ArticleProceedings. IEEE International Congress on Big Data
Predictive Modeling for Comfortable Death Outcome Using Electronic Health Records.
Article in Proceedings. IEEE International Congress on Big Data. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 3 of them syntheses that pooled 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.
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.
Who cites it
7 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Secondary use of standardized nursing care data for advancing nursing science and practice: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2019Pooled it
- Improving palliative and end-of-life care with machine learning and routine data: a rapid review.HRB open research · 2019Pooled it
- Evidence of Progress in Making Nursing Practice Visible Using Standardized Nursing Data: a Systematic Review.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Pooled it
- Conducting a representative national randomized control trial of tailored clinical decision support for nurses remotely: Methods and implications.Contemporary clinical trials · 2022Trial
- Standardizing nursing data extracted from electronic health records for integration into a statewide clinical data research network.International journal of medical informatics · 2024Article
- Identification of Digital Health Priorities for Palliative Care Research: Modified Delphi Study.JMIR aging · 2022Article
- Use of machine learning to transform complex standardized nursing care plan data into meaningful research variables: a palliative care exemplar.Journal of the American Medical Informatics Association : JAMIA · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Electronic health record (EHR) systems are used in healthcare industry to observe the progress of patients. With fast growth of the data, EHR data analysis has become a big data problem. Most EHRs are sparse and multi-dimensional datasets and mining them is a challenging task due to a number of reasons. In this paper, we have used a nursing EHR system to build predictive models to determine what factors impact death anxiety, a significant problem for the dying patients. Different existing modeling techniques have been used to develop coarse-grained as well as fine-grained models to predict patient outcomes. The coarse-grained models help in predicting the outcome at the end of each hospitalization, whereas fine-grained models help in predicting the outcome at the end of each shift, therefore providing a trajectory of predicted outcomes. Based on different modeling techniques, our results show significantly accurate predictions, due to relatively noise-free data. These models can help in determining effective treatments, lowering healthcare costs, and improving the quality of end-of-life (EOL) care.
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What OpenQuestion holds
Registered trials
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.