Paper Citation Record · LEDGER

A Comparative Study of Feature Selection Methods for EHR Diagnosis Codes in Opioid Use Disorder Prediction

As of 21 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2608.04180.

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

pith.paper-citation-record.v1
2608.04180 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T00:26:04.871353Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

25 of 25 outbound references displayed

verified exact0
verified fuzzy23
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d1d7bf7d-bd5e-4b33-844d-f6604984a033 · outbound

This paper cites Key Substance Use and Mental Health Indicators in the United States: Results from the 2019 National Survey on Drug Use and Health.

A Comparative Study of Feature Selection Methods for EHR Diagnosis Codes in Opioid Use Disorder Prediction Key Substance Use and Mental Health Indicators in the United States: Results from the 2019 National Survey on Drug Use and Health

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:26:05.102617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cc83f0f2-b2df-4532-8eee-91e97d5a3306 · outbound

This paper cites Training: Assessing and Addressing Opioid Use Disorder; 2025.

A Comparative Study of Feature Selection Methods for EHR Diagnosis Codes in Opioid Use Disorder Prediction Training: Assessing and Addressing Opioid Use Disorder; 2025

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:26:05.093319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9e0e4a58-90a1-4c09-93e9-2be75c1942f2 · outbound

This paper cites Representation of EHR data for predictive modeling: a comparison between UMLS and other terminologies.

A Comparative Study of Feature Selection Methods for EHR Diagnosis Codes in Opioid Use Disorder Prediction Representation of EHR data for predictive modeling: a comparison between UMLS and other terminologies

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:26:05.084503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 776e1d73-44da-4364-ba5b-9ebf321f726e · outbound

This paper cites Machine learning based opioid overdose prediction using electronic health records.

A Comparative Study of Feature Selection Methods for EHR Diagnosis Codes in Opioid Use Disorder Prediction Machine learning based opioid overdose prediction using electronic health records

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:26:05.075920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 63f4872e-3201-4f2e-952f-63b50e8eeaf2 · outbound

This paper cites Predicting opioid overdose risk of patients with opioid prescriptions using electronic health records based on temporal deep learning.

A Comparative Study of Feature Selection Methods for EHR Diagnosis Codes in Opioid Use Disorder Prediction Predicting opioid overdose risk of patients with opioid prescriptions using electronic health records based on temporal deep learning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:26:05.067086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 96c25cf9-2f9a-41e8-a5ab-04cde3aa6199 · outbound

This paper cites An integrated LSTM-HeteroRGNN model for interpretable opioid overdose risk prediction.

A Comparative Study of Feature Selection Methods for EHR Diagnosis Codes in Opioid Use Disorder Prediction An integrated LSTM-HeteroRGNN model for interpretable opioid overdose risk prediction

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:26:05.059008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 37243a52-ec09-4c1e-b947-a8c67e79c122 · outbound

This paper cites HIBERT: A Hybrid Clustering BERT for Interpretable Opioid Overdose Risk Prediction.

A Comparative Study of Feature Selection Methods for EHR Diagnosis Codes in Opioid Use Disorder Prediction HIBERT: A Hybrid Clustering BERT for Interpretable Opioid Overdose Risk Prediction

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:26:05.050741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d99f1acf-2768-44ad-b820-4fbb1139bd1f · outbound

This paper cites Big data and machine learning in health care.

A Comparative Study of Feature Selection Methods for EHR Diagnosis Codes in Opioid Use Disorder Prediction Big data and machine learning in health care

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:26:05.042904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e1048e77-5ea8-46fa-8bc1-242091eb6490 · outbound

This paper cites Scalable and accurate deep learning with electronic health records.

A Comparative Study of Feature Selection Methods for EHR Diagnosis Codes in Opioid Use Disorder Prediction Scalable and accurate deep learning with electronic health records

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:26:05.035157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 15e728e2-e3c1-4824-b549-a6c44bb35500 · outbound

This paper cites Doctor ai: Predicting clinical events via recurrent neural networks.

A Comparative Study of Feature Selection Methods for EHR Diagnosis Codes in Opioid Use Disorder Prediction Doctor ai: Predicting clinical events via recurrent neural networks

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:26:05.027145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e33e3c8b-fa73-4151-8a38-d2d934ff4c65 · outbound

This paper cites An introduction to variable and feature selection.

A Comparative Study of Feature Selection Methods for EHR Diagnosis Codes in Opioid Use Disorder Prediction An introduction to variable and feature selection

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:26:05.018783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 677faad7-065d-47d6-b46c-04382b1a1784 · outbound

This paper cites Regression shrinkage and selection via the lasso.

