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
- 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) - Discovery of the Hidden World with Large Language Models [100.38157787218044]
We introduce COAT: Causal representatiOn AssistanT.
COAT incorporates LLMs as a factor proposer that extracts the potential causal factors from unstructured data.
LLMs can also be instructed to provide additional information used to collect data values.
arXiv Detail & Related papers (2024-02-06T12:18:54Z) - 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) - Diagnosis Uncertain Models For Medical Risk Prediction [80.07192791931533]
We consider a patient risk model which has access to vital signs, lab values, and prior history but does not have access to a patient's diagnosis.
We show that such all-cause' risk models have good generalization across diagnoses but have a predictable failure mode.
We propose a fix for this problem by explicitly modeling the uncertainty in risk prediction coming from uncertainty in patient diagnoses.
arXiv Detail & Related papers (2023-06-29T23:36:04Z) - Informing clinical assessment by contextualizing post-hoc explanations
of risk prediction models in type-2 diabetes [50.8044927215346]
We consider a comorbidity risk prediction scenario and focus on contexts regarding the patients clinical state.
We employ several state-of-the-art LLMs to present contexts around risk prediction model inferences and evaluate their acceptability.
Our paper is one of the first end-to-end analyses identifying the feasibility and benefits of contextual explanations in a real-world clinical use case.
arXiv Detail & Related papers (2023-02-11T18:07:11Z) - Predicting Development of Chronic Obstructive Pulmonary Disease and its
Risk Factor Analysis [0.9146620606615891]
Chronic Obstructive Pulmonary Disease (COPD) is an irreversible airway obstruction with a high societal burden.
We aim to identify COPD risk factors by applying machine learning models that integrate sociodemographic, clinical, and genetic data to predict COPD development.
arXiv Detail & Related papers (2023-02-06T21:50:34Z) - 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) - Bootstrapping Your Own Positive Sample: Contrastive Learning With
Electronic Health Record Data [62.29031007761901]
This paper proposes a novel contrastive regularized clinical classification model.
We introduce two unique positive sampling strategies specifically tailored for EHR data.
Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
arXiv Detail & Related papers (2021-04-07T06:02:04Z)
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.