Search Results for author:
Found 135 papers, 65 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.
Making Pre-trained Language Models Great on Tabular Prediction
Condensing knowledge from diverse domains, language models (LMs) possess the capability to comprehend feature names from various tables, potentially serving as versatile learners in transferring knowledge across distinct tables and diverse prediction tasks, but their discrete text representation space is inherently incompatible with numerical feature values in tables.
SERVAL: Synergy Learning between Vertical Models and LLMs towards Oracle-Level Zero-shot Medical Prediction
Recent development of large language models (LLMs) has exhibited impressive zero-shot proficiency on generic and common sense questions.
AutoRD: An Automatic and End-to-End System for Rare Disease Knowledge Graph Construction Based on Ontologies-enhanced Large Language Models
We quantitatively evaluate our system on entity extraction, relation extraction, and the performance of knowledge graph construction.
GenRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language Models
The field of relation extraction (RE) is experiencing a notable shift towards generative relation extraction (GRE), leveraging the capabilities of large language models (LLMs).
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.
Stoichiometry Representation Learning with Polymorphic Crystal Structures
Despite the recent success of machine learning (ML) in materials science, its success heavily relies on the structural description of crystal, which is itself computationally demanding and occasionally unattainable.
Conformal Drug Property Prediction with Density Estimation under Covariate Shift
Conformal Prediction (CP) is a promising tool for creating such prediction sets for molecular properties with a coverage guarantee.
PromptRE: Weakly-Supervised Document-Level Relation Extraction via Prompting-Based Data Programming
Relation extraction aims to classify the relationships between two entities into pre-defined categories.
Molecular De Novo Design through Transformer-based Reinforcement Learning
In contrast to the traditional RNN-based models, our proposed method exhibits superior performance in generating compounds predicted to be active against various biological targets, capturing long-term dependencies in the molecular structure sequence.
UniPredict: Large Language Models are Universal Tabular Classifiers
Specifically, we train a single LLM on an aggregation of 169 tabular datasets with diverse targets and compare its performance against baselines that are trained on each dataset separately.
CITING: Large Language Models Create Curriculum for Instruction Tuning
Specifically, we employ a teacher LLM to create a curriculum for instruction tuning of the student LLM, namely Curriculum Instruction TunING (CITING).
DRG-LLaMA : Tuning LLaMA Model to Predict Diagnosis-related Group for Hospitalized Patients
In the U. S. inpatient payment system, the Diagnosis-Related Group (DRG) is pivotal, but its assignment process is inefficient.
PIE: Simulating Disease Progression via Progressive Image Editing
Disease progression simulation is a crucial area of research that has significant implications for clinical diagnosis, prognosis, and treatment.
MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models
Large language models (LLMs) have achieved remarkable performance in natural language understanding and generation tasks.
Matching Patients to Clinical Trials with Large Language Models
Second, the aggregated trial-level TrialGPT scores are highly correlated with expert eligibility annotations.
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.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
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.
Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models
Large language models (LLMs) specializing in natural language generation (NLG) have recently started exhibiting promising capabilities across a variety of domains.
Text-Augmented Open Knowledge Graph Completion via Pre-Trained Language Models
The mission of open knowledge graph (KG) completion is to draw new findings from known facts.
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.
BIOT: Cross-data Biosignal Learning in the Wild
Comprehensive evaluations on EEG, electrocardiogram (ECG), and human activity sensory signals demonstrate that \method outperforms robust baselines in common settings and facilitates learning across multiple datasets with different formats.
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.
ManyDG: Many-domain Generalization for Healthcare Applications
Extensive experiments show that ManyDG can boost the generalization performance on multiple real-world healthcare tasks (e. g., 3. 7% Jaccard improvements on MIMIC drug recommendation) and support realistic but challenging settings such as insufficient data and continuous learning.
Reinforced Genetic Algorithm for Structure-based Drug Design
The neural models take the 3D structure of the targets and ligands as inputs and are pre-trained using native complex structures to utilize the knowledge of the shared binding physics from different targets and then fine-tuned during optimization.
CT2: Colorization Transformer via Color Tokens
Automatic image colorization is an ill-posed problem with multi-modal uncertainty, and there remains two main challenges with previous methods: incorrect semantic colors and under-saturation.
MedCLIP: Contrastive Learning from Unpaired Medical Images and Text
Existing vision-text contrastive learning like CLIP aims to match the paired image and caption embeddings while pushing others apart, which improves representation transferability and supports zero-shot prediction.
Knowledge-Driven New Drug Recommendation
However, newly approved drugs do not have much historical prescription data and cannot leverage existing drug recommendation methods.
PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learning
Accessing longitudinal multimodal Electronic Healthcare Records (EHRs) is challenging due to privacy concerns, which hinders the use of ML for healthcare applications.
Artificial Intelligence for In Silico Clinical Trials: A Review
In silico trials are clinical trials conducted digitally through simulation and modeling as an alternative to traditional clinical trials.
MedML: Fusing Medical Knowledge and Machine Learning Models for Early Pediatric COVID-19 Hospitalization and Severity Prediction
We respond to the national Pediatric COVID-19 data challenge with a novel machine learning model, MedML.
Trial2Vec: Zero-Shot Clinical Trial Document Similarity Search using Self-Supervision
We propose a zero-shot clinical trial retrieval method, Trial2Vec, which learns through self-supervision without annotating similar clinical trials.
Conformal Prediction Intervals with Temporal Dependence
We focus on the task of constructing valid prediction intervals (PIs) in time series regression with a cross-section.
Conformal Prediction with Temporal Quantile Adjustments
TQA adjusts the quantile to query in CP at each time $t$, accounting for both cross-sectional and longitudinal coverage in a theoretically-grounded manner.
TransTab: Learning Transferable Tabular Transformers Across Tables
Can we leverage model pretraining on multiple distinct tables?
GOCPT: Generalized Online Canonical Polyadic Tensor Factorization and Completion
Our variant GOCPTE shows up to 1:2% and 5:5% fitness improvement on two datasets with about 20% speedup compared to the best model.
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.
MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design
Recently, with the rapid development of machine learning methods, especially generative methods, molecule design has achieved great progress by leveraging machine learning models to generate candidate molecules.
Effects of Epileptiform Activity on Discharge Outcome in Critically Ill Patients
Having a maximum EA burden greater than 75% when untreated had a 22% increased chance of a poor outcome (severe disability or death), and mild but long-lasting EA increased the risk of a poor outcome by 14%.
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.
Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks
Most existing calibration methods either lack theoretical guarantees for producing calibrated outputs, reduce classification accuracy in the process, or only calibrate the predicted class.
JULIA: Joint Multi-linear and Nonlinear Identification for Tensor Completion
Tensor completion aims at imputing missing entries from a partially observed tensor.
UniCoRN: A Unified Conditional Image Repainting Network
Conditional image repainting (CIR) is an advanced image editing task, which requires the model to generate visual content in user-specified regions conditioned on multiple cross-modality constraints, and composite the visual content with the provided background seamlessly.
Self-supervised EEG Representation Learning for Automatic Sleep Staging
Objective: In this paper, we aim to learn robust vector representations from massive unlabeled EEG signals, such that the learned vectorized features (1) are expressive enough to replace the raw signals in the sleep staging task; and (2) provide better predictive performance than supervised models in scenarios of fewer labels and noisy samples.
SurvTRACE: Transformers for Survival Analysis with Competing Events
In medicine, survival analysis studies the time duration to events of interest such as mortality.
PAC-Bayes Information Bottleneck
Then, we build an IIW-based information bottleneck on the trade-off between accuracy and information complexity of NNs, namely PIB.
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.
Locally Valid and Discriminative Prediction Intervals for Deep Learning Models
Moreover, when combined with deep learning (DL) methods, it should be scalable and affect the DL model performance minimally.
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.
EVA: Generating Longitudinal Electronic Health Records Using Conditional Variational Autoencoders
De-identified EHRs do not adequately address the needs of health systems, as de-identified data are susceptible to re-identification and its volume is also limited.
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.
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.
SWIFT: Scalable Wasserstein Factorization for Sparse Nonnegative Tensors
In particular, we define the N-th order tensor Wasserstein loss for the widely used tensor CP factorization and derive the optimization algorithm that minimizes it.
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.
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.
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.
Pay Attention to Evolution: Time Series Forecasting with Deep Graph-Evolution Learning
Time-series forecasting is one of the most active research topics in artificial intelligence.
SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates
We propose a new method for quantifying uncertainties of DNNs from a dynamical system perspective.
HOLMES: Health OnLine Model Ensemble Serving for Deep Learning Models in Intensive Care Units
HOLMES is tested on risk prediction task on pediatric cardio ICU data with above 95% prediction accuracy and sub-second latency on 64-bed simulation.
EMIXER: End-to-end Multimodal X-ray Generation via Self-supervision
EMIXER is an conditional generative adversarial model by 1) generating an image based on a label, 2) encoding the image to a hidden embedding, 3) producing the corresponding text via a hierarchical decoder from the image embedding, and 4) a joint discriminator for assessing both the image and the corresponding text.