A Comparative Study of Feature Selection Methods for EHR Diagnosis Codes in Opioid Use Disorder Prediction Regression shrinkage and selection via the lasso

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:26:05.010230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f553c7eb-c1ea-4be0-8efa-1a39476e6628 · outbound

This paper cites Deep patient: an unsupervised representation to predict the future of patients from the electronic health records.

A Comparative Study of Feature Selection Methods for EHR Diagnosis Codes in Opioid Use Disorder Prediction Deep patient: an unsupervised representation to predict the future of patients from the electronic health records

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:26:05.001368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 78cadd55-1a40-4823-8e58-beae91760d90 · outbound

This paper cites On the stability of feature selection algorithms.

A Comparative Study of Feature Selection Methods for EHR Diagnosis Codes in Opioid Use Disorder Prediction On the stability of feature selection algorithms

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:26:04.992174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 27d844d1-57b4-41d2-8564-bbd24a82a28c · outbound

This paper cites Clinical knowledge extraction via sparse em- bedding regression (KESER) with multi-center large scale electronic health record data.

A Comparative Study of Feature Selection Methods for EHR Diagnosis Codes in Opioid Use Disorder Prediction Clinical knowledge extraction via sparse em- bedding regression (KESER) with multi-center large scale electronic health record data

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:26:04.983375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5e1ea263-c166-47ad-a386-490e168392f1 · outbound

This paper cites Unsupervised feature selection to identify important ICD-10 and ATC codes for machine learning on a cohort of patients with coronary heart disease: retrospective study.

A Comparative Study of Feature Selection Methods for EHR Diagnosis Codes in Opioid Use Disorder Prediction Unsupervised feature selection to identify important ICD-10 and ATC codes for machine learning on a cohort of patients with coronary heart disease: retrospective study

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:26:04.974407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 72aeae23-f490-4727-9c06-44ebf030da13 · outbound

This paper cites Large language models facilitate the generation of electronic health record phenotyping algorithms.

A Comparative Study of Feature Selection Methods for EHR Diagnosis Codes in Opioid Use Disorder Prediction Large language models facilitate the generation of electronic health record phenotyping algorithms

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:26:04.965516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 822bf8c3-152b-4758-82e8-b7754172c3b3 · outbound

This paper cites Llm-select: Feature selection with large language models.

A Comparative Study of Feature Selection Methods for EHR Diagnosis Codes in Opioid Use Disorder Prediction Llm-select: Feature selection with large language models

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:26:04.956242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a82a36e3-7fba-4729-abd3-181105cdf15e · outbound

This paper cites A unified approach to interpreting model predictions.

A Comparative Study of Feature Selection Methods for EHR Diagnosis Codes in Opioid Use Disorder Prediction A unified approach to interpreting model predictions

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-08T00:26:04.855274Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf70498e-34ce-45f8-891a-0b8df9d19baf · outbound

This paper cites Cerner Health Facts ® Data Sets - SBMI Data Service; 2018.

A Comparative Study of Feature Selection Methods for EHR Diagnosis Codes in Opioid Use Disorder Prediction Cerner Health Facts ® Data Sets - SBMI Data Service; 2018

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:26:04.941906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4c0e8e38-88ab-46ac-9a29-de30b1c89924 · outbound

This paper cites A comparison of a multistate inpatient EHR database to the HCUP Nationwide Inpatient Sample.

A Comparative Study of Feature Selection Methods for EHR Diagnosis Codes in Opioid Use Disorder Prediction A comparison of a multistate inpatient EHR database to the HCUP Nationwide Inpatient Sample

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:26:04.933044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4542f84c-057e-4f3e-8eb3-f49fbd27410b · outbound

This paper cites DrugBank 5.0: a major update to the DrugBank database for 2018.

A Comparative Study of Feature Selection Methods for EHR Diagnosis Codes in Opioid Use Disorder Prediction DrugBank 5.0: a major update to the DrugBank database for 2018

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-08T00:26:04.863452Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01ef6f2b-9841-42b5-b5af-f344ae199e19 · outbound

This paper cites Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis.

A Comparative Study of Feature Selection Methods for EHR Diagnosis Codes in Opioid Use Disorder Prediction Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:26:04.924245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 55bc89be-1f4f-4834-a181-0fefd1cd5f34 · outbound

This paper cites Case Study: Exploring How Opioid-Related Diagnosis Codes Translate From ICD-9- CM to ICD-10-CM.

A Comparative Study of Feature Selection Methods for EHR Diagnosis Codes in Opioid Use Disorder Prediction Case Study: Exploring How Opioid-Related Diagnosis Codes Translate From ICD-9- CM to ICD-10-CM

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:26:04.915155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 624de1b9-7401-442b-94ae-daa62115f58d · outbound

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

A Comparative Study of Feature Selection Methods for EHR Diagnosis Codes in Opioid Use Disorder Prediction Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:26:04.905963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

No inbound Pith citation observations are available.