Yikuan Li
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View article: Leveraging large language models for academic conference organization
Leveraging large language models for academic conference organization Open
We piloted using Large Language Models (LLMs) for organizing AMIA 2024 Informatics Summit. LLMs were prompt engineered to develop algorithms for reviewer assignments, group presentations into sessions, suggest session titles, and provide o…
View article: FHIR-GPT Enhances Health Interoperability with Large Language Models
FHIR-GPT Enhances Health Interoperability with Large Language Models Open
Advancing health interoperability can significantly benefit health research, including phenotyping, clinical trial support, and public health surveillance. Federal agencies, including ONC, CDC, and CMS, have been collectively collaborating…
View article: Enhancing Health Data Interoperability with Large Language Models: A FHIR Study
Enhancing Health Data Interoperability with Large Language Models: A FHIR Study Open
In this study, we investigated the ability of the large language model (LLM) to enhance healthcare data interoperability. We leveraged the LLM to convert clinical texts into their corresponding FHIR resources. Our experiments, conducted on…
View article: Deep Reinforcement Learning for Efficient and Fair Allocation of Health Care Resources
Deep Reinforcement Learning for Efficient and Fair Allocation of Health Care Resources Open
Scarcity of health care resources could result in the unavoidable consequence of rationing. For example, ventilators are often limited in supply, especially during public health emergencies or in resource-constrained health care settings, …
View article: Patterns of diverse and changing sentiments towards COVID-19 vaccines: a sentiment analysis study integrating 11 million tweets and surveillance data across over 180 countries
Patterns of diverse and changing sentiments towards COVID-19 vaccines: a sentiment analysis study integrating 11 million tweets and surveillance data across over 180 countries Open
Objectives Vaccines are crucial components of pandemic responses. Over 12 billion coronavirus disease 2019 (COVID-19) vaccines were administered at the time of writing. However, public perceptions of vaccines have been complex. We integrat…
View article: Deep Reinforcement Learning for Cost-Effective Medical Diagnosis
Deep Reinforcement Learning for Cost-Effective Medical Diagnosis Open
Dynamic diagnosis is desirable when medical tests are costly or time-consuming. In this work, we use reinforcement learning (RL) to find a dynamic policy that selects lab test panels sequentially based on previous observations, ensuring ac…
View article: A comparative study of pretrained language models for long clinical text
A comparative study of pretrained language models for long clinical text Open
Objective Clinical knowledge-enriched transformer models (eg, ClinicalBERT) have state-of-the-art results on clinical natural language processing (NLP) tasks. One of the core limitations of these transformer models is the substantial memor…
View article: Multimodal machine learning in precision health: A scoping review
Multimodal machine learning in precision health: A scoping review Open
Machine learning is frequently being leveraged to tackle problems in the health sector including utilization for clinical decision-support. Its use has historically been focused on single modal data. Attempts to improve prediction and mimi…
View article: AD-BERT: Using Pre-trained contextualized embeddings to Predict the Progression from Mild Cognitive Impairment to Alzheimer's Disease
AD-BERT: Using Pre-trained contextualized embeddings to Predict the Progression from Mild Cognitive Impairment to Alzheimer's Disease Open
Objective: We develop a deep learning framework based on the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model using unstructured clinical notes from electronic health records (EHRs) to predict the risk of di…
View article: Improving Fairness in the Prediction of Heart Failure Length of Stay and Mortality by Integrating Social Determinants of Health
Improving Fairness in the Prediction of Heart Failure Length of Stay and Mortality by Integrating Social Determinants of Health Open
Background: Machine learning (ML) approaches have been broadly applied to the prediction of length of stay and mortality in hospitalized patients. ML may also reduce societal health burdens, assist in health resources planning and improve …
View article: Deep Learning Reveals Patterns of Diverse and Changing Sentiments Towards COVID-19 Vaccines Based on 11 Million Tweets
Deep Learning Reveals Patterns of Diverse and Changing Sentiments Towards COVID-19 Vaccines Based on 11 Million Tweets Open
Over 12 billion doses of COVID-19 vaccines have been administered at the time of writing. However, public perceptions of vaccines have been complex. We analyzed COVID-19 vaccine-related tweets to understand the evolving perceptions of COVI…
View article: Machine Learning in Causal Inference: Application in Pharmacovigilance
Machine Learning in Causal Inference: Application in Pharmacovigilance Open
