Search Results for author:
Found 13 papers, 5 papers with code
Are gene-by-environment interactions leveraged in multi-modality neural networks for breast cancer prediction?
While our model did not demonstrate improved performance over the baseline, we discovered 248 (<1%) statistically significant gene-by-gene and gene-by-environment interactions out of the ~53. 6k possible feature pairs, the most contributory of which included rs6001930 (MKL1) and rs889312 (MAP3K1), with age and menopause being the most heavily interacting non-genetic risk factors.
MedAdapter: Efficient Test-Time Adaptation of Large Language Models towards Medical Reasoning
Faced with the challenges of balancing model performance, computational resources, and data privacy, MedAdapter provides an efficient, privacy-preserving, cost-effective, and transparent solution for adapting LLMs to the biomedical domain.
BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers
Developing effective biomedical retrieval models is important for excelling at knowledge-intensive biomedical tasks but still challenging due to the deficiency of sufficient publicly annotated biomedical data and computational resources.
RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records
We present RAM-EHR, a Retrieval AugMentation pipeline to improve clinical predictions on Electronic Health Records (EHRs).
EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records
Large language models (LLMs) have demonstrated exceptional capabilities in planning and tool utilization as autonomous agents, but few have been developed for medical problem-solving.
Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review
This systematic review examines the use of Explainable Artificial Intelligence (XAI) during the pandemic and how its use could overcome barriers to real-world success.
Public Health Informatics: Proposing Causal Sequence of Death Using Neural Machine Translation
The sequence of clinical codes on the death report is named as causal chain of death, coded in the tenth revision of International Statistical Classification of Diseases (ICD-10); in line with the ICD-9-CM Official Guidelines for Coding and Reporting, the priority-ordered clinical conditions on the discharge record are coded in ICD-9.
Improve Model Generalization and Robustness to Dataset Bias with Bias-regularized Learning and Domain-guided Augmentation
In this study, we investigated model robustness to dataset bias using three large-scale Chest X-ray datasets: first, we assessed the dataset bias using vanilla training baseline; second, we proposed a novel multi-source domain generalization model by (a) designing a new bias-regularized loss function; and (b) synthesizing new data for domain augmentation.
DeepDeath: Learning to Predict the Underlying Cause of Death with Big Data
Multiple cause-of-death data provides a valuable source of information that can be used to enhance health standards by predicting health related trajectories in societies with large populations.
MotifMark: Finding Regulatory Motifs in DNA Sequences
A number of high-throughput technologies have recently emerged that try to quantify the affinity between proteins and DNA motifs.
A Multi-Modal Graph-Based Semi-Supervised Pipeline for Predicting Cancer Survival
Despite the wealth of information available in expression profiles of cancer tumors, fulfilling the aforementioned objective remains a big challenge, for the most part, due to the paucity of data samples compared to the high dimension of the expression profiles.
DeeperBind: Enhancing Prediction of Sequence Specificities of DNA Binding Proteins
To the best of our knowledge, this is the most accurate pipeline that can predict binding specificities of DNA sequences from the data produced by high-throughput technologies through utilization of the power of deep learning for feature generation and positional dynamics modeling.
A Semi-Supervised Method for Predicting Cancer Survival Using Incomplete Clinical Data
The results of applying our method to three cancer datasets show the promise of semi-supervised learning for prediction of cancer survival.
