Ping Zhang

Orcid: 0000-0002-4601-0779

Affiliations:
  • Ohio State University, Columbus, OH, USA
  • IBM Thomas J. Watson Research Center, Center for Computational Health, Yorktown Heights, NY, USA
  • Temple University, Center for Data Analytics and Biomedical Informatics, Philadelphia, PA, USA (PhD 2012)


According to our database1, Ping Zhang authored at least 101 papers between 2010 and 2024.

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Bibliography

2024
DDE: Deep Dynamic Epidemiological Modeling for Infectious Illness Development Forecasting in Multi-level Geographic Entities.
J. Heal. Informatics Res., September, 2024

CURE: A deep learning framework pre-trained on large-scale patient data for treatment effect estimation.
Patterns, 2024

CardioAI: A Multimodal AI-based System to Support Symptom Monitoring and Risk Detection of Cancer Treatment-Induced Cardiotoxicity.
CoRR, 2024

Lessons Learned from a Unifying Empirical Study of Parameter-Efficient Transfer Learning (PETL) in Visual Recognition.
CoRR, 2024

Fine-Tuning is Fine, if Calibrated.
CoRR, 2024

Clinical Challenges and AI Opportunities in Decision-Making for Cancer Treatment-Induced Cardiotoxicity.
CoRR, 2024

MedVH: Towards Systematic Evaluation of Hallucination for Large Vision Language Models in the Medical Context.
CoRR, 2024

Inquire, Interact, and Integrate: A Proactive Agent Collaborative Framework for Zero-Shot Multimodal Medical Reasoning.
CoRR, 2024

Non-stationary Domain Generalization: Theory and Algorithm.
CoRR, 2024

SubgroupTE: Advancing Treatment Effect Estimation with Subgroup Identification.
CoRR, 2024

SSM-Net: Semi-supervised multi-task network for joint lesion segmentation and classification from pancreatic EUS images.
Artif. Intell. Medicine, 2024

SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing.
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Predictive Modeling with Temporal Graphical Representation on Electronic Health Records.
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis.
Proceedings of the CHI Conference on Human Factors in Computing Systems, 2024

KG-TREAT: Pre-training for Treatment Effect Estimation by Synergizing Patient Data with Knowledge Graphs.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Stable clinical risk prediction against distribution shift in electronic health records.
Patterns, September, 2023

TMM-Nets: Transferred Multi- to Mono-Modal Generation for Lupus Retinopathy Diagnosis.
IEEE Trans. Medical Imaging, April, 2023

Estimating treatment effects for time-to-treatment antibiotic stewardship in sepsis.
Nat. Mac. Intell., April, 2023

A fair and interpretable network for clinical risk prediction: a regularized multi-view multi-task learning approach.
Knowl. Inf. Syst., April, 2023

Human-centered design and evaluation of AI-empowered clinical decision support systems: a systematic review.
Frontiers Comput. Sci., 2023

Domain Invariant Representation Learning and Sleep Dynamics Modeling for Automatic Sleep Staging.
CoRR, 2023

Deep Dynamic Epidemiological Modelling for COVID-19 Forecasting in Multi-level Districts.
CoRR, 2023

DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images.
CoRR, 2023

Fairness and Accuracy under Domain Generalization.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Heterogeneous Treatment Effect Estimation with Subpopulation Identification for Personalized Medicine in Opioid Use Disorder.
Proceedings of the IEEE International Conference on Data Mining, 2023

2022
Mixed-Weight Neural Bagging for Detecting $m^6A$ Modifications in SARS-CoV-2 RNA Sequencing.
IEEE Trans. Biomed. Eng., 2022

Chemical-induced gene expression ranking and its application to pancreatic cancer drug repurposing.
Patterns, 2022

DeepDRiD: Diabetic Retinopathy - Grading and Image Quality Estimation Challenge.
Patterns, 2022

FAME: Fragment-based Conditional Molecular Generation for Phenotypic Drug Discovery.
Proceedings of the 2022 SIAM International Conference on Data Mining, 2022

Predicting Age-Related Macular Degeneration Progression with Contrastive Attention and Time-Aware LSTM.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment Regimes.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

DREAM: Domain Invariant and Contrastive Representation for Sleep Dynamics.
Proceedings of the IEEE International Conference on Data Mining, 2022

2021
NHBS-Net: A Feature Fusion Attention Network for Ultrasound Neonatal Hip Bone Segmentation.
IEEE Trans. Medical Imaging, 2021

