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Found 14 papers, 4 papers with code
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.
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.
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.
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.
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.
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.
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.
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.
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.
Leveraging a Joint of Phenotypic and Genetic Features on Cancer Patient Subgrouping
Cancer is responsible for millions of deaths worldwide every year.
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
Comparisons of Graph Neural Networks on Cancer Classification Leveraging a Joint of Phenotypic and Genetic Features
We applied and compared eight GNN models including AGNN, ChebNet, GAT, GCN, GIN, GraphSAGE, SGC, and TAGCN on the Mayo Clinic cancer disease dataset and assessedtheir performance as well as compared them with each other and with more conventional machinelearning models such as decision tree, gradient boosting, multi-layer perceptron, naive bayes, andrandom forest which we used as the baselines.
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.
