AD-AutoGPT: An Autonomous GPT for Alzheimer's Disease Infodemiology
- URL: http://arxiv.org/abs/2306.10095v1
- Date: Fri, 16 Jun 2023 16:35:59 GMT
- Title: AD-AutoGPT: An Autonomous GPT for Alzheimer's Disease Infodemiology
- Authors: Haixing Dai, Yiwei Li, Zhengliang Liu, Lin Zhao, Zihao Wu, Suhang
Song, Ye Shen, Dajiang Zhu, Xiang Li, Sheng Li, Xiaobai Yao, Lu Shi,
Quanzheng Li, Zhuo Chen, Donglan Zhang, Gengchen Mai, Tianming Liu
- Abstract summary: We develop a novel tool called AD-AutoGPT which can conduct data collection, processing, and analysis about Alzheimer's Disease in an autonomous manner.
We collated comprehensive data from a variety of news sources, including the Alzheimer's Association, BBC, Mayo Clinic, and the National Institute on Aging since June 2022.
This application of AD-AutoGPT in public health signifies the transformative potential of AI in facilitating a data-rich understanding of complex health narratives.
- Score: 19.175068769088366
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: In this pioneering study, inspired by AutoGPT, the state-of-the-art
open-source application based on the GPT-4 large language model, we develop a
novel tool called AD-AutoGPT which can conduct data collection, processing, and
analysis about complex health narratives of Alzheimer's Disease in an
autonomous manner via users' textual prompts. We collated comprehensive data
from a variety of news sources, including the Alzheimer's Association, BBC,
Mayo Clinic, and the National Institute on Aging since June 2022, leading to
the autonomous execution of robust trend analyses, intertopic distance maps
visualization, and identification of salient terms pertinent to Alzheimer's
Disease. This approach has yielded not only a quantifiable metric of relevant
discourse but also valuable insights into public focus on Alzheimer's Disease.
This application of AD-AutoGPT in public health signifies the transformative
potential of AI in facilitating a data-rich understanding of complex health
narratives like Alzheimer's Disease in an autonomous manner, setting the
groundwork for future AI-driven investigations in global health landscapes.
Related papers
- Leveraging Social Determinants of Health in Alzheimer's Research Using LLM-Augmented Literature Mining and Knowledge Graphs [33.755845172595365]
Growing evidence suggests that social determinants of health (SDoH) affect individuals' risks of developing Alzheimer's disease (AD) and related dementias.
This study presents a novel, automated framework to mine SDoH knowledge from extensive literature and integrate it with AD-related biological entities.
Our framework shows promise for enhancing knowledge discovery in AD and can be generalized to other SDoH-related research areas.
arXiv Detail & Related papers (2024-10-04T21:39:30Z) - A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis [51.07114445705692]
neurodegenerative diseases (NDs) traditionally require extensive healthcare resources and human effort for medical diagnosis and monitoring.
As a crucial disease-related motor symptom, human gait can be exploited to characterize different NDs.
The current advances in artificial intelligence (AI) models enable automatic gait analysis for NDs identification and classification.
arXiv Detail & Related papers (2024-05-21T06:44:40Z) - Automatic Detection of Alzheimer's Disease with Multi-Modal Fusion of
Clinical MRI Scans [8.684668542584701]
15 million Americans will have either clinical AD or mild cognitive impairment by 2060.
We aim to predict the stage of the disease based on two different types of brain MRI scans.
We design an AlexNet-based deep learning model that learns the synergy of complementary information from both T1 and FLAIR MRI scans.
arXiv Detail & Related papers (2023-11-30T04:32:28Z) - MemoryCompanion: A Smart Healthcare Solution to Empower Efficient
Alzheimer's Care Via Unleashing Generative AI [8.741075482543991]
This paper unveils MemoryCompanion', a pioneering digital health solution specifically tailored for Alzheimer's disease (AD) patients and their caregivers.
MemoryCompanion manifests a personalized caregiving paradigm, fostering interactions via voice-cloning and talking-face mechanisms.
Our methodology, grounded in its innovative design, addresses both the caregiving and technological challenges intrinsic to this domain.
arXiv Detail & Related papers (2023-11-20T19:41:50Z) - Leveraging Pretrained Representations with Task-related Keywords for
Alzheimer's Disease Detection [69.53626024091076]
Alzheimer's disease (AD) is particularly prominent in older adults.
Recent advances in pre-trained models motivate AD detection modeling to shift from low-level features to high-level representations.
This paper presents several efficient methods to extract better AD-related cues from high-level acoustic and linguistic features.
arXiv Detail & Related papers (2023-03-14T16:03:28Z) - The Role of AI in Drug Discovery: Challenges, Opportunities, and
Strategies [97.5153823429076]
The benefits, challenges and drawbacks of AI in this field are reviewed.
The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
arXiv Detail & Related papers (2022-12-08T23:23:39Z) - GDPR Compliant Collection of Therapist-Patient-Dialogues [48.091760741427656]
We elaborate on the challenges we faced in starting our collection of therapist-patient dialogues in a psychiatry clinic under the General Data Privacy Regulation of the European Union.
We give an overview of each step in our procedure and point out the potential pitfalls to motivate further research in this field.
arXiv Detail & Related papers (2022-11-22T15:51:10Z) - Exploring linguistic feature and model combination for speech
recognition based automatic AD detection [61.91708957996086]
Speech based automatic AD screening systems provide a non-intrusive and more scalable alternative to other clinical screening techniques.
Scarcity of specialist data leads to uncertainty in both model selection and feature learning when developing such systems.
This paper investigates the use of feature and model combination approaches to improve the robustness of domain fine-tuning of BERT and Roberta pre-trained text encoders.
arXiv Detail & Related papers (2022-06-28T05:09:01Z) - Conformer Based Elderly Speech Recognition System for Alzheimer's
Disease Detection [62.23830810096617]
Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care to delay further progression.
This paper presents the development of a state-of-the-art Conformer based speech recognition system built on the DementiaBank Pitt corpus for automatic AD detection.
arXiv Detail & Related papers (2022-06-23T12:50:55Z) - Multimodal Inductive Transfer Learning for Detection of Alzheimer's
Dementia and its Severity [39.57255380551913]
We present a novel architecture that leverages acoustic, cognitive, and linguistic features to form a multimodal ensemble system.
It uses specialized artificial neural networks with temporal characteristics to detect Alzheimer's dementia (AD) and its severity.
Our system achieves state-of-the-art test accuracy, precision, recall, and F1-score of 83.3% each for AD classification, and state-of-the-art test root mean squared error (RMSE) of 4.60 for MMSE score regression.
arXiv Detail & Related papers (2020-08-30T21:47:26Z) - Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS
Challenge [10.497861245133086]
The ADReSS Challenge at INTERSPEECH 2020 defines a shared task through which different approaches to the automated recognition of Alzheimer's dementia can be compared.
ADReSS provides researchers with a benchmark speech dataset which has been acoustically pre-processed and balanced in terms of age and gender.
arXiv Detail & Related papers (2020-04-14T23:25:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.