Search Results for author:
Found 34 papers, 5 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.
Emerging Opportunities of Using Large Language Models for Translation Between Drug Molecules and Indications
In this paper, we first propose a new task, which is the translation between drug molecules and corresponding indications, and then test existing LLMs on this new task.
Assertion Detection Large Language Model In-context Learning LoRA Fine-tuning
To further assess the generalizability of our approach, we extended our evaluation to a local dataset that focused on sleep concept extraction.
Enhancing Large Language Models for Clinical Decision Support by Incorporating Clinical Practice Guidelines
Results All four LLMs exhibit improved performance when enhanced with CPGs compared to the baseline ZSP.
TrojFSP: Trojan Insertion in Few-shot Prompt Tuning
However, the security issues, e. g., Trojan attacks, of prompt tuning on a few data samples are not well-studied.
Distilling Large Language Models for Matching Patients to Clinical Trials
To address these issues, this study presents the first systematic examination of the efficacy of both proprietary (GPT-3. 5, and GPT-4) and open-source LLMs (LLAMA 7B, 13B, and 70B) for the task of patient-trial matching.
In-Context Learning Functions with Varying Number of Minima
In our study, we use a formal framework to explore ICL and propose a new task of approximating functions with varying number of minima.
Leveraging Generative AI for Clinical Evidence Summarization Needs to Ensure Trustworthiness
Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence.
Foundation Metrics for Evaluating Effectiveness of Healthcare Conversations Powered by Generative AI
The purpose of this paper is to explore state-of-the-art LLM-based evaluation metrics that are specifically applicable to the assessment of interactive conversational models in healthcare.
An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing
To the best of our knowledge, this is one of the first works on the empirical evaluation of different prompt engineering approaches for clinical NLP in this era of generative AI, and we hope that it will inspire and inform future research in this area.
Large Language Models Vote: Prompting for Rare Disease Identification
This method achieves improved results to any one model in the ensemble on one-shot rare disease identification and classification tasks.
From Military to Healthcare: Adopting and Expanding Ethical Principles for Generative Artificial Intelligence
In 2020, the U. S. Department of Defense officially disclosed a set of ethical principles to guide the use of Artificial Intelligence (AI) technologies on future battlefields.
Fair Patient Model: Mitigating Bias in the Patient Representation Learned from the Electronic Health Records
Methods: We defined a new loss function, called weighted loss function, in the deep representation learning model to balance the importance of different groups of patients and features.
Less Likely Brainstorming: Using Language Models to Generate Alternative Hypotheses
To tackle this problem, we propose a controlled text generation method that uses a novel contrastive learning strategy to encourage models to differentiate between generating likely and less likely outputs according to humans.
ReDWINE: A Clinical Datamart with Text Analytical Capabilities to Facilitate Rehabilitation Research
Using this comprehensive representation of patient data in ReDWINE for rehabilitation research will facilitate real-world evidence for health interventions and outcomes.
Mining Clinical Notes for Physical Rehabilitation Exercise Information: Natural Language Processing Algorithm Development and Validation Study
Objective: This study aims to develop and evaluate a variety of NLP algorithms to extract and categorize physical rehabilitation exercise information from the clinical notes of post-stroke patients treated at the University of Pittsburgh Medical Center.
Toward Improving Health Literacy in Patient Education Materials with Neural Machine Translation Models
We also verified the effectiveness of NMT models in translating health illiterate languages by comparing the ratio of health illiterate language in the sentence.
Automated Fidelity Assessment for Strategy Training in Inpatient Rehabilitation using Natural Language Processing
Although the fidelity assessment for detecting guided and directed verbal cues is valid and feasible for single-site studies, it can become labor intensive, time consuming, and expensive in large, multi-site pragmatic trials.
Few-Shot Learning for Clinical Natural Language Processing Using Siamese Neural Networks
Working with smaller annotated datasets is typical in clinical NLP and therefore, ensuring that deep learning models perform well is crucial for the models to be used in real-world applications.
Graph-Augmented Cyclic Learning Framework for Similarity Estimation of Medical Clinical Notes
Semantic textual similarity (STS) in the clinical domain helps improve diagnostic efficiency and produce concise texts for downstream data mining tasks.
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 Patients with Alzheimer's Disease Using Natural Language Processing
The results show that the rule-based NLP algorithm consistently achieved the best performance for all sleep concepts.
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.
