Artificial Neural Networks for Estimation of Dementias Types

Mantzaris, Dimitrios; Vrizas, Michael; Trougkakos, Spyridon; Priska, Evaggelia; Vadikolias, Κonstantinos

doi:10.15764/aia.2014.01006

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…Another well-known example (with no time dependence) is Zipf’s Law, which states that the rank frequency distribution of words in any sufficiently long text is distributed as a power law [ 81 ]. This law is independent of the language and holds even for artificial languages such as Esperanto [ 82 ]. While Zipf investigates specifically the distribution of words, a generalization of this law was later developed by Mandelbrot [ 83 ].…”

Section: Discussionmentioning

confidence: 99%

A master equation for power laws

Roman

Bertolotti²

2022

R. Soc. open sci.

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We propose a new mechanism for generating power laws. Starting from a random walk, we first outline a simple derivation of the Fokker–Planck equation. By analogy, starting from a certain Markov chain, we derive a master equation for power laws that describes how the number of cascades changes over time (cascades are consecutive transitions that end when the initial state is reached). The partial differential equation has a closed form solution which gives an explicit dependence of the number of cascades on their size and on time. Furthermore, the power law solution has a natural cut-off, a feature often seen in empirical data. This is due to the finite size a cascade can have in a finite time horizon. The derivation of the equation provides a justification for an exponent equal to 2, which agrees well with several empirical distributions, including Richardson’s Law on the size and frequency of deadly conflicts. Nevertheless, the equation can be solved for any exponent value. In addition, we propose an urn model where the number of consecutive ball extractions follows a power law. In all cases, the power law is manifest over the entire range of cascade sizes, as shown through log–log plots in the frequency and rank distributions.

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Section: Discussionmentioning

confidence: 99%

A master equation for power laws

Roman

Bertolotti²

2022

R. Soc. open sci.

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“…Multiple computational and mathematical methods have been proposed in the diagnosis of dementia [7,8], creating an interesting scientific field where a wide body of work exists. In general, the majority of developed research is centered on the detection of the existence or absence of a dementia, usually AD, or performs a differential diagnosis between two types of dementia, or a diagnosis between AD and MCI [9][10][11], or in longitudinal studies, the identification of conversion from MCI to AD [12,13].…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Clinical Decision Support System, for Mild Cognitive Impairment Detection, Based on a Hybrid Neural Architecture

Suárez-Araujo

Báez

Cabrera-León

et al. 2021

Computational and Mathematical Methods in Medicine

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Clinical procedure for mild cognitive impairment (MCI) is mainly based on clinical records and short cognitive tests. However, low suspicion and difficulties in understanding test cut-offs make diagnostic accuracy being low, particularly in primary care. Artificial neural networks (ANNs) are suitable to design computed aided diagnostic systems because of their features of generating relationships between variables and their learning capability. The main aim pursued in that work is to explore the ability of a hybrid ANN-based system in order to provide a tool to assist in the clinical decision-making that facilitates a reliable MCI estimate. The model is designed to work with variables usually available in primary care, including Minimental Status Examination (MMSE), Functional Assessment Questionnaire (FAQ), Geriatric Depression Scale (GDS), age, and years of education. It will be useful in any clinical setting. Other important goal of our study is to compare the diagnostic rendering of ANN-based system and clinical physicians. A sample of 128 MCI subjects and 203 controls was selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The ANN-based system found the optimal variable combination, being AUC, sensitivity, specificity, and clinical utility index (CUI) calculated. The ANN results were compared with those from medical experts which include two family physicians, a neurologist, and a geriatrician. The optimal ANN model reached an AUC of 95.2%, with a sensitivity of 90.0% and a specificity of 84.78% and was based on MMSE, FAQ, and age inputs. As a whole, physician performance achieved a sensitivity of 46.66% and a specificity of 91.3%. CUIs were also better for the ANN model. The proposed ANN system reaches excellent diagnostic accuracy although it is based only on common clinical tests. These results suggest that the system is especially suitable for primary care implementation, aiding physicians work with cognitive impairment suspicions.

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Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction

2021

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The diagnosis of diseases is decisive for planning proper treatment and ensuring the well-being of patients. Human error hinders accurate diagnostics, as interpreting medical information is a complex and cognitively challenging task. The application of artificial intelligence (AI) can improve the level of diagnostic accuracy and efficiency. While the current literature has examined various approaches to diagnosing various diseases, an overview of fields in which AI has been applied, including their performance aiming to identify emergent digitalized healthcare services, has not yet been adequately realized in extant research. By conducting a critical review, we portray the AI landscape in diagnostics and provide a snapshot to guide future research. This paper extends academia by proposing a research agenda. Practitioners understand the extent to which AI improves diagnostics and how healthcare benefits from it. However, several issues need to be addressed before successful application of AI in disease diagnostics can be achieved.

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Artificial Neural Networks for Estimation of Dementias Types

Cited by 7 publications

References 29 publications

A master equation for power laws

A master equation for power laws

A Real-Time Clinical Decision Support System, for Mild Cognitive Impairment Detection, Based on a Hybrid Neural Architecture

Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction

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