Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions

Javed, Abdul Rehman; Saadia, Ayesha; Mughal, Huma; Gadekallu, Thippa Reddy; Rizwan, Muhammad; Maddikunta, Praveen Kumar Reddy; Mahmud, Mufti; Liyanage, Madhusanka; Hussain, Amir

doi:10.1007/s12559-023-10153-4

Cited by 20 publications

(5 citation statements)

References 278 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine learning tools can be used with ease, and this can be due to their reliability and cost-reducing functionalities [53,54]. Artificial Intelligence includes machine learning tools that deal with structured and unstructured data and help researchers across various domains achieve appropriate results [55,56]. This study used AI (ML) tools to predict land surface temperatures based on air quality parameters like CO, HCHO, NO2, SO2, AAI, and AOD.…”

Section: Discussionmentioning

confidence: 99%

Machine learning assisted prediction of land surface temperature (LST) based on major air pollutants over the Annamayya District of India

Mogaraju

2024

International Journal of Engineering and Geosciences

View full text Add to dashboard Cite

Remote sensing (RS), Geographic information systems (GIS), and Machine learning can be integrated to predict land surface temperatures (LST) based on the data related to carbon monoxide (CO), Formaldehyde (HCHO), Nitrogen dioxide (NO2), Sulphur dioxide (SO2), absorbing aerosol index (AAI), and Aerosol optical depth (AOD). In this study, LST was predicted using machine learning classifiers, i.e., Extra trees classifier (ET), Logistic regressors (LR), and Random Forests (RF). The accuracy of the LR classifier (0.89 or 89%) is higher than ET (82%) and RF (82%) classifiers. Evaluation metrics for each classifier are presented in the form of accuracy, Area under the curve (AUC), Recall, Precision, F1 score, Kappa, and MCC (Matthew’s correlation coefficient). Based on the relative performance of the ML classifiers, it was concluded that the LR classifier performed better. Geographic information systems and RS tools were used to extract the data across spatial and temporal scales (2019 to 2022). In order to evaluate the model graphically, ROC (Receiver operating characteristic) curve, Confusion matrix, Validation curve, Classification report, Feature importance plot, and t- SNE (t-distributed stochastic neighbour embedding) plot were used. On validation of each ML classifier, it was observed that the RF classifier returned model complexity due to limited data availability and other factors yet to be studied post data availability. Sentinel-5-P and MODIS data are used in this study.

show abstract

Section: Discussionmentioning

confidence: 99%

Machine learning assisted prediction of land surface temperature (LST) based on major air pollutants over the Annamayya District of India

Mogaraju

2024

International Journal of Engineering and Geosciences

View full text Add to dashboard Cite

show abstract

“…The advent of AI has resulted in automated assessment techniques which improve the accuracy of diagnosis. ML and AI-based approaches like Support Vector Machine (SVM), neural networks and ensemble techniques like Convolutional Neural Network (CNN), AlexNet, GoogLeNet and LeNet5 have yielded some of the best results and accuracies when it comes to the use of AI for the assessment of cognitive mental health disorders ( 42 ).…”

Section: Advantages Of Ai Applications In Mental Health Outcomesmentioning

confidence: 99%

Artificial intelligence in positive mental health: a narrative review

Thakkar,

Gupta,

De Sousa

2024

Front. Digit. Health

View full text Add to dashboard Cite

The paper reviews the entire spectrum of Artificial Intelligence (AI) in mental health and its positive role in mental health. AI has a huge number of promises to offer mental health care and this paper looks at multiple facets of the same. The paper first defines AI and its scope in the area of mental health. It then looks at various facets of AI like machine learning, supervised machine learning and unsupervised machine learning and other facets of AI. The role of AI in various psychiatric disorders like neurodegenerative disorders, intellectual disability and seizures are discussed along with the role of AI in awareness, diagnosis and intervention in mental health disorders. The role of AI in positive emotional regulation and its impact in schizophrenia, autism spectrum disorders and mood disorders is also highlighted. The article also discusses the limitations of AI based approaches and the need for AI based approaches in mental health to be culturally aware, with structured flexible algorithms and an awareness of biases that can arise in AI. The ethical issues that may arise with the use of AI in mental health are also visited.

show abstract

“…Recent research studies have analyzed the usage of different ML methods to potentially detect or assess cognitive decline [18,19]. The methodologies vary from traditional ML approaches to advanced deep learning models, using different types of data, including genetic data, neuroimaging data, neuropsychological assessments, and, more recently, data from wearable devices [20]. In a study published by Gomes et al [21], the potential of ML algorithms to predict cognitive and functional decline in individuals aged 75 and older using routine laboratory tests was analyzed.…”

Section: Related Workmentioning

confidence: 99%

Graph Learning and Deep Neural Network Ensemble for Supporting Cognitive Decline Assessment

Antonesi,

Rancea,

Cioara

et al. 2023

Technologies

View full text Add to dashboard Cite

Cognitive decline represents a significant public health concern due to its severe implications on memory and general health. Early detection is crucial to initiate timely interventions and improve patient outcomes. However, traditional diagnosis methods often rely on personal interpretations or biases, may not detect the early stages of cognitive decline, or involve invasive screening procedures; thus, there is a growing interest in developing non-invasive methods benefiting also from the technological advances. Wearable devices and Internet of Things sensors can monitor various aspects of daily life together with health parameters and can provide valuable data regarding people’s behavior. In this paper, we propose a technical solution that can be useful for potentially supporting cognitive decline assessment in early stages, by employing advanced machine learning techniques for detecting higher activity fragmentation based on daily activity monitoring using wearable devices. Our approach also considers data coming from wellbeing assessment questionnaires that can offer other important insights about a monitored person. We use deep neural network models to capture complex, non-linear relationships in the daily activities data and graph learning for the structural wellbeing information in the questionnaire answers. The proposed solution is evaluated in a simulated environment on a large synthetic dataset, the results showing that our approach can offer an alternative as a support for early detection of cognitive decline during patient-assessment processes.

show abstract

Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions

Cited by 20 publications

References 278 publications

Machine learning assisted prediction of land surface temperature (LST) based on major air pollutants over the Annamayya District of India

Machine learning assisted prediction of land surface temperature (LST) based on major air pollutants over the Annamayya District of India

Artificial intelligence in positive mental health: a narrative review

Graph Learning and Deep Neural Network Ensemble for Supporting Cognitive Decline Assessment

Contact Info

Product

Resources

About