889caa7e91ff107e038363c81e0517898ef540bd3ed5e22e3ec884b547cd2b2f

Observation 49448e37-688d-4045-8eaa-e18d46d7f2a1 · outbound

This paper cites Developing clinical prediction models using primary care electronic health record data: The impact of data preparation choices on model performance.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Developing clinical prediction models using primary care electronic health record data: The impact of data preparation choices on model performance

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:21:55.939851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-10T19:21:55.698668Z digest=sha256:6a1bd95d7b89ce870caeb93daa8f19192947d845ce55af3610085b5304e4577b

Observation e55fcdc7-12dd-4aa3-991f-50aa96aa3145 · outbound

This paper cites Attention is all you need.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Attention is all you need

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-10T19:21:55.702767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T19:21:55.702767Z digest=sha256:e0049e6c3687697c80d0737560ca50344287f5621367336ea3886eab65f87d13

Observation 8072ef16-bf9f-4dd1-976a-c2107b5dde7f · outbound

This paper cites Mimic-extract: A data extraction, preprocessing, and representation pipeline for mimic-iii.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Mimic-extract: A data extraction, preprocessing, and representation pipeline for mimic-iii

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:21:55.899217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-10T19:21:55.707123Z digest=sha256:c7488dbf0ab3771af9b84df75d7827aec7709e89a37b501fa15b58ba8637d2f2

Observation 8a6be9ea-67fd-4a4a-9893-a9d3ef66cf2f · outbound

This paper cites Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:21:55.880839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-10T19:21:55.711164Z digest=sha256:ad795b6820233ff8a2d181071009b79a22dbf72aa0e03de6f394bd345de44050

Observation 29d726b9-780f-4666-a959-e16fb09c089b · outbound

This paper cites External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients.

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:21:55.845848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-10T19:21:55.715745Z digest=sha256:05fb82935da70321a2ecac385d8e9b66adcff6b34fed7821f8cdd927bec5bd6a

Pith citing papers

No inbound Pith citation observations are available.