An interpretable deep-learning model for early prediction of sepsis in the emergency department.
Patterns, 2021

A survival model generalized to regression learning algorithms.
Nat. Comput. Sci., 2021

A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing.
Nat. Mach. Intell., 2021

A deep learning framework for drug repurposing via emulating clinical trials on real-world patient data.
Nat. Mach. Intell., 2021

Clinical connectivity map for drug repurposing: using laboratory results to bridge drugs and diseases.
BMC Medical Informatics Decis. Mak., 2021

Temporal Clustering with External Memory Network for Disease Progression Modeling.
Proceedings of the IEEE International Conference on Data Mining, 2021

Cardiac Complication Risk Profiling for Cancer Survivors via Multi-View Multi-Task Learning.
Proceedings of the IEEE International Conference on Data Mining, 2021

TransICD: Transformer Based Code-Wise Attention Model for Explainable ICD Coding.
Proceedings of the Artificial Intelligence in Medicine, 2021

Contrastive Attention for Automatic Chest X-ray Report Generation.
Proceedings of the Findings of the Association for Computational Linguistics: ACL/IJCNLP 2021, 2021

2020
Combining structured and unstructured data for predictive models: a deep learning approach.
BMC Medical Informatics Decis. Mak., 2020

An interpretable risk prediction model for healthcare with pattern attention.
BMC Medical Informatics Decis. Mak., 2020

Interpretable Deep Learning for Automatic Diagnosis of 12-lead Electrocardiogram.
CoRR, 2020

Brain Atlas Guided Attention U-Net for White Matter Hyperintensity Segmentation.
CoRR, 2020

When deep learning meets causal inference: a computational framework for drug repurposing from real-world data.
CoRR, 2020

DrugDBEmbed : Semantic Queries on Relational Database using Supervised Column Encodings.
CoRR, 2020

Document Classification for COVID-19 Literature.
CoRR, 2020

Graph embedding on biomedical networks: methods, applications and evaluations.
Bioinform., 2020

Identifying Sepsis Subphenotypes via Time-Aware Multi-Modal Auto-Encoder.
Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Estimating Individual Treatment Effects with Time-Varying Confounders.
Proceedings of the 20th IEEE International Conference on Data Mining, 2020

Document Classification for COVID-19 Literature.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2020, 2020

Finding Causal Mechanistic Drug-Drug Interactions from Observational Data.
Proceedings of the AMIA 2020, 2020

2019
Towards early detection of adverse drug reactions: combining pre-clinical drug structures and post-market safety reports.
BMC Medical Informatics Decis. Mak., 2019

PerDREP: Personalized Drug Effectiveness Prediction from Longitudinal Observational Data.
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

Domain Knowledge Guided Deep Learning with Electronic Health Records.
Proceedings of the 2019 IEEE International Conference on Data Mining, 2019

A Systematic Framework for Drug Repurposing based on Literature Mining.
Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine, 2019

2018
Systematic analysis of drug combinations that mitigate adverse drug reactions.
IBM J. Res. Dev., 2018

Predicting adverse drug reactions through interpretable deep learning framework.
BMC Bioinform., 2018

Cell-specific prediction and application of drug-induced gene expression .
Proceedings of the Biocomputing 2018: Proceedings of the Pacific Symposium, 2018

Interpretable Drug Target Prediction Using Deep Neural Representation.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

A Trusted Healthcare Data Analytics Cloud Platform.
Proceedings of the 38th IEEE International Conference on Distributed Computing Systems, 2018

An Interpretable End-to-End Framework for Drug-Target Interaction Prediction Through Deep Neural Representation.
Proceedings of the AMIA 2018, 2018

Estimating Personalized Drug Effects with Longitudinal Observational Data.
Proceedings of the AMIA 2018, 2018

Estimating Causal Multi-Drug-Drug Interaction for Adverse Drug Reactions.
Proceedings of the AMIA 2018, 2018

2017
Large-scale structural and textual similarity-based mining of knowledge graph to predict drug-drug interactions.
J. Web Semant., 2017

Polyadic Regression and its Application to Chemogenomics.
Proceedings of the 2017 SIAM International Conference on Data Mining, 2017

Computational Drug Discovery with Dyadic Positive-Unlabeled Learning.
Proceedings of the 2017 SIAM International Conference on Data Mining, 2017

Precision Cohort Finding with Outcome-Driven Similarity Analytics: A Case Study of Patients with Atrial Fibrillation.
Proceedings of the MEDINFO 2017: Precision Healthcare through Informatics, 2017

