Search Results for author:
Found 15 papers, 3 papers with code
EchoGen: Generating Conclusions from Echocardiogram Notes
Generating a summary from findings has been recently explored (Zhang et al., 2018, 2020) in note types such as radiology reports that typically have short length.
HealthPrompt: A Zero-shot Learning Paradigm for Clinical Natural Language Processing
We developed a novel prompt-based clinical NLP framework called HealthPrompt and applied the paradigm of prompt-based learning on clinical texts.
Extraction of Sleep Information from Clinical Notes of Alzheimer's Disease Patients Using Natural Language Processing
In this study, we developed a rule-based NLP algorithm and machine learning models to automate the extraction of sleep-related concepts, including snoring, napping, sleep problem, bad sleep quality, daytime sleepiness, night wakings, and sleep duration, from the clinical notes of patients diagnosed with AD.
Neural Language Models with Distant Supervision to Identify Major Depressive Disorder from Clinical Notes
The experimental results indicate that our proposed approach is effective in identifying MDD phenotypes and that the Bio- Clinical BERT, a specific BERT model for clinical data, achieved the best performance in comparison with conventional machine learning models.
Social and behavioral determinants of health in the era of artificial intelligence with electronic health records: A scoping review
Keywords: Social and Behavioral Determinants of Health, Artificial Intelligence, Electronic Health Records, Natural Language Processing, Predictive Model
Extracting Lifestyle Factors for Alzheimer's Disease from Clinical Notes Using Deep Learning with Weak Supervision
We performed two case studies: physical activity and excessive diet, in order to validate the effectiveness of BERT models in classifying lifestyle factors for AD.
A Qualitative Evaluation of Language Models on Automatic Question-Answering for COVID-19
However, such models are rarely applied and evaluated in the healthcare domain, to meet the information needs with accurate and up-to-date healthcare data.
Clinical Concept Extraction: a Methodology Review
Background Concept extraction, a subdomain of natural language processing (NLP) with a focus on extracting concepts of interest, has been adopted to computationally extract clinical information from text for a wide range of applications ranging from clinical decision support to care quality improvement.
How Good is Artificial Intelligence at Automatically Answering Consumer Questions Related to Alzheimer's Disease?
The total lifetime cost of care for someone with dementia is estimated to be $350, 174 in 2018, 70% of which is associated with family-provided care.
Applications of Natural Language Processing in Clinical Research and Practice
Through this tutorial, we would like to introduce NLP methodologies and tools developed in the clinical domain, and showcase the real-world NLP applications in clinical research and practice at Mayo Clinic (the No.
Unsupervised Machine Learning for the Discovery of Latent Disease Clusters and Patient Subgroups Using Electronic Health Records
Both unsupervised machine learning approaches could be leveraged to discover patient subgroups using EHRs but with different foci.
MedSTS: A Resource for Clinical Semantic Textual Similarity
A subset of MedSTS (MedSTS_ann) containing 1, 068 sentence pairs was annotated by two medical experts with semantic similarity scores of 0-5 (low to high similarity).
A Comparison of Word Embeddings for the Biomedical Natural Language Processing
First, the word embeddings trained on clinical notes and biomedical publications can capture the semantics of medical terms better, and find more relevant similar medical terms, and are closer to human experts' judgments, compared to these trained on Wikipedia and news.
Information Retrieval
Stock price direction prediction by directly using prices data: an empirical study on the KOSPI and HSI
In this paper, we propose a method that directly uses prices data to predict market index direction and stock price direction.
