Evidence map›Paper›PMID 35582239›Full record

ArticleBiomedical signal processing and control2022

LHSPred: A web based application for predicting lung health severity.

Sudipto Bhattacharjee, Banani Saha, Parthasarathi Bhattacharyya, Sudipto Saha

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biomedical signal processing and control, 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
0.7field-weighted citation impact, top 32% of its field
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, 5 citations in OpenAlex.

  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

4 authors at 2 institutions in 1 country.

Sudipto BhattacharjeeDepartment of Computer Science and Engineering, University of Calcutta, JD-2, Sector-III, Salt Lake, Kolkata 700098, India.
Banani SahaDepartment of Computer Science and Engineering, University of Calcutta, JD-2, Sector-III, Salt Lake, Kolkata 700098, India.
Parthasarathi BhattacharyyaInstitute of Pulmocare and Research, DG-8, Action Area-1, New Town, Kolkata 700156, India.
Sudipto SahaDivision of Bioinformatics, Bose Institute, P-1/12 C.I.T. Scheme VII-M, Kolkata 700054, India.
University of Calcutta · INBose Institute · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and objectives: The computed tomography (CT) scan facilities are crucial for diagnosis of pulmonary diseases and are overburdened during the current pandemic of novel coronavirus disease 2019 (COVID-19). Methods: This tool uses Support Vector Regression (SVR) and Multi-Layer Perceptron Regression (MLPR), trained on COVID-19 patient data reported in the literature. It allows to compute a score (CT severity score) that evaluates the involvement of lesions in lung lobes and to predict risk of pneumonia. A web application was implemented that uses the trained regression models. Results: The application has proven to be effective and user friendly in a clinical setting for pulmonary disease treatment. The SVR model achieved Pearson correlation coefficient (PCC) of Conclusion: LHSPred can be used as a decision support system by the clinicians and as a tool for self-assessment by the patients with only six blood test input features.

Indexed as

COVID-19HRCT scan imageLung healthMulti-layer Perceptron RegressionPneumoniaSupport Vector Regression

Identifiers

PMID35582239
PMCPMC9098195
OpenAlexW4280488508

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.