Jianying Hu

contact information

IBM Fellow; Global Science Leader, AI for Health; Director, Healthcare and Life Sciences Research; IEEE Fellow, IAPR Fellow, IAHSI Fellow, ACMI Fellow
Thomas J. Watson Research Center, Yorktown Heights, NY USA

links

Professional Associations

Professional Associations: American Association for the Advancement of Sciences | American Medical Informatics Association | Fellow, IEEE | Fellow, International Association of Pattern Recognition

2020

Evaluating artificial intelligence in medicine: phases of clinical research
Y Park, GP Jackson, M Foreman, D Gruen, J Hu, AK Das
JAMIA Open, 2020

On Machine Learning-Based Short-Term Adjustment of Epidemiological Projections of COVID-19 in US
Sarah Kefayati, Hu Huang, Prithwish Chakraborty, Fred Roberts, Vishrawas Gopalakrishnan, Raman Srinivasan, Sayali Pethe, Piyush Madan, Ajay Deshpande, Xuan Liu, Jianying Hu, Gretchen Jackson
medRxiv, 2020
Abstract cluster analysis, staffing, autoregressive model, econometrics, pandemic, computer science, epidemiology, coronavirus disease 2019, early results, systemic analysis

Disease Network Delineates the Disease Progression Profile of Cardiovascular Diseases
Zefang Tang, Yiqin Yu, Kenney Ng, Daby Sow, Jianying Hu, Jing Mei
bioRxiv, 2020
Abstract disease, heart failure, long qt syndrome, intensive care medicine, medicine, age bias, disease progression, health records, real world data, real world evidence

Personalized Input-Output Hidden Markov Models for Disease Progression Modeling
Kristen A Severson, Lana M Chahine, Luba A Smolensky, Kenney Ng, Jianying Hu, Soumya Ghosh
Machine Learning for Healthcare, 2020
Abstract time series modeling, probabilistic logic, medication effects, markov model, machine learning, input output, hidden markov model, ground truth, disease progression, computer science, artificial intelligence

2019

A new promising way for tackling the pharma dilemma: artificial intelligence for clinical trials
S. Harrer, B. Anthony, A. Bayat, J. Hu
The Biochemist (Portland Press), 2019

Artificial Intelligence in Health in 2018: New Opportunities, Challenges, and Practical Implications
Gretchen Jackson, Jianying Hu
Yearb Med Inform 28(1), 52-54, 2019
Abstract yearbook, practical implications, multidisciplinary approach, heuristics, health informatics, encryption, editorial board, domain knowledge, computer science, artificial intelligence

Explicit-Blurred Memory Network for Analyzing Patient Electronic Health Records
Prithwish Chakraborty, Fei Wang, Jianying Hu, Daby Sow
arXiv preprint arXiv:1911.06472, 2019
Abstract salient, memory architecture, mathematics, machine learning, interpretability, explicit memory, data point, cognition, auxiliary memory, artificial intelligence, abstraction

G-Computation and Hierarchical Models for Estimating Multiple Causal Effects From Observational Disease Registries With Irregular Visits
Zach Shahn, Ying Li, Zhaonan Sun, Amrita Mohan, Cristina Sampaio, Jianying Hu
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2019, 789-798
Abstract population, physical medicine and rehabilitation, observational study, multiple comparisons problem, medicine, disease, confounding, clinical trial, chorea, causal inference

A probabilistic disease progression modeling approach and its application to integrated Huntingtons disease observational data
Zhaonan Sun, Soumya Ghosh, Ying Li, Yu Cheng, Amrita Mohan, Cristina Sampaio, Jianying Hu
JAMIA open 2(1), 123-130, 2019
Abstract probabilistic logic, observational study, machine learning, huntington s disease, disease progression, disease management, disease, computer science, clinical knowledge, clinical diagnosis, artificial intelligence

Artificial Intelligence for Clinical Trial Design
S. Harrer. P. Shah, B. Antony, J. Hu
Trends in Pharmacological Sciences (Cell Press), 2019

Artificial intelligence and machine learning in clinical development: a translational perspective
Pratik Shah, Francis Kendall, Sean Khozin, Ryan Goosen, Jianying Hu, Jason Laramie, Michael Ringel, Nicholas Schork
NPJ digital medicine 2(1), 2019
Abstract translational research, secure computing, medical care, health records, health care, food and drug administration, computer science, clinical trial, biotechnology industry, artificial intelligence

Complication Risk Profiling in Diabetes Care: A Bayesian Multi-Task and Feature Relationship Learning Approach
B Liu, Y Li, S Ghosh, Z Sun, K Ng, J Hu
IEEE Transactions on Knowledge and Data Engineering, 2019

