Evidence map›Paper›PMID 35790097›Full record

ArticleEC paediatrics : open access2022

Using Temporal Data Mining on Patient Data for Clinical Decision Making in the Care of the Sick Newborn.

Sidhartha Tan, K P Unnikrishnan

Abstract read
In one paragraph

Article in EC paediatrics : open access, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Sidhartha TanDepartment of Pediatrics, Wayne State University School of Medicine, Detroit, MI, USA.
K P UnnikrishnaneNeuroLearn, Ann Arbor, MI USA.

Funding

Probing Role of Tetrahydrobiopterin in Cerebral Palsy by Using Transgenic RabbitsR01NS117146 · NINDS · WAYNE STATE UNIVERSITY · PI TAN, SIDHARTHA, VASQUEZ VIVAR, JEANNETTE M. · 2021 to 2025
$3.1M
Neuroprotection by nNOS inhibitors in perinatal hypoxia-ischemiaR01NS114972 · NINDS · WAYNE STATE UNIVERSITY · PI TAN, SIDHARTHA · 2020 to 2024
$3.1M
NINDS NIH HHS R01 NS114972NINDS NIH HHS R01 NS117146
6 · The paper itself

Abstract

Background: In a neonatal intensive care unit, streaming healthcare data comes from many sources, but humans are unable to understand relationships between data variables. Data mining and analysis are just beginning to get utilized in critical care. We present a case study using electronic medical record data in the neonatal intensive care unit and explore possible avenues of advancement using temporal data analytics. Case Presentation: Electronic medical record data were collected for physiological monitor data. Heart rate, respiratory rate, oxygen saturation and temperature data were retrospectively analyzed by temporal data mining. Three premature babies were selected and data de-identified. The first case of a urinary tract infection showed nursing ability to synthesize data streams coming from a patient. For the second case of necrotizing enterocolitis, Temporal-Data-Mining analysis of combinations of clinical events based on deviations from the mean showed specific heuristic biomarkers related to events before discovery of necrotizing enterocolitis. Specific sequences 6-event and 5-event in length were identified with nursing unease at clinical deterioration, which were 100- and 87-times unlikely to occur randomly with 99.5% confidence. No such sequences were found in the rest of the 37 days for the second case and entire 133 days of stay in the third case of an uneventful premature baby. Conclusion: Temporal data mining is a possible clinical tool in providing useful information in the neonatal intensive care unit for diagnosis of adverse clinical occurrences such as necrotizing enterocolitis. There is the possibility of changing the clinical paradigm of episodic watchfulness to constant vigilance using real-time data gathering.

Indexed as

Computer Data ProcessingCritical CareData MiningNursing Informatics

Identifiers

PMID35790097
PMCPMC9249406

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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.