Evidence map›Paper›PMID 25678995›Full record

ArticleMedical journal of the Islamic Republic of Iran2014

A diagnostic model for cirrhosis in patients with non-alcoholic fatty liver disease: an artificial neural network approach.

Omid Pournik, Sara Dorri, Hedieh Zabolinezhad, Seyyed Moayed Alavian, Saeid Eslami

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

Article in Medical journal of the Islamic Republic of Iran, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 2 pooled it
0.4field-weighted citation impact, top 35% 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

8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 15 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. 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 at 3 institutions in 1 country.

Omid Pournik1. MD, MPH, PhD, Department of Community Medicine, School of Medicine, Iran University of Medical Sciences, Tehran, Iran. pournik.o@iums.ac.ir.
Sara Dorri2. Msc, Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran. dorris901@mums.ac.ir.
Hedieh Zabolinezhad3. Msc, Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran. zabolinh@gmail.com.
Seyyed Moayed Alavian4. Professor, Middle East Liver Diseases Center (MELD), Tehran, Iran. Alavian@thc.ir.
Saeid Eslami5. PharmD, PhD, Pharmaceutical Research Center, School of Pharmacy, Mashhad, Iran, Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran. eslamis@mums.ac.ir.
Mashhad University of Medical Sciences · IRIran University of Medical Sciences · IRMiddle East Liver Disease Center · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTimely diagnosis of liver cirrhosis is vital for preventing further liver damage and giving the patient the chance of transplantation. Although biopsy of the liver is the gold standard for cirrhosis assessment, it has some risks and limitations and this has led to the development of new noninvasive methods to determine the stage and prognosis of the patients. We aimed to design an artificial neural network (ANN) model to diagnose cirrhosis patients with non-alcoholic fatty liver disease (NAFLD) using routine laboratory data.

methodsData were collected from 392 patients with NAFLD by the Middle East Research Center in Tehran. Demographic variables, history of diabetes, INR, complete blood count, albumin, ALT, AST and other routine laboratory tests, examinations and medical history were gathered. Relevant variables were selected by means of feature extraction algorithm (Knime software) and were accredited by the experts. A neural network was developed using the MATLAB software.

resultsThe best obtained model was developed with two layers, eight neurons and TANSIG and PURLIN functions for layer one and output layer, respectively. The sensitivity and specificity of the model were 86.6% and 92.7%, respectively.

conclusionThe results of this study revealed that the neural network modeling may be able to provide a simple, noninvasive and accurate method for diagnosing cirrhosis only based on routine laboratory data.

Indexed as

DiagnosisLiver cirrhosisNeural NetworksNon-Alcoholic Fatty Liver Disease (NAFLD)

Identifiers

PMID25678995
PMCPMC4313459
OpenAlexW103629673

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.