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Found 67 papers, 23 papers with code
A Study on Expert Sourcing Enterprise Question Collection and Classification
In this paper, we present a study on the characteristics and classification of IBM sales questions.
Parametric Prediction from Parametric Agents
In order to elicit heterogeneous agents' private information and incentivize agents with different capabilities to act in the principal's best interest, we design an optimal joint incentive mechanism and prediction algorithm called COPE (COst and Prediction Elicitation), the analysis of which offers several valuable engineering insights.
Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease
Parkinson's Disease (PD) is one of the most prevalent neurodegenerative diseases that affects tens of millions of Americans.
Natural Language Processing for EHR-Based Computational Phenotyping
This article reviews recent advances in applying natural language processing (NLP) to Electronic Health Records (EHRs) for computational phenotyping.
Using Clinical Narratives and Structured Data to Identify Distant Recurrences in Breast Cancer
Our model can accurately and efficiently identify distant recurrences in breast cancer by combining features extracted from unstructured clinical narratives and structured clinical data.
Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks
Clinical text classification is an important problem in medical natural language processing.
Ranked #2 on
Clinical Note Phenotyping
on I2B2 2008: Obesity
Developing a Portable Natural Language Processing Based Phenotyping System
Our system facilitates portable phenotyping of obesity and its 15 comorbidities based on the unstructured patient discharge summaries, while achieving a performance that often ranked among the top 10 of the challenge participants.
Graph Convolutional Networks for Text Classification
We build a single text graph for a corpus based on word co-occurrence and document word relations, then learn a Text Graph Convolutional Network (Text GCN) for the corpus.
Ranked #6 on
Text Classification
on Ohsumed
Distribution Networks for Open Set Learning
In this paper, we recognize that novel classes should be different from each other, and propose distribution networks for open set learning that can model different novel classes based on probability distributions.
Deep Generative Classifiers for Thoracic Disease Diagnosis with Chest X-ray Images
However, most of the previous deep neural network classifiers were based on deterministic architectures which are usually very noise-sensitive and are likely to aggravate the overfitting issue.
Supervised Nonnegative Matrix Factorization to Predict ICU Mortality Risk
We used the simulation data to verify the effectiveness of this method, and then we applied it to ICU mortality risk prediction and demonstrated its superiority over other conventional supervised NMF methods.
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.
Characterizing Design Patterns of EHR-Driven Phenotype Extraction Algorithms
Good classification accuracy with simple features demonstrated the attribution coherence and the feasibility of automatic identification of design patterns.
Implementing a Portable Clinical NLP System with a Common Data Model - a Lisp Perspective
We also developed a utility to convert an inline annotation format to stand-off annotations to enable the reuse of clinical text datasets with inline annotations.
MedGCN: Medication recommendation and lab test imputation via graph convolutional networks
In this study, we construct a graph to associate 4 types of medical entities, i. e., patients, encounters, lab tests, and medications, and applied a graph neural network to learn node embeddings for medication recommendation and lab test imputation.
ImageGCN: Multi-Relational Image Graph Convolutional Networks for Disease Identification with Chest X-rays
However, most of the existing approaches for image representation ignore the relations between images and consider each input image independently.
Evaluating the Portability of an NLP System for Processing Echocardiograms: A Retrospective, Multi-site Observational Study
While the NLP system showed high precision and recall measurements for four target concepts (aortic valve regurgitation, left atrium size at end systole, mitral valve regurgitation, tricuspid valve regurgitation) across all sites, we found moderate or poor results for the remaining concepts and the NLP system performance varied between individual sites.
Identifying Sub-Phenotypes of Acute Kidney Injury using Structured and Unstructured Electronic Health Record Data with Memory Networks
Sub-phenotype III is with average age 65. 07$ \pm 11. 32 $ years, and was characterized moderate loss of kidney excretory function and thus more likely to develop stage II AKI (SCr $1. 69\pm 0. 32$ mg/dL, eGFR $93. 97\pm 56. 53$ mL/min/1. 73$m^2$).
Mixture-based Multiple Imputation Model for Clinical Data with a Temporal Dimension
The problem of missing values in multivariable time series is a key challenge in many applications such as clinical data mining.
Symmetric Cross Entropy for Robust Learning with Noisy Labels
In this paper, we show that DNN learning with Cross Entropy (CE) exhibits overfitting to noisy labels on some classes ("easy" classes), but more surprisingly, it also suffers from significant under learning on some other classes ("hard" classes).
Ranked #43 on
Image Classification
on Clothing1M
KG-BERT: BERT for Knowledge Graph Completion
Knowledge graphs are important resources for many artificial intelligence tasks but often suffer from incompleteness.