Improving predictive models with clustered sequences: An Application on Heart Failure Risk Prediction.
Proceedings of the Summit on Clinical Research Informatics, 2017

Integrating Temporal Pattern Mining in Ischemic Stroke Prediction and Treatment Pathway Discovery for Atrial Fibrillation.
Proceedings of the Summit on Clinical Research Informatics, 2017

Exploiting Electronic Health Records to Mine Drug Effects on Laboratory Test Results.
Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017

DrugPathSeeker: Interactive UI for exploring drug-ADR relation via pathways.
Proceedings of the 2017 IEEE Pacific Visualization Symposium, 2017

Adverse Drug Reaction Prediction with Symbolic Latent Dirichlet Allocation.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

Multitask Dyadic Prediction and Its Application in Prediction of Adverse Drug-Drug Interaction.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016
Predicting Drug-Drug Interactions Through Similarity-Based Link Prediction Over Web Data.
Proceedings of the 25th International Conference on World Wide Web, 2016

Risk Prediction with Electronic Health Records: A Deep Learning Approach.
Proceedings of the 2016 SIAM International Conference on Data Mining, 2016

Using Frequent Item Set Mining and Feature Selection Methods to Identify Interacted Risk Factors - The Atrial Fibrillation Case Study.
Proceedings of the Exploring Complexity in Health: An Interdisciplinary Systems Approach - Proceedings of MIE2016 at HEC2016, Munich, Germany, 28 August, 2016

Healthcare Data Mining with Matrix Models.
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

Predicting Drug-Drug Interactions Through Large-Scale Similarity-Based Link Prediction.
Proceedings of the Semantic Web. Latest Advances and New Domains, 2016

Joint Modeling of Survival Events through Multi-task Learning Framework.
Proceedings of the AMIA 2016, 2016

Data-Driven Prediction of Beneficial Drug Combinations in Spontaneous Reporting Systems.
Proceedings of the AMIA 2016, 2016

Integrated Machine Learning Approaches for Predicting Ischemic Stroke and Thromboembolism in Atrial Fibrillation.
Proceedings of the AMIA 2016, 2016

Towards Large-Scale Predictive Drug Safety: A Computational Framework for Inferring Drug Interactions Through Similarity-Based Link Prediction.
Proceedings of the AMIA 2016, 2016

Tiresias: Knowledge Engineering and Large-Scale Machine Learning for Interpretable Drug-Drug Interaction Prediction.
Proceedings of the AMIA 2016, 2016

2015
Curating and Integrating Data from Multiple Sources to Support Healthcare Analytics.
Proceedings of the MEDINFO 2015: eHealth-enabled Health, 2015

Towards Computational Drug Repositioning: A Comparative Study of Single-task and Multi-task Learning.
Proceedings of the AMIA 2015, 2015

2014
<i>DDI-CPI</i>, a server that predicts drug-drug interactions through implementing the chemical-protein interactome.
Nucleic Acids Res., 2014

Exploring the associations between drug side-effects and therapeutic indications.
J. Biomed. Informatics, 2014

Predicting changes in hypertension control using electronic health records from a chronic disease management program.
J. Am. Medical Informatics Assoc., 2014

Clinical risk prediction with multilinear sparse logistic regression.
Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014

Towards Drug Repositioning: A Unified Computational Framework for Integrating Multiple Aspects of Drug Similarity and Disease Similarity.
Proceedings of the AMIA 2014, 2014

Clinical Risk Prediction by Exploring High-Order Feature Correlations.
Proceedings of the AMIA 2014, 2014

2013
Learning by aggregating experts and filtering novices: a solution to crowdsourcing problems in bioinformatics.
BMC Bioinform., 2013

Computational Drug Repositioning by Ranking and Integrating Multiple Data Sources.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013

Exploring the Relationship Between Drug Side-Effects and Therapeutic Indications.
Proceedings of the AMIA 2013, 2013

2012
Semi-Supervised Learning on Single-View Datasets by Integration of Multiple Co-trained Classifiers.
Proceedings of the 11th International Conference on Machine Learning and Applications, 2012

Integration of multiple annotators by aggregating experts and filtering novices.
Proceedings of the 2012 IEEE International Conference on Bioinformatics and Biomedicine, 2012

2011
Learning from Inconsistent and Unreliable Annotators by a Gaussian Mixture Model and Bayesian Information Criterion.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2011

2010
Unsupervised integration of multiple protein disorder predictors.
Proceedings of the 2010 IEEE International Conference on Bioinformatics and Biomedicine, 2010


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