Software intelligent system for effective solutions for hearing impaired subjects

Rajkumar, S.; Muttan, S.; Sapthagirivasan, V.; Jaya, V.; Vignesh, S. S.

doi:10.1016/j.ijmedinf.2016.10.009

Cited by 7 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…NN deserve a special consideration, since they are the key technique adopted in many of the retrieved papers 15 , 17 – 19 , 28 – 30 , 35 , 40 , 41 , 52 , 58 . Indeed, image interpretation and classification are fields where NN/deep learning approaches work well 59 .…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey

Montani

Striani

2019

Yearb Med Inform

View full text Add to dashboard Cite

Objectives: This survey analyses the latest literature contributions to clinical decision support systems (DSSs) on a two-year period (2017-2018), focusing on the approaches that adopt Artificial Intelligence (AI) techniques in a broad sense. The goal is to analyse the distribution of data-driven AI approaches with respect to “classical" knowledge-based ones, and to consider the issues raised and their possible solutions. Methods: We included PubMed and Web of ScienceTM publications, focusing on contributions describing clinical DSSs that adopted one or more AI methodologies. Results: We selected 75 papers, 49 of which describe approaches in the data-driven AI area, 20 present purely knowledge-based DSSs, and 6 adopt hybrid approaches relying on both formalized knowledge and data. Conclusions: Recent studies in the clinical DSS area demonstrate a prevalence of data-driven AI, which can be adopted autonomously in purely data-driven systems, or in cooperation with domain knowledge in hybrid systems. Such hybrid approaches, able to conjugate all available knowledge sources through proper knowledge integration steps, represent an interesting example of synergy between the two AI categories. This synergy can lead to the resolution of some existing issues, such as the need for transparency and explainability, nowadays recognized as central themes to be addressed by both AI and medical informatics research.

show abstract

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey

Montani

Striani

2019

Yearb Med Inform

View full text Add to dashboard Cite

show abstract

“…In the visual disability area, various studies have investigated visual impairment-for example, identifying the behavioral dynamics of visually impaired infants (26). In the hearing disability area, one study designed and developed an intelligent decision support system that performed audiological investigations to assess degree of hearing loss and suggest appropriate hearing-aid gain value (27). In the realm of cognitive disability, there is research related to the identi cation and evaluation of cognitive de cits in schizophrenia (28).…”

Section: Discussionmentioning

confidence: 99%

AIGDR: An Artificial Intelligence Framework for Disability Research – A Multiple Sclerosis Case

Al-Eid

Alfantoukh

Alarfaj³

et al. 2022

Preprint

View full text Add to dashboard Cite

Background: Artificial intelligence (AI) is becoming an essential tool in the disability world. AI can be applied in a variety of contexts and domains, ranging from assistive devices for disabled individuals to decision-making systems that aid healthcare providers in the diagnosis and treatment of disabilities. Examples of these contexts include discrimination of behaviors, identifying and assessing disability-related problems, detecting abnormalities, classifying disabling diseases, predicting the progression of disabilities, and assisting and triaging patients.Methods: We investigated which AI predictive analytics models and features have been used in disability research locally and globally, focusing on multiple sclerosis. This allowed us to test multiple algorithms to determine which was best suited for each kind of data available locally. Results: We developed a framework called Artificial Intelligence Group for Disability Research (AIGDR) for disability-related research at King Salman Centre for Disability Research. The framework was influenced by existing AI use in disability research. Conclusions: The AIGDR framework uses existing predictive analytics models to generate dashboards and reports for decision-makers and produce a database that can help researchers conduct research. In the future, we plan to develop the proposed platform and apply it in various disability contexts in Saudi Arabia.

show abstract

Care2Vec: a hybrid autoencoder-based approach for the classification of self-care problems in physically disabled children

Putatunda

2020

Neural Comput & Applic

View full text Add to dashboard Cite

Software intelligent system for effective solutions for hearing impaired subjects

Cited by 7 publications

References 23 publications

Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey

Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey

AIGDR: An Artificial Intelligence Framework for Disability Research – A Multiple Sclerosis Case

Care2Vec: a hybrid autoencoder-based approach for the classification of self-care problems in physically disabled children

Contact Info

Product

Resources

About