A methodology for the characterization and diagnosis of cognitive impairments—Application to specific language impairment

Oliva, Jesús; Serrano, J. Ignacio; Castillo, M. Dolores del; Iglesias, Ángel

doi:10.1016/j.artmed.2014.04.002

Cited by 8 publications

(5 citation statements)

References 26 publications

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“…The application of ML in health is also of concern. Indeed, ML is widely used in critical disease models in cardiology, neurology, and diabetes research [ 6 ] to automatically identify heart disease risk factors [ 7 ], to classify primary progressive aphasia subtypes [ 8 ], and for the characterization and diagnosis of cognitive impairments [ 9 ], diabetes, and cardiovascular disorders [ 10 - 17 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and Natural Language Processing in Mental Health: Systematic Review

Glaz¹,

Haralambous²,

Kim-Dufor³

et al. 2021

J Med Internet Res

290

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Background Machine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining. Objective The primary aim of this systematic review was to summarize and characterize, in methodological and technical terms, studies that used machine learning and NLP techniques for mental health. The secondary aim was to consider the potential use of these methods in mental health clinical practice Methods This systematic review follows the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) guidelines and is registered with PROSPERO (Prospective Register of Systematic Reviews; number CRD42019107376). The search was conducted using 4 medical databases (PubMed, Scopus, ScienceDirect, and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, and mental disorder. The exclusion criteria were as follows: languages other than English, anonymization process, case studies, conference papers, and reviews. No limitations on publication dates were imposed. Results A total of 327 articles were identified, of which 269 (82.3%) were excluded and 58 (17.7%) were included in the review. The results were organized through a qualitative perspective. Although studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into 3 categories: patients included in medical databases, patients who came to the emergency room, and social media users. The main objectives were to extract symptoms, classify severity of illness, compare therapy effectiveness, provide psychopathological clues, and challenge the current nosography. Medical records and social media were the 2 major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than transparent functioning classifiers. Python was the most frequently used platform. Conclusions Machine learning and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new information, and only one major category of the population (ie, social media users) is an imprecise cohort. Moreover, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, machine learning and NLP techniques provide useful information from unexplored data (ie, patients’ daily habits that are usually inaccessible to care providers). Before considering It as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner. Machine learning and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice.

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Section: Introductionmentioning

confidence: 99%

Machine Learning and Natural Language Processing in Mental Health: Systematic Review

Glaz¹,

Haralambous²,

Kim-Dufor³

et al. 2021

J Med Internet Res

290

View full text Add to dashboard Cite

show abstract

“…They reported accuracy of 98.82% with feature selection. Oliva et al [28] proposed an SLI detection method using machine learning techniques. In their study, data (mean length of utterance, ungrammatical sentences, correct use of articles, correct use of verbs, correct use of clitics, correct use of theme arguments, proportion of ditransitive structures) of 24 SLI children and 24 control children were used.…”

Section: Literature Reviewmentioning

confidence: 99%

Novel favipiravir pattern-based learning model for automated detection of specific language impairment disorder using vowels

Barua

Aydemir

Doğan

et al. 2022

Neural Comput & Applic

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Specific language impairment (SLI) is one of the most common diseases in children, and early diagnosis can help to obtain better timely therapy economically. It is difficult and time-consuming for clinicians to accurately detect SLI through standard clinical assessments. Hence, machine learning algorithms have been developed to assist in the accurate diagnosis of SLI. This work aims to investigate the graph of the favipiravir molecule-based feature extraction function and propose an accurate SLI detection model using vowels. We proposed a novel handcrafted machine learning framework. This architecture comprises the favipiravir molecular structure pattern, statistical feature extractor, wavelet packet decomposition (WPD), iterative neighborhood component analysis (INCA), and support vector machine (SVM) classifier. Two feature extraction models, statistical and textural, are employed in the handcrafted feature generation methodology. A new nature-inspired graph-based feature extractor that uses the chemical depiction of the favipiravir (favipiravir became popular with the COVID-19 pandemic) is employed for feature extraction. Finally, the proposed favipiravir pattern, statistical feature extractor, and wavelet packet decomposition are used to create a feature vector. Moreover, a statistical feature extractor is used in this work. The WPD generates multilevel features, and the most meaningful features are selected using the NCA feature selector. Finally, these chosen features are fed to SVM classifier for automated classification. Two validation methods, (i) leave one subject out (LOSO) and (ii) tenfold cross-validations (CV), are used to obtain robust classification results. Our proposed favipiravir pattern-based model developed using a vowel dataset can detect SLI children with an accuracy of 99.87% and 98.86% using tenfold and LOSO CV strategies, respectively. These results demonstrated the high vowel classification ability of the proposed favipiravir pattern-based model.

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“…The application of ML in health is also of concern. Indeed, ML is widely used in critical disease models in cardiology, neurology, and diabetes research [6] to automatically identify heart disease risk factors [7], to classify primary progressive aphasia subtypes [8], and for the characterization and diagnosis of cognitive impairments [9], diabetes, and cardiovascular disorders [10][11][12][13][14][15][16][17].…”

Section: Machine Learningmentioning

confidence: 99%

Machine learning and natural language processing in mental health: a systematic review

Glaz¹,

[]²,

Berrouiguet³

et al. 2019

Preprint

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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A methodology for the characterization and diagnosis of cognitive impairments—Application to specific language impairment

Cited by 8 publications

References 26 publications

Machine Learning and Natural Language Processing in Mental Health: Systematic Review

Machine Learning and Natural Language Processing in Mental Health: Systematic Review

Novel favipiravir pattern-based learning model for automated detection of specific language impairment disorder using vowels

Machine learning and natural language processing in mental health: a systematic review

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