Search Results for author:
Found 71 papers, 34 papers with code
TriSum: Learning Summarization Ability from Large Language Models with Structured Rationale
To overcome this, we introduce TriSum, a framework for distilling LLMs' text summarization abilities into a compact, local model.
Recent Advances in Predictive Modeling with Electronic Health Records
This survey systematically reviews recent advances in deep learning-based predictive models using EHR data.
PILOT: Legal Case Outcome Prediction with Case Law
We demonstrate the importance of accurately identifying precedent cases and mitigating the temporal shift when making predictions for case law, as our method shows a significant improvement over the prior methods that focus on civil law case outcome predictions.
ConSequence: Synthesizing Logically Constrained Sequences for Electronic Health Record Generation
Generative models can produce synthetic patient records for analytical tasks when real data is unavailable or limited.
Zero-Resource Hallucination Prevention for Large Language Models
Existing techniques for hallucination detection in language assistants rely on intricate fuzzy, specific free-language-based chain of thought (CoT) techniques or parameter-based methods that suffer from interpretability issues.
TREEMENT: Interpretable Patient-Trial Matching via Personalized Dynamic Tree-Based Memory Network
In recent years, machine learning models have been proposed for speeding up patient recruitment via automatically matching patients with clinical trials based on longitudinal patient electronic health records (EHR) data and eligibility criteria of clinical trials.
PyTrial: Machine Learning Software and Benchmark for Clinical Trial Applications
The code is available at https://github. com/RyanWangZf/PyTrial.
Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations
In this paper, we propose a novel method called GODE, which takes into account the two-level structure of individual molecules.
FRAMM: Fair Ranking with Missing Modalities for Clinical Trial Site Selection
This paper focuses on the trial site selection task and proposes FRAMM, a deep reinforcement learning framework for fair trial site selection.
GraphCare: Enhancing Healthcare Predictions with Personalized Knowledge Graphs
This is because personalized predictions require personalized knowledge graphs (KGs), which are difficult to generate from patient EHR data.
MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinement
Tabular data prediction has been employed in medical applications such as patient health risk prediction.
AutoTrial: Prompting Language Models for Clinical Trial Design
Clinical trials are critical for drug development.
SPOT: Sequential Predictive Modeling of Clinical Trial Outcome with Meta-Learning
Accurate trial outcome prediction based on historical trial data promises better trial investment decisions and more trial success.
Synthesize High-dimensional Longitudinal Electronic Health Records via Hierarchical Autoregressive Language Model
In this paper, we propose Hierarchical Autoregressive Language mOdel (HALO) for generating longitudinal high-dimensional EHR, which preserve the statistical properties of real EHR and can be used to train accurate ML models without privacy concerns.
Fast Online Value-Maximizing Prediction Sets with Conformal Cost Control
We focus on a typical scenario where such requirements, separately encoding $\textit{value}$ and $\textit{cost}$, compete with each other.
Clinical trial site matching with improved diversity using fair policy learning
More recently, the issue of diversity and inclusion in clinical trials is gaining importance.
AutoMap: Automatic Medical Code Mapping for Clinical Prediction Model Deployment
To tackle this problem, we propose AutoMap to automatically map the medical codes across different EHR systems in a coarse-to-fine manner: (1) Ontology-level Alignment: We leverage the ontology structure to learn a coarse alignment between the source and target medical coding systems; (2) Code-level Refinement: We refine the alignment at a fine-grained code level for the downstream tasks using a teacher-student framework.
MedAttacker: Exploring Black-Box Adversarial Attacks on Risk Prediction Models in Healthcare
Deep neural networks (DNNs) have been broadly adopted in health risk prediction to provide healthcare diagnoses and treatments.
Differentiable Scaffolding Tree for Molecule Optimization
The structural design of functional molecules, also called molecular optimization, is an essential chemical science and engineering task with important applications, such as drug discovery.
Differentiable Scaffolding Tree for Molecular Optimization
The structural design of functional molecules, also called molecular optimization, is an essential chemical science and engineering task with important applications, such as drug discovery.
ATD: Augmenting CP Tensor Decomposition by Self Supervision
This paper addresses the above challenges by proposing augmented tensor decomposition (ATD), which effectively incorporates data augmentations and self-supervised learning (SSL) to boost downstream classification.
MTC: Multiresolution Tensor Completion from Partial and Coarse Observations
Our MTC model explores tensor mode properties and leverages the hierarchy of resolutions to recursively initialize an optimization setup, and optimizes on the coupled system using alternating least squares.
