AD-BERT: Using pre-trained language model to predict the progression from mild cognitive impairment to Alzheimer's disease

Mao, Chengsheng; Xu, Jie; Rasmussen, Luke V.; Li, Yikuan; Adekkanattu, Prakash; Pacheco, Jennifer A.; Bonakdarpour, Borna; Vassar, Robert; Shen, Libin; Jiang, Guoqian; Wang, Fei; Pathak, Jyotishman; Luo, Yuan

doi:10.1016/j.jbi.2023.104442

Cited by 10 publications

(3 citation statements)

References 52 publications

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“…Second, clinical summaries have been used with LLMs to make differential diagnoses (Koga et al, 2024 ). Mao et al ( 2023 ) showed that a language model can use clinical notes to successfully predict the transition from mild cognitive impairment to Alzheimer's disease. Third, diagnostic markers can be extracted from patients' speech, either directly from acoustic signals or from the transcribed text.…”

Section: Methodsmentioning

confidence: 99%

Introduction to Large Language Models (LLMs) for dementia care and research

Treder,

Lee,

Tsvetanov

2024

Front. Dement.

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IntroductionDementia is a progressive neurodegenerative disorder that affects cognitive abilities including memory, reasoning, and communication skills, leading to gradual decline in daily activities and social engagement. In light of the recent advent of Large Language Models (LLMs) such as ChatGPT, this paper aims to thoroughly analyse their potential applications and usefulness in dementia care and research.MethodTo this end, we offer an introduction into LLMs, outlining the key features, capabilities, limitations, potential risks, and practical considerations for deployment as easy-to-use software (e.g., smartphone apps). We then explore various domains related to dementia, identifying opportunities for LLMs to enhance understanding, diagnostics, and treatment, with a broader emphasis on improving patient care. For each domain, the specific contributions of LLMs are examined, such as their ability to engage users in meaningful conversations, deliver personalized support, and offer cognitive enrichment. Potential benefits encompass improved social interaction, enhanced cognitive functioning, increased emotional well-being, and reduced caregiver burden. The deployment of LLMs in caregiving frameworks also raises a number of concerns and considerations. These include privacy and safety concerns, the need for empirical validation, user-centered design, adaptation to the user's unique needs, and the integration of multimodal inputs to create more immersive and personalized experiences. Additionally, ethical guidelines and privacy protocols must be established to ensure responsible and ethical deployment of LLMs.ResultsWe report the results on a questionnaire filled in by people with dementia (PwD) and their supporters wherein we surveyed the usefulness of different application scenarios of LLMs as well as the features that LLM-powered apps should have. Both PwD and supporters were largely positive regarding the prospect of LLMs in care, although concerns were raised regarding bias, data privacy and transparency.DiscussionOverall, this review corroborates the promising utilization of LLMs to positively impact dementia care by boosting cognitive abilities, enriching social interaction, and supporting caregivers. The findings underscore the importance of further research and development in this field to fully harness the benefits of LLMs and maximize their potential for improving the lives of individuals living with dementia.

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Section: Methodsmentioning

confidence: 99%

Introduction to Large Language Models (LLMs) for dementia care and research

Treder,

Lee,

Tsvetanov

2024

Front. Dement.

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“…Our bulk NLP effort has powered research and development efforts, driven business intelligence, and facilitated clinical tasks such as computational phenotyping, 30 , 31 disease predictive modeling, 32 , 33 , 34 semantic analysis, 35 and adverse event detection. 36 , 37 For example, we have collaborated with NMEDW team to deposit results from the NLP pipelines into data marts for breast cancer patients (deployment completed) 38 and cardiovascular disease patients (deployment in progress).…”

Section: Building Collaborative Artificial Intelligence In Healthcarementioning

confidence: 99%

Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system

Luo,

Mao,

Sanchez‐Pinto

et al. 2024

Learning Health Systems

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IntroductionThe rapid development of artificial intelligence (AI) in healthcare has exposed the unmet need for growing a multidisciplinary workforce that can collaborate effectively in the learning health systems. Maximizing the synergy among multiple teams is critical for Collaborative AI in Healthcare.MethodsWe have developed a series of data, tools, and educational resources for cultivating the next generation of multidisciplinary workforce for Collaborative AI in Healthcare. We built bulk‐natural language processing pipelines to extract structured information from clinical notes and stored them in common data models. We developed multimodal AI/machine learning (ML) tools and tutorials to enrich the toolbox of the multidisciplinary workforce to analyze multimodal healthcare data. We have created a fertile ground to cross‐pollinate clinicians and AI scientists and train the next generation of AI health workforce to collaborate effectively.ResultsOur work has democratized access to unstructured health information, AI/ML tools and resources for healthcare, and collaborative education resources. From 2017 to 2022, this has enabled studies in multiple clinical specialties resulting in 68 peer‐reviewed publications. In 2022, our cross‐discipline efforts converged and institutionalized into the Center for Collaborative AI in Healthcare.ConclusionsOur Collaborative AI in Healthcare initiatives has created valuable educational and practical resources. They have enabled more clinicians, scientists, and hospital administrators to successfully apply AI methods in their daily research and practice, develop closer collaborations, and advanced the institution‐level learning health system.

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“…Compared with conventional machine learning approaches, the accuracy of AD prediction using DL algorithms was much higher. Using MRI images, the authors [29] predicted the progression of MCI to AD using a deep learning system. The research indicated that the deep learning system predicted the onset of AD within three years with an accuracy of 83.3 percent.…”

Section: Related Workmentioning

confidence: 99%

Alzheimer’s Disease Prediction Using Deep Feature Extraction and Optimization

Mohammad,

Al Ahmadi

2023

Mathematics

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Alzheimer’s disease (AD) is a prevalent neurodegenerative disorder that affects a substantial proportion of the population. The accurate and timely prediction of AD carries considerable importance in enhancing the diagnostic process and improved treatment. This study provides a thorough examination of AD prediction using the VGG19 deep learning model. The primary objective of this study is to investigate the effectiveness of feature fusion and optimization techniques in enhancing the accuracy of classification. The generation of a comprehensive feature map is achieved through the fusion of features that have been extracted from the fc7 and fc8 layers of VGG19. Several machine learning algorithms are employed to classify integrated features and recognize AD. The amalgamated feature map demonstrates a significant level of accuracy of 98% in the prognostication of AD, outperforming present cutting-edge methodologies. In this study, a methodology is utilized that makes use of the whale optimization algorithm (WoA), a metaheuristic approach to optimize features through feature selection. Feature optimization aims to eliminate redundant features and enhance the discriminatory power of the selected features. Following the optimization procedure, the F-KNN algorithm attained a precision level of 99%, surpassing the present state-of-the-art (SOTA) results reported in the current literature.

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AD-BERT: Using pre-trained language model to predict the progression from mild cognitive impairment to Alzheimer's disease

Cited by 10 publications

References 52 publications

Introduction to Large Language Models (LLMs) for dementia care and research

Introduction to Large Language Models (LLMs) for dementia care and research

Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system

Alzheimer’s Disease Prediction Using Deep Feature Extraction and Optimization

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