ELF: An Early-Exiting Framework for Long-Tailed Classification
The natural world often follows a long-tailed data distribution where only a few classes account for most of the examples.
Ranked #40 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths
We propose a self-supervised taxonomy expansion model named STEAM, which leverages natural supervision in the existing taxonomy for expansion.
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.
TASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic Health Records
TASTE combines the PARAFAC2 model with non-negative matrix factorization to model a temporal and a static tensor.
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.
Clinical Concept Extraction for Document-Level Coding
The text of clinical notes can be a valuable source of patient information and clinical assessments.
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.
Pearl: Prototype lEArning via Rule Lists
However, the interpretability of those models are often lacking.
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".
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.
RAIM: Recurrent Attentive and Intensive Model of Multimodal Patient Monitoring Data
With the improvement of medical data capturing, vast amount of continuous patient monitoring data, e. g., electrocardiogram (ECG), real-time vital signs and medications, become available for clinical decision support at intensive care units (ICUs).
RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records
Therefore, our design study aims to provide a visual analytics solution to increase interpretability and interactivity of RNNs via a joint effort of medical experts, artificial intelligence scientists, and visual analytics researchers.
HAMLET: Interpretable Human And Machine co-LEarning Technique
Although cEEG monitoring yields large volumes of data, labeling costs and difficulty make it hard to build a classifier.
SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping
We propose two variants, SUSTain_M and SUSTain_T, to handle both matrix and tensor inputs, respectively.
COPA: Constrained PARAFAC2 for Sparse & Large Datasets
To tackle these challenges, we propose a {\it CO}nstrained {\it PA}RAFAC2 (COPA) method, which carefully incorporates optimization constraints such as temporal smoothness, sparsity, and non-negativity in the resulting factors.
Explainable Prediction of Medical Codes from Clinical Text
Our method aggregates information across the document using a convolutional neural network, and uses an attention mechanism to select the most relevant segments for each of the thousands of possible codes.
Ranked #10 on
Medical Code Prediction
on MIMIC-III
SLEEPNET: Automated Sleep Staging System via Deep Learning
Sleep disorders, such as sleep apnea, parasomnias, and hypersomnia, affect 50-70 million adults in the United States (Hillman et al., 2006).
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.
Generating Multi-label Discrete Patient Records using Generative Adversarial Networks
Access to electronic health record (EHR) data has motivated computational advances in medical research.
SPARTan: Scalable PARAFAC2 for Large & Sparse Data
For example, when modeling medical features across a set of patients, the number and duration of treatments may vary widely in time, meaning there is no meaningful way to align their clinical records across time points for analysis purposes.
Causal Regularization
In application domains such as healthcare, we want accurate predictive models that are also causally interpretable.
GRAM: Graph-based Attention Model for Healthcare Representation Learning
-Interpretation:The representations learned by deep learning methods should align with medical knowledge.
Sparse Hierarchical Tucker Factorization and its Application to Healthcare
It can also do so more accurately and in less time than the state-of-the-art: on a 12th order subset of the input data, Sparse H-Tucker is 18x more accurate and 7. 5x faster than a previously state-of-the-art method.
RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism
RETAIN was tested on a large health system EHR dataset with 14 million visits completed by 263K patients over an 8 year period and demonstrated predictive accuracy and computational scalability comparable to state-of-the-art methods such as RNN, and ease of interpretability comparable to traditional models.
Ranked #2 on
Disease Trajectory Forecasting
on UK CF trust
FLASH: Fast Bayesian Optimization for Data Analytic Pipelines
To achieve the best performance, it is often critical to select optimal algorithms and to set appropriate hyperparameters, which requires large computational efforts.
Multi-layer Representation Learning for Medical Concepts
Learning efficient representations for concepts has been proven to be an important basis for many applications such as machine translation or document classification.
Medical Concept Representation Learning from Electronic Health Records and its Application on Heart Failure Prediction
Objective: To transform heterogeneous clinical data from electronic health records into clinically meaningful constructed features using data driven method that rely, in part, on temporal relations among data.
Time-Sensitive Recommendation From Recurrent User Activities
By making personalized suggestions, a recommender system is playing a crucial role in improving the engagement of users in modern web-services.
Doctor AI: Predicting Clinical Events via Recurrent Neural Networks
Leveraging large historical data in electronic health record (EHR), we developed Doctor AI, a generic predictive model that covers observed medical conditions and medication uses.
Guaranteed Scalable Learning of Latent Tree Models
We present an integrated approach for structure and parameter estimation in latent tree graphical models.
