Search Results for author:
Found 42 papers, 7 papers with code
GENEVIC: GENetic data Exploration and Visualization via Intelligent interactive Console
Summary: The vast generation of genetic data poses a significant challenge in efficiently uncovering valuable knowledge.
De-identification is not always enough
In this work, we demonstrated that (i) de-identification of real clinical notes does not protect records against a membership inference attack, (ii) proposed a novel approach to generate synthetic clinical notes using the current state-of-the-art large language models, (iii) evaluated the performance of the synthetically generated notes in a clinical domain task, and (iv) proposed a way to mount a membership inference attack where the target model is trained with synthetic data.
Extracting periodontitis diagnosis in clinical notes with RoBERTa and regular expression
This study aimed to utilize text processing and natural language processing (NLP) models to mine clinical notes for the diagnosis of periodontitis and to evaluate the performance of a named entity recognition (NER) model on different regular expression (RE) methods.
Use GPT-J Prompt Generation with RoBERTa for NER Models on Diagnosis Extraction of Periodontal Diagnosis from Electronic Dental Records
This study explored the usability of prompt generation on named entity recognition (NER) tasks and the performance in different settings of the prompt.
FERI: A Multitask-based Fairness Achieving Algorithm with Applications to Fair Organ Transplantation
Liver transplantation often faces fairness challenges across subgroups defined by sensitive attributes like age group, gender, and race/ethnicity.
DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research
The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are used to describe similar research.
Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics
Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks.
Dental CLAIRES: Contrastive LAnguage Image REtrieval Search for Dental Research
Learning about diagnostic features and related clinical information from dental radiographs is important for dental research.
Sensitive Data Detection with High-Throughput Machine Learning Models in Electrical Health Records
Based on this novel finding, we engineered over 30 features from the metadata of the original features and used machine learning to build classification models to automatically identify PHI fields in structured Electronic Health Record (EHR) data.
Predicting multiple sclerosis disease severity with multimodal deep neural networks
Multiple Sclerosis (MS) is a chronic disease developed in human brain and spinal cord, which can cause permanent damage or deterioration of the nerves.
A Transformer-Based Deep Learning Approach for Fairly Predicting Post-Liver Transplant Risk Factors
Specifically, we proposed a deep-learning model to predict multiple risk factors after a liver transplant.
Multi-Task Learning for Post-transplant Cause of Death Analysis: A Case Study on Liver Transplant
Organ transplant is the essential treatment method for some end-stage diseases, such as liver failure.
Improving Large Language Models for Clinical Named Entity Recognition via Prompt Engineering
Results: Using baseline prompts, GPT-3. 5 and GPT-4 achieved relaxed F1 scores of 0. 634, 0. 804 for MTSamples, and 0. 301, 0. 593 for VAERS.
Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint
To tackle these challenges, deep learning frameworks have been created to match patients to trials.
Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching
The process of matching patients with suitable clinical trials is essential for advancing medical research and providing optimal care.
SPeC: A Soft Prompt-Based Calibration on Performance Variability of Large Language Model in Clinical Notes Summarization
Electronic health records (EHRs) store an extensive array of patient information, encompassing medical histories, diagnoses, treatments, and test outcomes.
Does Synthetic Data Generation of LLMs Help Clinical Text Mining?
Our method has resulted in significant improvements in the performance of downstream tasks, improving the F1-score from 23. 37% to 63. 99% for the named entity recognition task and from 75. 86% to 83. 59% for the relation extraction task.
Fairly Predicting Graft Failure in Liver Transplant for Organ Assigning
To tackle this problem, this work proposes a fair machine learning framework targeting graft failure prediction in liver transplant.
Private Semi-supervised Knowledge Transfer for Deep Learning from Noisy Labels
PATE combines an ensemble of "teacher models" trained on sensitive data and transfers the knowledge to a "student" model through the noisy aggregation of teachers' votes for labeling unlabeled public data which the student model will be trained on.
MULTIPAR: Supervised Irregular Tensor Factorization with Multi-task Learning
By supervising the tensor factorization with downstream prediction tasks and leveraging information from multiple related predictive tasks, MULTIPAR can yield not only more meaningful phenotypes but also better predictive performance for downstream tasks.
Scalable Causal Structure Learning: Scoping Review of Traditional and Deep Learning Algorithms and New Opportunities in Biomedicine
Causal structure learning refers to a process of identifying causal structures from observational data, and it can have multiple applications in biomedicine and health care.
