Evidence map›Paper›PMID 36911275›Full record

ArticleArXiv2023

Artificial Intelligence for Dementia Research Methods Optimization.

Magda Bucholc, Charlotte James, Ahmad Al Khleifat, AmanPreet Badhwar, Natasha Clarke, Amir Dehsarvi, Christopher R Madan, Sarah J Marzi, Cameron Shand, Brian M Schilder and 5 more

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PubMed Full text at PMC
In one paragraph

Article in ArXiv, 2023. 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

5 · Who and what money

Authors and funding

15 authors.

Magda BucholcCognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, UK.
Charlotte JamesNIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol, Bristol, UK.
Ahmad Al KhleifatDepartment of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.
AmanPreet BadhwarMultiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Canada.
Natasha ClarkeMultiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Canada.
Amir DehsarviAberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences, and Nutrition, University of Aberdeen, Aberdeen, UK.
Christopher R MadanSchool of Psychology, University of Nottingham, Nottingham, UK.
Sarah J MarziUK Dementia Research Institute, Imperial College London, London, UK.
Cameron ShandCentre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
Brian M SchilderUK Dementia Research Institute, Imperial College London, London, UK.
Stefano TamburinDepartment of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy.
Hanz M TantiangcoInformation School, University of Sheffield, Sheffield, UK.
Ilianna LouridaUniversity of Exeter Medical School, Exeter, UK.
David J LlewellynUniversity of Exeter Medical School, Exeter, UK.
Janice M RansonUniversity of Exeter Medical School, Exeter, UK.

Funding

Identifying modifiable aspects of gene-by-environment interplay in later-life cognitive declineRF1AG055654 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI FAUL, JESSICA, GALAMA, TITUS JOHANNES · 2017 to 2018
$4.3M
NIA NIH HHS RF1 AG055654
6 · The paper itself

Abstract

introductionMachine learning (ML) has been extremely successful in identifying key features from high-dimensional datasets and executing complicated tasks with human expert levels of accuracy or greater.

methodsWe summarize and critically evaluate current applications of ML in dementia research and highlight directions for future research.

resultsWe present an overview of ML algorithms most frequently used in dementia research and highlight future opportunities for the use of ML in clinical practice, experimental medicine, and clinical trials. We discuss issues of reproducibility, replicability and interpretability and how these impact the clinical applicability of dementia research. Finally, we give examples of how state-of-the-art methods, such as transfer learning, multi-task learning, and reinforcement learning, may be applied to overcome these issues and aid the translation of research to clinical practice in the future. DISCUSSION: ML-based models hold great promise to advance our understanding of the underlying causes and pathological mechanisms of dementia.

Identifiers

PMID36911275
PMCPMC10002770

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read14
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.