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Found 10 papers, 2 papers with code
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.
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 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.
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.
