Evidence map›Paper›PMID 36567754›Full record

ArticleFrontiers in big data2022

Worldwide impact of lifestyle predictors of dementia prevalence: An eXplainable Artificial Intelligence analysis.

Loredana Bellantuono, Alfonso Monaco, Nicola Amoroso, Antonio Lacalamita, Ester Pantaleo, Sabina Tangaro, Roberto Bellotti

Open access · goldAbstract read
In one paragraph

Article in Frontiers in big data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.9field-weighted citation impact, top 15% 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

11 citing papers in PubMed, 19 citations in OpenAlex.

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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

7 authors at 2 institutions in 1 country.

Loredana BellantuonoDipartimento di Biomedicina Traslazionale e Neuroscienze (DiBraiN), Università degli Studi di Bari Aldo Moro, Bari, Italy.
Alfonso MonacoIstituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy.
Nicola AmorosoIstituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy.
Antonio LacalamitaDipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy.
Ester PantaleoIstituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy.
Sabina TangaroIstituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy.
Roberto BellottiIstituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy.
University of Bari Aldo Moro · ITIstituto Nazionale di Fisica Nucleare, Sezione di Bari · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Dementia is an umbrella term indicating a group of diseases that affect the cognitive sphere. Dementia is not a mere individual health issue, since its interference with the ability to carry out daily activities entails a series of collateral problems, comprising exclusion of patients from civil rights and welfare, unpaid caregiving work, mostly performed by women, and an additional burden on the public healthcare systems. Thus, gender and wealth inequalities (both among individuals and among countries) tend to amplify the social impact of such a disease. Since at present there is no cure for dementia but only drug treatments to slow down its progress and mitigate the symptoms, it is essential to work on prevention and early diagnosis, identifying the risk factors that increase the probability of its onset. The complex and multifactorial etiology of dementia, resulting from an interplay between genetics and environmental factors, can benefit from a multidisciplinary approach that follows the "One Health" guidelines of the World Health Organization. Methods: In this work, we apply methods of Artificial Intelligence and complex systems physics to investigate the possibility to predict dementia prevalence throughout world countries from a set of variables concerning individual health, food consumption, substance use and abuse, healthcare system efficiency. The analysis uses publicly available indicator values at a country level, referred to a time window of 26 years. Results: Employing methods based on eXplainable Artificial Intelligence (XAI) and complex networks, we identify a group of lifestyle factors, mostly concerning nutrition, that contribute the most to dementia incidence prediction. Discussion: The proposed approach provides a methodological basis to develop quantitative tools for action patterns against such a disease, which involves issues deeply related with sustainable, such as good health and resposible food consumption.

Indexed as

AI for social goodcomplex systemscomputational social sciencedata science for social gooddementiaeXplainable Artificial IntelligenceOne Healthsustainable development goals

Identifiers

PMID36567754
PMCPMC9772995
OpenAlexW4311858041

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.