Feasibility of Identifying Factors Related to Alzheimer's Disease and Related Dementia in Real-World Data

• URL: http://arxiv.org/abs/2402.15515v1
• Date: Sat, 3 Feb 2024 18:17:19 GMT
• Title: Feasibility of Identifying Factors Related to Alzheimer's Disease and Related Dementia in Real-World Data
• Authors: Aokun Chen, Qian Li, Yu Huang, Yongqiu Li, Yu-neng Chuang, Xia Hu, Serena Guo, Yonghui Wu, Yi Guo, Jiang Bian
• Abstract summary: In total, we extracted 477 risk factors in 10 categories from 537 studies. Genetic testing for AD/ADRD is still not a common practice and is poorly documented in both structured and unstructured EHRs. Considering the constantly evolving research on AD/ADRD risk factors, literature mining via NLP methods offers a solution to automatically update our knowledge map.
• Score: 56.7069469207376
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A comprehensive view of factors associated with AD/ADRD will significantly aid in studies to develop new treatments for AD/ADRD and identify high-risk populations and patients for prevention efforts. In our study, we summarized the risk factors for AD/ADRD by reviewing existing meta-analyses and review articles on risk and preventive factors for AD/ADRD. In total, we extracted 477 risk factors in 10 categories from 537 studies. We constructed an interactive knowledge map to disseminate our study results. Most of the risk factors are accessible from structured Electronic Health Records (EHRs), and clinical narratives show promise as information sources. However, evaluating genomic risk factors using RWD remains a challenge, as genetic testing for AD/ADRD is still not a common practice and is poorly documented in both structured and unstructured EHRs. Considering the constantly evolving research on AD/ADRD risk factors, literature mining via NLP methods offers a solution to automatically update our knowledge map.

Related papers

• HACSurv: A Hierarchical Copula-based Approach for Survival Analysis with Dependent Competing Risks [51.95824566163554]
  HACSurv is a survival analysis method that learns structures and cause-specific survival functions from data with competing risks. By capturing the dependencies between risks and censoring, HACSurv achieves better survival predictions.
  arXiv  Detail & Related papers  (2024-10-19T18:52:18Z)
• 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)
• Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework [77.45983464131977]
  We focus on how likely it is that a RAG model's prediction is incorrect, resulting in uncontrollable risks in real-world applications. Our research identifies two critical latent factors affecting RAG's confidence in its predictions. We develop a counterfactual prompting framework that induces the models to alter these factors and analyzes the effect on their answers.
  arXiv  Detail & Related papers  (2024-09-24T14:52:14Z)
• Towards Within-Class Variation in Alzheimer's Disease Detection from Spontaneous Speech [60.08015780474457]
  Alzheimer's Disease (AD) detection has emerged as a promising research area that employs machine learning classification models. We identify within-class variation as a critical challenge in AD detection: individuals with AD exhibit a spectrum of cognitive impairments. We propose two novel methods: Soft Target Distillation (SoTD) and Instance-level Re-balancing (InRe), targeting two problems respectively.
  arXiv  Detail & Related papers  (2024-09-22T02:06:05Z)
• Risks and NLP Design: A Case Study on Procedural Document QA [52.557503571760215]
  We argue that clearer assessments of risks and harms to users will be possible when we specialize the analysis to more concrete applications and their plausible users. We conduct a risk-oriented error analysis that could then inform the design of a future system to be deployed with lower risk of harm and better performance.
  arXiv  Detail & Related papers  (2024-08-16T17:23:43Z)
• Automatic Extraction of Disease Risk Factors from Medical Publications [1.321009936753118]
  We present a novel approach to automating the identification of risk factors for diseases from medical literature. We first identify relevant articles, then classify them based on the presence of risk factor discussions, and finally extract specific risk factor information for a disease. Our contributions include the development of a comprehensive pipeline for the automated extraction of risk factors and the compilation of several datasets.
  arXiv  Detail & Related papers  (2024-07-10T05:17:55Z)
• Risk of AI in Healthcare: A Comprehensive Literature Review and Study Framework [0.5130062125323206]
  This study conducts a thorough examination of the research stream focusing on AI risks in healthcare, aiming to explore the distinct genres within this domain. A selection criterion was employed to carefully analyze 39 articles to identify three primary genres of AI risks prevalent in healthcare: clinical data risks, technical risks, and socio-ethical risks.
  arXiv  Detail & Related papers  (2023-09-25T21:09:21Z)
• Self-explainable Graph Neural Network for Alzheimer's Disease And Related Dementias Risk Prediction [5.601973265501243]
  Alzheimer's disease and related dementias (ADRD) ranks as the sixth leading cause of death in the US. Merging machine learning with claims data can reveal additional risk factors and interconnections among diverse medical codes.
  arXiv  Detail & Related papers  (2023-09-12T20:12:08Z)
• A Survey of Risk-Aware Multi-Armed Bandits [84.67376599822569]
  We review various risk measures of interest, and comment on their properties. We consider algorithms for the regret minimization setting, where the exploration-exploitation trade-off manifests. We conclude by commenting on persisting challenges and fertile areas for future research.
  arXiv  Detail & Related papers  (2022-05-12T02:20:34Z)
• Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review [2.8136734847819773]
  Alzheimer disease (AD) is the most common cause of dementia, a syndrome characterized by cognitive impairment severe enough to interfere with activities of daily life. We searched for articles published between January 1, 2010, and May 31, 2020, in PubMed, Scopus, ScienceDirect, IEEE Explore Digital Library, Association for Computing Machinery Digital Library, and arXiv. We used predefined criteria to select relevant articles and summarized them according to key components of ML analysis such as data characteristics, computational algorithms, and research focus.
  arXiv  Detail & Related papers  (2021-08-05T04:38:47Z)

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.