Ranked #5 on
Link Prediction
on UMLS
Mixed Pooling Multi-View Attention Autoencoder for Representation Learning in Healthcare
Distributed representations have been used to support downstream tasks in healthcare recently.
Hierarchical Semantic Correspondence Learning for Post-Discharge Patient Mortality Prediction
Predicting patient mortality is an important and challenging problem in the healthcare domain, especially for intensive care unit (ICU) patients.
Dirichlet Latent Variable Hierarchical Recurrent Encoder-Decoder in Dialogue Generation
Based on which, we further find that there is redundancy among the dimensions of latent variable, and the lengths and sentence patterns of the responses can be strongly correlated to each dimension of the latent variable.
Conditional Hierarchical Bayesian Tucker Decomposition for Genetic Data Analysis
We apply these models to examine patients with one of four common types of cancer (breast, lung, prostate, and colorectal) and siblings with and without autism spectrum disorder.
Med2Meta: Learning Representations of Medical Concepts with Meta-Embeddings
Distributed representations of medical concepts have been used to support downstream clinical tasks recently.
Open-Set Recognition with Gaussian Mixture Variational Autoencoders
In inference, open-set classification is to either classify a sample into a known class from training or reject it as an unknown class.
Decentralized Source Localization without Sensor Parameters in Wireless Sensor Networks
However, we propose two methods to estimate the source location in this paper under the fault model: hitting set approach and feature selection method, which only utilize the noisy data set at the fusion center for estimation of the source location without knowing the sensor parameters.
A Comparison of Pre-trained Vision-and-Language Models for Multimodal Representation Learning across Medical Images and Reports
Joint image-text embedding extracted from medical images and associated contextual reports is the bedrock for most biomedical vision-and-language (V+L) tasks, including medical visual question answering, clinical image-text retrieval, clinical report auto-generation.
Towards Expressive Graph Representation
Graph Neural Network (GNN) aggregates the neighborhood of each node into the node embedding and shows its powerful capability for graph representation learning.
Improving Medical NLI Using Context-Aware Domain Knowledge
Domain knowledge is important to understand both the lexical and relational associations of words in natural language text, especially for domain-specific tasks like Natural Language Inference (NLI) in the medical domain, where due to the lack of a large annotated dataset such knowledge cannot be implicitly learned during training.
PANTHER: Pathway Augmented Nonnegative Tensor factorization for HighER-order feature learning
We apply genetically motivated constrained tensor factorization to group pathways in a way that reflects molecular mechanism interactions.
Non-Convex Optimization with Spectral Radius Regularization
We develop a regularization method which finds flat minima during the training of deep neural networks and other machine learning models.
Aggregation Delayed Federated Learning
Federated learning is a distributed machine learning paradigm where multiple data owners (clients) collaboratively train one machine learning model while keeping data on their own devices.
Towards Understanding Catastrophic Overfitting in Fast Adversarial Training
After adversarial training was proposed, a series of works focus on improving the compunational efficiency of adversarial training for deep neural networks (DNNs).
Dissecting Local Properties of Adversarial Examples
Adversarial examples have attracted significant attention over the years, yet a sufficient understanding is in lack, especially when analyzing their performances in combination with adversarial training.
Unsupervised Learning to Subphenotype Delirium Patients from Electronic Health Records
Delirium is a common acute onset brain dysfunction in the emergency setting and is associated with higher mortality.
Early Prediction of Mortality in Critical Care Setting in Sepsis Patients Using Structured Features and Unstructured Clinical Notes
We built and applied several machine learning models to predict the risk of hospital mortality and 30-day mortality in sepsis patients.
Assessing Social Determinants-Related Performance Bias of Machine Learning Models: A case of Hyperchloremia Prediction in ICU Population
Machine learning in medicine leverages the wealth of healthcare data to extract knowledge, facilitate clinical decision-making, and ultimately improve care delivery.
SNPs Filtered by Allele Frequency Improve the Prediction of Hypertension Subtypes
Hypertension is the leading global cause of cardiovascular disease and premature death.
Development and Validation of MicrobEx: an Open-Source Package for Microbiology Culture Concept Extraction
Our concept extraction Python package, MicrobEx, is designed to be reused and adapted to individual institutions as an upstream process for other clinical applications, such as machine learning studies, clinical decision support, and disease surveillance systems.
Disparities in Social Determinants among Performances of Mortality Prediction with Machine Learning for Sepsis Patients
On the 5, 783 sepsis patients identified by the Sepsis-3 criteria statistically significant performance decreases for mortality prediction were observed when applying the trained machine learning model on Asian and Hispanic patients.
Open-Set Recognition of Breast Cancer Treatments
Open-set recognition generalizes a classification task by classifying test samples as one of the known classes from training or "unknown."
Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences
To overcome this, long sequence transformer models, e. g. Longformer and BigBird, were proposed with the idea of sparse attention mechanism to reduce the memory usage from quadratic to the sequence length to a linear scale.
Topology-Preserving Dimensionality Reduction via Interleaving Optimization
Dimensionality reduction techniques are powerful tools for data preprocessing and visualization which typically come with few guarantees concerning the topological correctness of an embedding.
Distribution Preserving Graph Representation Learning
Besides the generalizability, by applying an expressive GNN backbone, DP-GNN can also have high expressive power.
Machine Learning Applications in Lung Cancer Diagnosis, Treatment and Prognosis
The recent development of imaging and sequencing technologies enables systematic advances in the clinical study of lung cancer.
Multimodal Machine Learning in Precision Health
Attempts to improve prediction and resemble the multimodal nature of clinical expert decision-making this has been met in the computational field of machine learning by a fusion of disparate data.
AKI-BERT: a Pre-trained Clinical Language Model for Early Prediction of Acute Kidney Injury
However, few have explored BERT on disease-specific medical domain tasks such as AKI early prediction.
Deep Learning Reveals Patterns of Diverse and Changing Sentiments Towards COVID-19 Vaccines Based on 11 Million Tweets
We analyzed COVID-19 vaccine-related tweets to understand the evolving perceptions of COVID-19 vaccines.
SHINE: SubHypergraph Inductive Neural nEtwork
Hypergraph neural networks can model multi-way connections among nodes of the graphs, which are common in real-world applications such as genetic medicine.
AD-BERT: Using Pre-trained contextualized embeddings to Predict the Progression from Mild Cognitive Impairment to Alzheimer's Disease
Materials and Methods: We identified 3657 patients diagnosed with MCI together with their progress notes from Northwestern Medicine Enterprise Data Warehouse (NMEDW) between 2000-2020.
Metabolomics of Aging and Alzheimer's Disease: From Single-Omics to Multi-Omics
Aging is a multifactorial process and a key factor of morbidity and mortality.
A Comparative Study of Pretrained Language Models for Long Clinical Text
Objective: Clinical knowledge enriched transformer models (e. g., ClinicalBERT) have state-of-the-art results on clinical NLP (natural language processing) tasks.
Using Machine Learning to Develop Smart Reflex Testing Protocols
Here, using the analyte ferritin as an example, we propose an alternative machine learning-based approach to "smart" reflex testing with a wider scope and greater impact than traditional rule-based approaches.
IRTCI: Item Response Theory for Categorical Imputation
Results demonstrated that the new method, Item Response Theory for Categorical Imputation (IRTCI), fared quite well compared to currently used methods, outperforming several of them in many conditions.
Deep Reinforcement Learning for Cost-Effective Medical Diagnosis
In this work, we use reinforcement learning (RL) to find a dynamic policy that selects lab test panels sequentially based on previous observations, ensuring accurate testing at a low cost.
Medical Image Deidentification, Cleaning and Compression Using Pylogik
This methodology de-identifies the images, reduces file sizes, and prepares image volumes for applications in deep learning and data sharing.
Exploring Large Language Models for Knowledge Graph Completion
Knowledge graphs play a vital role in numerous artificial intelligence tasks, yet they frequently face the issue of incompleteness.
Deep Reinforcement Learning for Efficient and Fair Allocation of Health Care Resources
Scarcity of health care resources could result in the unavoidable consequence of rationing.
Enhancing Health Data Interoperability with Large Language Models: A FHIR Study
In this study, we investigated the ability of the large language model (LLM) to enhance healthcare data interoperability.
Rethinking Domain Generalization: Discriminability and Generalizability
On the one hand, the simultaneous attainment of generalizability and discriminability of features presents a complex challenge, often entailing inherent contradictions.
Diverse Target and Contribution Scheduling for Domain Generalization
In specific, DTS employs distinct soft labels as training targets to account for various feature distributions across domains and thereby mitigates the gradient conflicts, and DCB dynamically balances the contributions of source domains by ensuring a fair decline in losses of different source domains.
Machine learning's own Industrial Revolution
Machine learning is expected to enable the next Industrial Revolution.
Seeing is not always believing: The Space of Harmless Perturbations
In the context of deep neural networks, we expose the existence of a harmless perturbation space, where perturbations leave the network output entirely unaltered.
DGMamba: Domain Generalization via Generalized State Space Model
SPR strives to encourage the model to concentrate more on objects rather than context, consisting of two designs: Prior-Free Scanning~(PFS), and Domain Context Interchange~(DCI).
Privacy-Preserving UCB Decision Process Verification via zk-SNARKs
With the increasingly widespread application of machine learning, how to strike a balance between protecting the privacy of data and algorithm parameters and ensuring the verifiability of machine learning has always been a challenge.
