Evidence map›Paper›PMID 27500287›Full record

ArticleAdvances in data mining. Industrial Conference on Data Mining2015

Predictive Modeling for End-of-Life Pain Outcome using Electronic Health Records.

Muhammad K Lodhi, Janet Stifter, Yingwei Yao, Rashid Ansari, Gail M Kee-Nan, Diana J Wilkie, Ashfaq A Khokhar

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advances in data mining. Industrial Conference on Data Mining, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Moving towards the use of artificial intelligence in pain management.European journal of pain (London, England) · 2025
    Pooled it
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  12. HPNA 2019-2022 Research Agenda: Development and Rationale.Journal of hospice and palliative nursing : JHPN : the official journal of the Hospice and Palliative Nurses Association · 2019
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Muhammad K LodhiUniversity of Illinois at Chicago, Chicago, IL, US.
Janet StifterUniversity of Illinois at Chicago, Chicago, IL, US.
Yingwei YaoUniversity of Illinois at Chicago, Chicago, IL, US.
Rashid AnsariUniversity of Illinois at Chicago, Chicago, IL, US.
Gail M Kee-NanUniversity of Florida, Gainesville, FL, US.
Diana J WilkieUniversity of Florida, Gainesville, FL, US.
Ashfaq A KhokharIllinois Institute of Technology, Chicago, IL, US.

Funding

Describing, Contrasting, and Visualizing End-of-Life Care in the 21st CenturyR01NR012949 · NINR · UNIVERSITY OF ILLINOIS AT CHICAGO · PI ANSARI, RASHID, KEENAN, GAIL M · 2011 to 2014
$2.0M
NINR NIH HHS R01 NR012949
6 · The paper itself

Abstract

Electronic health record (EHR) systems are being widely used in the healthcare industry nowadays, mostly for monitoring the progress of the patients. EHR data analysis has become a big data problem as data is growing rapidly. Using a nursing EHR system, we built predictive models for determining what factors influence pain in end-of-life (EOL) patients. Utilizing different modeling techniques, we developed coarse-grained and fine-grained models to predict patient pain outcomes. The coarse-grained models help predict the outcome at the end of each hospitalization, whereas fine-grained models help predict the outcome at the end of each shift, thus providing a trajectory of predicted outcomes over the entire hospitalization. These models can help in determining effective treatments for individuals and groups of patients and support standardization of care where appropriate. Using these models may also lower the cost and increase the quality of end-of-life care. Results from these techniques show significantly accurate predictions.

Indexed as

data miningelectronic health records (EHR)end-of-life (EOL)predictive modeling

Identifiers

PMID27500287
PMCPMC4975539

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.