Machine learning, artificial intelligence and the prediction of dementia

Merkin, Alexander; Krishnamurthi, Rita; Medvedev, Oleg N.

doi:10.1097/yco.0000000000000768

Cited by 32 publications

(18 citation statements)

References 46 publications

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“…b) Implicit Detection of Neurological Disorders: Patterns of impairment in natural language production (e.g., writing samples, spoken language) have proven sensitive to detecting a wide range of neurological and psychiatric disorders (Garrard et al, 2005). Automated screening using implicit language sampling is emerging a powerful tool for early detection of prodromal dementia (Merkin et al, 2022;Song et al, 2011;Spooner et al, 2020). We know of no current algorithm that considers abnormal semantic distance as a predictor of cognitive impairment.…”

Section: Potential Applications and Future Directionsmentioning

confidence: 99%

What is Semantic Distance? A Review and Proposed Method for Modeling Conceptual Transitions in Natural Language

Reilly¹,

Zuckerman

Finley

et al. 2022

Preprint

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Cognitive science has seen a rapid evolution of the tools available for studying conceptual knowledge. Historically, much of our understanding of semantic memory has been informed through studies using language. Natural language processing (NLP) has offered groundbreaking techniques for elucidating relationships between concepts and language at unprecedented scales. One of the most popular applications has involved mathematically representing concepts using high dimensional feature spaces. Here we describe the nature of such semantic spaces and review ways in which human- and machine-generated semantic distance metrics differ in capturing taxonomic (e.g., dog-wolf) versus thematic (e.g., dog-leash) semantic relationships. We propose a novel method and open-source algorithm for deriving semantic distances between adjacent content words in connected language samples. This R package transforms a user-specified language transcript into a vector of pairwise semantic distances spanning all adjacent bigrams (e.g., The cat drank the milk → cat-drink, drink-milk, etc.). These distances constitute a continuous time series reflecting word-by-word level changes in meaning across a language sample of any length. We derive semantic distance norms and apply the proposed technique to a classic work of short fiction, To Build a Fire (Jack London, 1908). We discuss extensions of this time series approach, including the potential for forecasting, causal modeling, topic cohesion, and as an implicit measure of semantic impairment in spoken and/or written narratives.

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Section: Potential Applications and Future Directionsmentioning

confidence: 99%

What is Semantic Distance? A Review and Proposed Method for Modeling Conceptual Transitions in Natural Language

Reilly¹,

Zuckerman

Finley

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…According to the literature, a ML device has been used to identify, forecast, and predict certain diseases and calamities, including SARS-CoV-2 (Chamola et al 2021 ; Caballé-Cervigón et al 2020 ; Merkin et al 2022 ; Rahimi et al 2021 ). To appropriately assess the transmission of the pandemic, Harrow and coauthors built a presumption data-driven approach, as shown in Figure S2 .…”

Section: Significant Applications Of ML For the Covid-19 Pandemicmentioning

confidence: 99%

A review about COVID-19 in the MENA region: environmental concerns and machine learning applications

Meskher

Belhaouari

Thakur

et al. 2022

Environ Sci Pollut Res

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Coronavirus disease 2019 (COVID-19) has delayed global economic growth, which has affected the economic life globally. On the one hand, numerous elements in the environment impact the transmission of this new coronavirus. Every country in the Middle East and North Africa (MENA) area has a different population density, air quality and contaminants, and water- and land-related conditions, all of which influence coronavirus transmission. The World Health Organization (WHO) has advocated fast evaluations to guide policymakers with timely evidence to respond to the situation. This review makes four unique contributions. One, many data about the transmission of the new coronavirus in various sorts of settings to provide clear answers to the current dispute over the virus’s transmission were reviewed. Two, highlight the most significant application of machine learning to forecast and diagnose severe acute respiratory syndrome coronavirus (SARS-CoV-2). Three, our insights provide timely and accurate information along with compelling suggestions and methodical directions for investigators. Four, the present study provides decision-makers and community leaders with information on the effectiveness of environmental controls for COVID-19 dissemination. Graphical Abstract Supplementary Information The online version contains supplementary material available at 10.1007/s11356-022-23392-z.

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“…Patients with dementia will benefit from high-quality care when these diverse and strategic resources are utilized effectively. Therefore, ML is a crucial component in achieving this goal, and there is evidence that ML knowledge from clinical data can be used to plan care for people at risk of different dementia forms [ 27 , 28 , 29 , 30 , 31 ]. Review articles on the use of AI in the brain sciences analyze the opportunities and challenges associated with its implementation [ 32 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review

et al. 2022

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Background: The progressive aging of populations, primarily in the industrialized western world, is accompanied by the increased incidence of several non-transmittable diseases, including neurodegenerative diseases and adult-onset dementia disorders. To stimulate adequate interventions, including treatment and preventive measures, an early, accurate diagnosis is necessary. Conventional magnetic resonance imaging (MRI) represents a technique quite common for the diagnosis of neurological disorders. Increasing evidence indicates that the association of artificial intelligence (AI) approaches with MRI is particularly useful for improving the diagnostic accuracy of different dementia types. Objectives: In this work, we have systematically reviewed the characteristics of AI algorithms in the early detection of adult-onset dementia disorders, and also discussed its performance metrics. Methods: A document search was conducted with three databases, namely PubMed (Medline), Web of Science, and Scopus. The search was limited to the articles published after 2006 and in English only. The screening of the articles was performed using quality criteria based on the Newcastle–Ottawa Scale (NOS) rating. Only papers with an NOS score 7 were considered for further review. Results: The document search produced a count of 1876 articles and, because of duplication, 1195 papers were not considered. Multiple screenings were performed to assess quality criteria, which yielded 29 studies. All the selected articles were further grouped based on different attributes, including study type, type of AI model used in the identification of dementia, performance metrics, and data type. Conclusions: The most common adult-onset dementia disorders occurring were Alzheimer’s disease and vascular dementia. AI techniques associated with MRI resulted in increased diagnostic accuracy ranging from 73.3% to 99%. These findings suggest that AI should be associated with conventional MRI techniques to obtain a precise and early diagnosis of dementia disorders occurring in old age.

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Machine learning, artificial intelligence and the prediction of dementia

Cited by 32 publications

References 46 publications

What is Semantic Distance? A Review and Proposed Method for Modeling Conceptual Transitions in Natural Language

What is Semantic Distance? A Review and Proposed Method for Modeling Conceptual Transitions in Natural Language

A review about COVID-19 in the MENA region: environmental concerns and machine learning applications

Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review

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