Multi-version Tensor Completion for Time-delayed Spatio-temporal Data
Recovering such missing or noisy (under-reported) elements of the input tensor can be viewed as a generalized tensor completion problem.
Change Matters: Medication Change Prediction with Recurrent Residual Networks
Deep learning is revolutionizing predictive healthcare, including recommending medications to patients with complex health conditions.
SafeDrug: Dual Molecular Graph Encoders for Recommending Effective and Safe Drug Combinations
On a benchmark dataset, our SafeDrug is relatively shown to reduce DDI by 19. 43% and improves 2. 88% on Jaccard similarity between recommended and actually prescribed drug combinations over previous approaches.
Machine Learning Applications for Therapeutic Tasks with Genomics Data
Thanks to the increasing availability of genomics and other biomedical data, many machine learning approaches have been proposed for a wide range of therapeutic discovery and development tasks.
SCRIB: Set-classifier with Class-specific Risk Bounds for Blackbox Models
Despite deep learning (DL) success in classification problems, DL classifiers do not provide a sound mechanism to decide when to refrain from predicting.
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development
Here, we introduce Therapeutics Data Commons (TDC), the first unifying platform to systematically access and evaluate machine learning across the entire range of therapeutics.
HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data
Next, these embeddings will be fed into the knowledge embedding module to generate knowledge embeddings that are pretrained using external knowledge on pharmaco-kinetic properties and trial risk from the web.
PyHealth: A Python Library for Health Predictive Models
PyHealth consists of data preprocessing module, predictive modeling module, and evaluation module.
STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization
Accurate prediction of the transmission of epidemic diseases such as COVID-19 is crucial for implementing effective mitigation measures.
Benchmarking Automated Clinical Language Simplification: Dataset, Algorithm, and Evaluation
To fairly evaluate the performance, we also propose three specific evaluation metrics.
FLANNEL: Focal Loss Based Neural Network Ensemble for COVID-19 Detection
To test the possibility of differentiating chest x-ray images of COVID-19 against other pneumonia and healthy patients using deep neural networks.
DeepRite: Deep Recurrent Inverse TreatmEnt Weighting for Adjusting Time-varying Confounding in Modern Longitudinal Observational Data
How to model such time-varying effects from longitudinal observational data?
UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data
However, existing uncertainty estimation approaches often failed in handling high-dimensional data, which are present in multi-sourced data.
MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization
Molecule optimization is a fundamental task for accelerating drug discovery, with the goal of generating new valid molecules that maximize multiple drug properties while maintaining similarity to the input molecule.
MolDesigner: Interactive Design of Efficacious Drugs with Deep Learning
The efficacy of a drug depends on its binding affinity to the therapeutic target and pharmacokinetics.
SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization
Furthermore, most previous works focus on binary DDI prediction whereas the multi-typed DDI pharmacological effect prediction is a more meaningful but harder task.
COMPOSE: Cross-Modal Pseudo-Siamese Network for Patient Trial Matching
The other path processes EHR with multi-granularity memory network that encodes structured patient records into multiple levels based on medical ontology.
Fast Graph Attention Networks Using Effective Resistance Based Graph Sparsification
The attention mechanism has demonstrated superior performance for inference over nodes in graph neural networks (GNNs), however, they result in a high computational burden during both training and inference.
CHEER: Rich Model Helps Poor Model via Knowledge Infusion
There is a growing interest in applying deep learning (DL) to healthcare, driven by the availability of data with multiple feature channels in rich-data environments (e. g., intensive care units).
SkipGNN: Predicting Molecular Interactions with Skip-Graph Networks
Here, we present SkipGNN, a graph neural network approach for the prediction of molecular interactions.
MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction
Drug target interaction (DTI) prediction is a foundational task for in silico drug discovery, which is costly and time-consuming due to the need of experimental search over large drug compound space.
DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction
Accurate prediction of drug-target interactions (DTI) is crucial for drug discovery.
Ranked #2 on
Drug Discovery
on KIBA
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection
Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples with numerous high-stake applications including fraud detection and intrusion detection.
CLARA: Clinical Report Auto-completion
Most existing methods try to generate the whole reports from the raw input with limited success because 1) generated reports often contain errors that need manual review and correction, 2) it does not save time when doctors want to write additional information into the report, and 3) the generated reports are not customized based on individual doctors' preference.
REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild
By deploying these models to an Android application on a smartphone, we quantitatively observe that REST allows models to achieve up to 17x energy reduction and 9x faster inference.