Federated Learning Algorithms for Generalized Mixed-effects Model (GLMM) on Horizontally Partitioned Data from Distributed Sources
Objectives: This paper develops two algorithms to achieve federated generalized linear mixed effect models (GLMM), and compares the developed model's outcomes with each other, as well as that from the standard R package (`lme4').
Heterogeneous Treatment Effect Estimation using machine learning for Healthcare application: tutorial and benchmark
We also expect to provide the feasibility of HTE for personalized drug effectiveness.
Use of the Deep Learning Approach to Measure Alveolar Bone Level
Conclusion: The proposed DL model provides reliable RBL measurements and image-based periodontal diagnosis using periapical radiographic images.
A Fast PC Algorithm with Reversed-order Pruning and A Parallelization Strategy
The PC algorithm is the state-of-the-art algorithm for causal structure discovery on observational data.
De-identification of Unstructured Clinical Texts from Sequence to Sequence Perspective
In this work, we propose a novel problem formulation for de-identification of unstructured clinical text.
An Empirical Study of UMLS Concept Extraction from Clinical Notes using Boolean Combination Ensembles
Our objective in this study is to investigate the behavior of Boolean operators on combining annotation output from multiple Natural Language Processing (NLP) systems across multiple corpora and to assess how filtering by aggregation of Unified Medical Language System (UMLS) Metathesaurus concepts affects system performance for Named Entity Recognition (NER) of UMLS concepts.
Relational graph convolutional networks for predicting blood-brain barrier penetration of drug molecules
The RGCN model achieved an overall accuracy of 0. 872, an AUROC of 0. 919 and an AUPRC of 0. 838 for the testing dataset with the drug-protein interactions and the Mordred descriptors as the input.
COVID-19 Smart Chatbot Prototype for Patient Monitoring
Many COVID-19 patients developed prolonged symptoms after the infection, including fatigue, delirium, and headache.
Real-time Prediction for Mechanical Ventilation in COVID-19 Patients using A Multi-task Gaussian Process Multi-objective Self-attention Network
We propose a novel in-time risk trajectory predictive model to handle the irregular sampling rate in the data, which follows the dynamics of risk of performing mechanical ventilation for individual patients.
Deep Representation Learning of Patient Data from Electronic Health Records (EHR): A Systematic Review
We show the importance and feasibility of learning comprehensive representations of patient EHR data through a systematic review.
Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence
In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and rigorous validation can facilitate the rapid identification of candidate drugs for COVID-19 treatment.
Secure and Differentially Private Bayesian Learning on Distributed Data
Data integration and sharing maximally enhance the potential for novel and meaningful discoveries.
Dataset and Enhanced Model for Eligibility Criteria-to-SQL Semantic Parsing
Compared to existing datasets, the queries in the dataset here are derived from the eligibility criteria of clinical trials and include \textit{Order-sensitive, Counting-based, and Boolean-type} cases which are not seen before.
Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis
We propose DPFact, a privacy-preserving collaborative tensor factorization method for computational phenotyping using EHR.
From Brain Imaging to Graph Analysis: a study on ADNI's patient cohort
In this paper, we studied the association between the change of structural brain volumes to the potential development of Alzheimer's disease (AD).
Discriminative Sleep Patterns of Alzheimer's Disease via Tensor Factorization
Sleep change is commonly reported in Alzheimer's disease (AD) patients and their brain wave studies show decrease in dreaming and non-dreaming stages.
Combining Representation Learning with Tensor Factorization for Risk Factor Analysis - an application to Epilepsy and Alzheimer's disease
Existing studies consider Alzheimer's disease (AD) a comorbidity of epilepsy, but also recognize epilepsy to occur more frequently in patients with AD than those without.
Early Prediction of Acute Kidney Injury in Critical Care Setting Using Clinical Notes
We developed data-driven prediction models to estimate the risk of new AKI onset.
Model-Protected Multi-Task Learning
Multi-task learning (MTL) refers to the paradigm of learning multiple related tasks together.
Federated Tensor Factorization for Computational Phenotyping
In this paper, we developed a novel solution to enable federated tensor factorization for computational phenotyping without sharing patient-level data.
SAFETY: Secure gwAs in Federated Environment Through a hYbrid solution with Intel SGX and Homomorphic Encryption
In this article, we present SAFETY, a hybrid framework, which can securely perform GWAS on federated genomic datasets using homomorphic encryption and recently introduced secure hardware component of Intel Software Guard Extensions (Intel SGX) to ensure high efficiency and privacy at the same time.
Cryptography and Security
