Evidence map›Paper›PMID 35713870›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2022

Extracting Significant Comorbid Diseases from MeSH Index of PubMed.

Dheepa Anand, Sharanya Manoharan, Oviya Ramalakshmi Iyyappan, Sadhanha Anand, Kalpana Raja

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

5 authors.

Dheepa AnandDepartment of Pharmacology, Cheran College of Pharmacy, Coimbatore, Tamilnadu, India.
Sharanya Manoharan *Department of Bioinformatics, Stella Maris College (Autonomous), Chennai, Tamilnadu, India.
Oviya Ramalakshmi Iyyappan *Department of Sciences, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Chennai, Tamilnadu, India.
Sadhanha AnandDepartment of Biomedical Engineering, PSG College of Technology, Coimbatore, Tamilnadu, India.
Kalpana RajaRegenerative Biology, The Morgridge Institute for Research, Madison, WI, USA. kalpana.rajaa@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Text mining is an important research area to be explored in terms of understanding disease associations and have an insight in disease comorbidities. The reason for comorbid occurrence in any patient may be genetic or molecular interference from any other processes. Comorbidity and multimorbidity may be technically different, yet still are inseparable in studies. They have overlapping nature of associations and hence can be integrated for a more rational approach. The association rule generally used to determine comorbidity may also be helpful in novel knowledge prediction or may even serve as an important tool of assessment in surgical cases. Another approach of interest may be to utilize biological vocabulary resources like UMLS/MeSH across a patient health information and analyze the interrelationship between different health conditions. The protocol presented here can be utilized for understanding the disease associations and analyze at an extensive level.

Indexed as

Abstracting and IndexingMedical Subject HeadingsData MiningHumansNatural Language ProcessingPubMedDisease comorbidityInformation extractionInformation retrievalNatural language processingText mining

Identifiers

PMID35713870

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.