StageNet: Stage-Aware Neural Networks for Health Risk Prediction
Compared to the best baseline model, StageNet achieves up to 12% higher AUPRC for risk prediction task on two real-world patient datasets.
DeepEnroll: Patient-Trial Matching with Deep Embedding and Entailment Prediction
To address these challenges, we proposed DeepEnroll, a cross-modal inference learning model to jointly encode enrollment criteria (text) and patients records (tabular data) into a shared latent space for matching inference.
Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review
Methods:We extracted papers that applied deep learning (deep neural network) models to ECG data that were published between Jan. 1st of 2010 and Feb. 29th of 2020 from Google Scholar, PubMed, and the DBLP.
CONAN: Complementary Pattern Augmentation for Rare Disease Detection
How to further leverage patients with possibly uncertain diagnosis to improve detection?
Doctor2Vec: Dynamic Doctor Representation Learning for Clinical Trial Recruitment
We propose doctor2vec which simultaneously learns 1) doctor representations from EHR data and 2) trial representations from the description and categorical information about the trials.
CORE: Automatic Molecule Optimization Using Copy & Refine Strategy
The state-of-the-art approaches partition the molecules into a large set of substructures $S$ and grow the new molecule structure by iteratively predicting which substructure from $S$ to add.
CUP: Cluster Pruning for Compressing Deep Neural Networks
With CUP, we overcome two limitations of prior work-(1) non-uniform pruning: CUP can efficiently determine the ideal number of filters to prune in each layer of a neural network.
CASTER: Predicting Drug Interactions with Chemical Substructure Representation
Adverse drug-drug interactions (DDIs) remain a leading cause of morbidity and mortality.
SLEEPER: interpretable Sleep staging via Prototypes from Expert Rules
In this study, we propose Sleep staging via Prototypes from Expert Rules (SLEEPER), which combines deep learning models with expert defined rules using a prototype learning framework to generate simple interpretable models.
Ranked #1 on
Automatic Sleep Stage Classification
on ISRUC-Sleep
GENN: Predicting Correlated Drug-drug Interactions with Graph Energy Neural Networks
We propose the graph energy neural network (GENN) to explicitly model link type correlations.
Rare Disease Detection by Sequence Modeling with Generative Adversarial Networks
Rare diseases affecting 350 million individuals are commonly associated with delay in diagnosis or misdiagnosis.
Predicting Treatment Initiation from Clinical Time Series Data via Graph-Augmented Time-Sensitive Model
Many computational models were proposed to extract temporal patterns from clinical time series for each patient and among patient group for predictive healthcare.
Pre-training of Graph Augmented Transformers for Medication Recommendation
G-BERT is the first to bring the language model pre-training schema into the healthcare domain and it achieved state-of-the-art performance on the medication recommendation task.
MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals
Electrocardiography (ECG) signals are commonly used to diagnose various cardiac abnormalities.
CGNF: Conditional Graph Neural Fields
By integrating the conditional random fields (CRF) in the graph convolutional networks, we explicitly model a joint probability of the entire set of node labels, thus taking advantage of neighborhood label information in the node label prediction task.
MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare
Deep learning models exhibit state-of-the-art performance for many predictive healthcare tasks using electronic health records (EHR) data, but these models typically require training data volume that exceeds the capacity of most healthcare systems.
AWE: Asymmetric Word Embedding for Textual Entailment
It refers to the directional relation between text fragments such that the "premise" can infer "hypothesis".
Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders
We focus on the matrix representation of graphs and formulate penalty terms that regularize the output distribution of the decoder to encourage the satisfaction of validity constraints.
GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination
Recent progress in deep learning is revolutionizing the healthcare domain including providing solutions to medication recommendations, especially recommending medication combination for patients with complex health conditions.
RDPD: Rich Data Helps Poor Data via Imitation
In many situations, we need to build and deploy separate models in related environments with different data qualities.
Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders
In this paper, we propose to learn accurate and interpretable similarity measures from multiple types of drug features.
FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling
The graph convolutional networks (GCN) recently proposed by Kipf and Welling are an effective graph model for semi-supervised learning.
Ranked #3 on
Node Classification
on Citeseer Full-supervised
Patient Subtyping via Time-Aware LSTM Networks
We propose a patient subtyping model that leverages the proposed T-LSTM in an auto-encoder to learn a powerful single representation for sequential records of patients, which are then used to cluster patients into clinical subtypes.
Ranked #4 on
Multivariate Time Series Forecasting
on USHCN-Daily
