SPELTA-Miner: An expert system based on data mining and multilabel classification to design therapy plans for communication disorders

Robles-Bykbaev, Vladimir; Quisi-Peralta, Diego; López‐Nores, Martín; Gil‐Solla, Alberto; García‐Duque, Jorge

doi:10.1109/codit.2016.7593574

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…As rhythmicity is a key component of well-being in bipolar disorder, it has been assessed using smartphones for automatic detection [7]. Dynamic psychological processes (bipolar disorder) are most often assessed using self-report instruments [8,9]. Their limitations have been assessed through the use of smartphone sensors, in which clinically validated treatments have been previously incorporated, as an acquisition system [8].…”

Section: Bipolar Disorder (Bd)mentioning

confidence: 99%

A review about Technology in mental health sensing and assessment

et al. 2022

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Information and communication technologies (ICT) such as smart devices, the Internet of Things and wireless sensor networks are gradually being introduced into the health system for early diagnosis and management of certain diseases. The state of the art of the use of these technologies in mental health identified 37 articles published in indexed high impact journals in the period 2003-2021. The snowball sampling method was used to select these papers. From this literature review, it appears that several of these technologies are used to support the early detection of mental disorders. Various systems based on wearable sensor networks, the Internet of Things and pervasive and ubiquitous computing have been designed and implemented in this sense. However, most of the applications are designed for academic purposes. 29% of the papers deal with the use of mobile technology in the detection of mental illness, while 67% have studied other technologies such as wearable sensor networks. 4% of the papers concern the use of web platforms in the detection and assessment of mental health disorders.

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Section: Bipolar Disorder (Bd)mentioning

confidence: 99%

A review about Technology in mental health sensing and assessment

et al. 2022

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“…old Romanian speaking children, boys and girls with difficulties in pronunciation of R and S sounds. 20F):42 14 to 60 years old Dutch-speaking children and adults without disabilities. (9M, 9F): 18 19 to 75 months old.…”

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confidence: 99%

“…X[41] An educational platform based on expert systems, speech recognition, and ludic activities to support the lexical and semantic development in children from 2 to 3 years.Y [3]SPELTA: An expert system to generate therapy plans for speech and language disorders. Z[42] SPELTA-Miner: An expert system based on data mining and multilabel classification to design therapy plans for communication disorders.AA[43] The AppVox mobile application, a tool for speech and language training sessions BB[10] A prelingual tool for the education of altered voices CC [9] A Game Application to assist Speech Language Pathologists in the Assessment of Children with Speech Disorders DD [44] End-User Recommendations on LOGOMON -a Computer Based Speech Therapy System for Romanian Language EE [45] Multimodal Speech Capture System for Speech Rehabilitation and Learning FF [46] Tabby Talks: An automated tool for the assessment of childhood apraxia of speech GG [47] AACVOX: mobile application for augmentative alternative communication to help people with speech disorder and motor impairment HH [5] An Online Expert System for Diagnostic Assessment Procedures on Young Children's Oral Speech and Language II [48] E-inclusion technologies for the speech handicapped…”

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confidence: 99%

A Systematic Review of Online Speech Therapy Systems for Intervention in Childhood Speech Communication Disorders

Attwell

Bennin

Tekinerdoğan

2022

Sensors

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Currently, not all children that need speech therapy have access to a therapist. With the current international shortage of speech–language pathologists (SLPs), there is a demand for online tools to support SLPs with their daily tasks. Several online speech therapy (OST) systems have been designed and proposed in the literature; however, the implementation of these systems is lacking. The technical knowledge that is needed to use these programs is a challenge for SLPs. There has been limited effort to systematically identify, analyze and report the findings of prior studies. We provide the results of an extensive literature review of OST systems for childhood speech communication disorders. We systematically review OST systems that can be used in clinical settings or from home as part of a treatment program for children with speech communication disorders. Our search strategy found 4481 papers, of which 35 were identified as focusing on speech therapy programs for speech communication disorders. The features of these programs were examined, and the main findings are extracted and presented. Our analysis indicates that most systems which are designed mainly to support the SLPs adopt and use supervised machine learning approaches that are either desktop-based or mobile-phone-based applications. Our findings reveal that speech therapy systems can provide important benefits for childhood speech. A collaboration between computer programmers and SLPs can contribute to implementing useful automated programs, leading to more children having access to good speech therapy.

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Mapping Data Mining Technique and Gamification Approach for Studying Post-Stroke Rehabilitation Training: A Systematic Literature Review

et al. 2023

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Data mining has been widely used in healthcare to provide treatment and care recommendations based on a collective prediction of individual conditions. For rehabilitation, various data mining techniques have been applied to predict and recommend suitable recovery paths and training. Also, the gamification concept was applied to rehabilitation training to motivate the patient to follow the training until the end. Researchers have conducted considerable research to investigate the validity and effectiveness of those techniques on massive patient data on specific conditions and treatment contexts. However, it is still unclear how to effectively offer customized rehabilitation training to stroke patients using gamification and data mining approaches. Thus, to understand how researchers studied them, we examined 34 peer-reviewed articles published in computer science and medical proceedings and journals between 2012 and 2022. We systematically reviewed the data mining and gamification techniques researchers had applied for post-stroke rehabilitation and related prediction models resulting from the data mining processes. As a result of the analyses, three significant contributions are identified. This article 1) identifies trends in data mining and gamification used in personalized post-stroke rehabilitation training; 2) maps trends in the study of data mining and gamification in post-stroke rehabilitation; and 3) identifies underexplored studies for future work. There is a definite need to continue developing and researching intervention strategies related to rehabilitation to address recovery problems by providing accuracy and protection of healthcare, as well as incorporating components that promote patients' motivation and engagement.

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SPELTA-Miner: An expert system based on data mining and multilabel classification to design therapy plans for communication disorders

Cited by 6 publications

References 5 publications

A review about Technology in mental health sensing and assessment

A review about Technology in mental health sensing and assessment

A Systematic Review of Online Speech Therapy Systems for Intervention in Childhood Speech Communication Disorders

Mapping Data Mining Technique and Gamification Approach for Studying Post-Stroke Rehabilitation Training: A Systematic Literature Review

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