Monitoring adverse drug events or pharmacovigilance has been promoted by the World Health Organization to assure the safety of medicines through a timely and reliable information exchange regarding drug safety issues. We aim to discuss the…
View article: Multimodal Machine Learning in Precision Health
Multimodal Machine Learning in Precision Health Open
As machine learning and artificial intelligence are more frequently being leveraged to tackle problems in the health sector, there has been increased interest in utilizing them in clinical decision-support. This has historically been the c…
View article: Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences
Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences Open
Transformers-based models, such as BERT, have dramatically improved the performance for various natural language processing tasks. The clinical knowledge enriched model, namely ClinicalBERT, also achieved state-of-the-art results when perf…
View article: Disparities in Social Determinants among Performances of Mortality Prediction with Machine Learning for Sepsis Patients
Disparities in Social Determinants among Performances of Mortality Prediction with Machine Learning for Sepsis Patients Open
Background Sepsis is one of the most life-threatening circumstances for critically ill patients in the US, while a standardized criteria for sepsis identification is still under development. Disparities in social determinants of sepsis pat…
View article: Early Prediction of Mortality in Critical Care Setting in Sepsis Patients Using Structured Features and Unstructured Clinical Notes
Early Prediction of Mortality in Critical Care Setting in Sepsis Patients Using Structured Features and Unstructured Clinical Notes Open
Sepsis is an important cause of mortality, especially in intensive care unit (ICU) patients. Developing novel methods to identify early mortality is critical for improving survival outcomes in sepsis patients. Using the MIMIC-III database,…
View article: Using Tweets to Understand How COVID-19–Related Health Beliefs Are Affected in the Age of Social Media: Twitter Data Analysis Study
Using Tweets to Understand How COVID-19–Related Health Beliefs Are Affected in the Age of Social Media: Twitter Data Analysis Study Open
Background The emergence of SARS-CoV-2 (ie, COVID-19) has given rise to a global pandemic affecting 215 countries and over 40 million people as of October 2020. Meanwhile, we are also experiencing an infodemic induced by the overabundance …
View article: Using Tweets to Understand How COVID-19–Related Health Beliefs Are Affected in the Age of Social Media: Twitter Data Analysis Study (Preprint)
Using Tweets to Understand How COVID-19–Related Health Beliefs Are Affected in the Age of Social Media: Twitter Data Analysis Study (Preprint) Open
BACKGROUND The emergence of SARS-CoV-2 (ie, COVID-19) has given rise to a global pandemic affecting 215 countries and over 40 million people as of October 2020. Meanwhile, we are also experiencing an infodemic induced by the overabundance…
View article: A Comparison of Pre-trained Vision-and-Language Models for Multimodal Representation Learning across Medical Images and Reports
A Comparison of Pre-trained Vision-and-Language Models for Multimodal Representation Learning across Medical Images and Reports Open
Joint image-text embedding extracted from medical images and associated contextual reports is the bedrock for most biomedical vision-and-language (V+L) tasks, including medical visual question answering, clinical image-text retrieval, clin…
View article: Learning Multimorbidity Patterns from Electronic Health Records Using\n Non-negative Matrix Factorisation
Learning Multimorbidity Patterns from Electronic Health Records Using\n Non-negative Matrix Factorisation Open
Multimorbidity, or the presence of several medical conditions in the same\nindividual, has been increasing in the population, both in absolute and\nrelative terms. However, multimorbidity remains poorly understood, and the\nevidence from e…
View article: Performance Measurement for Deep Bayesian Neural Network
Performance Measurement for Deep Bayesian Neural Network Open
Deep Bayesian neural network has aroused a great attention in recent years since it combines the benefits of deep neural network and probability theory. Because of this, the network can make predictions and quantify the uncertainty of the …
View article: Using Machine Learning to Integrate Socio-Behavioral Factors in Predicting Cardiovascular-Related Mortality Risk
Using Machine Learning to Integrate Socio-Behavioral Factors in Predicting Cardiovascular-Related Mortality Risk Open
Cardiovascular disease is prevalent and associated with significant mortality rate. Robust lifetime risk stratification for cardiovascular disease is important for effective prevention, early diagnoses, targeted intervention, and improved …
View article: Early Prediction of Acute Kidney Injury in Critical Care Setting Using Clinical Notes
Early Prediction of Acute Kidney Injury in Critical Care Setting Using Clinical Notes Open
Acute kidney injury (AKI) in critically ill patients is associated with significant morbidity and mortality. Development of novel methods to identify patients with AKI earlier will allow for testing of novel strategies to prevent or reduce…