2018

Digital Diabetes Congress 2018
Fraya King, David C. Klonoff, David Kerr, Jianying Hu, Courtney Lyles, Charlene Quinn, Saleh Adi, Kong Chen, Korey Hood, Patricia Salber, Casper de Clercq, Julia Hu, Robert Gabbay
Journal of diabetes science and technology 12(6), 1231-1238, 2018
Abstract the internet, software, internet privacy, health care, digital health, computer science

Predicting adverse drug reactions through interpretable deep learning framework
Sanjoy Dey, Heng Luo, Achille Fokoue, Jianying Hu, Ping Zhang
BMC Bioinformatics 19(Suppl 21), 476, 2018
Abstract

Cell-Specific Prediction and Application of Drug-Induced Gene Expression Profiles
Rachel Hodos, Ping Zhang, Hao-Chih Lee, Qiaonan Duan, Zichen Wang, Neil Clark, Avi Ma'ayan, Fei Wang, Brian Kidd, Jianying Hu, David Sontag
Pacific Symposium on Biocomputing (PSB), 2018
Abstract

Molecular Docking for Prediction and Interpretation of Adverse Drug Reactions
Heng Luo, Achille Fokoue, Nalini Singh, Lun Yang, Jianying Hu, Ping Zhang
Combinatorial Chemistry & High Throughput Screening, 2018
Abstract

2017

A Data-Driven Method for Generating Robust Symptom Onset Indicators in Huntingtons Disease Registry Data
Zhaonan Sun, Ying Li, Soumya Ghosh, Yu Cheng, Amrita Mohan, Cristina Sampaio, Jianying Hu
AMIA ... Annual Symposium proceedings. AMIA Symposium, pp. 1635-1644, 2017
symptom onset, symptom assessment, registry data, pediatrics, medicine, huntington s disease

An Exploration of Latent Structure in Observational Huntingtons Disease Studies
Soumya Ghosh, Zhaonan Sun, Ying Li, Yu Cheng, Amrita Mohan, Cristina Sampaio, Jianying Hu
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2017, 92-102
Abstract observational study, medicine, latent structure, huntington s disease, disease, cognition, clinical psychology

Predicting Future High-Cost Schizophrenia Patients Using High-Dimensional Administrative Data
Wang, Yajuan and Iyengar, Vijay and Hu, Jianying and Kho, David and Falconer, Erin and Docherty, John P and Yuen, Gigi Y
Frontiers in Psychiatry8, Frontiers Media SA, 2017
Abstract

Adverse Drug Reaction Prediction with Symbolic Latent Dirichlet Allocation
Cao Xiao, Ping Zhang, W. Art Chaovalitwongse, Jianying Hu, Fei Wang
AAAI Conference on Artificial Intelligence (AAAI), 2017
Abstract

DrugPathSeeker: Interactive UI for Exploring Drug-ADR Relation via Pathways
Janu Verma, Heng Luo, Jianying Hu, Ping Zhang
IEEE Pacific Visualization Symposium (PacificVis), 2017
Abstract

Exploiting Electronic Health Records to Mine Drug Effects on Laboratory Test Results
Mohamed Ghalwash, Ying Li, Ping Zhang, Jianying Hu
Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (CIKM), pp. 1837--1846

An Exploration of Latent Structure in Observational Huntington' s Disease Studies
Soumya Ghosh, Zhaonan Sun, Ying Li, Yu Cheng, Amrita Mohan, Cristina Sampaio, Jianying Hu
AMIA Summits on Translational Scinece Proceddings 2017

2016

Data-Driven Prediction of Beneficial Drug Combinations in Spontaneous Reporting Systems
Li, Ying and Zhang, Ping and Sun, Zhaonan and Hu, Jianying
American Medical Informatics Association Summit on Clinical Research Informatics(AMIA)., 2016
Abstract

Predicting Negative Events: Using Post-discharge Data to Detect High-Risk Patients
Sulieman, Lina and Fabbri, Daniel and Wang, Fei and Hu, Jianying and Malin, Bradley A
AMIA Annual Symposium Proceedings, pp. 1169, 2016
Abstract

Data-Driven Modeling of Electronic Health Record Data to Predict Prediagnostic Heart Failure in Primary Care
Ng, Kenney and Stewart, Walter F and deFilippi, Christopher and Dey, Sanjoy and Byrd, Roy J and Steinhubl, Steven R and Daar, Zahra and Law, Heather and Pressmnan, Alice R and Hu, Jianying
Journal of Patient-Centered Research and Reviews 3(3), 200, Aurora Health Care, Inc., 2016
Abstract

Risk Prediction with Electronic Health Records: A Deep Learning Approach
Y Cheng, F Wang, P Zhang and J Hu
2016 SIAM International Conference on Data Mining (SDM 2016)

Identifying and investigating unexpected response to treatment: a diabetes case study
Ozery-Flato, Michal and Ein-Dor, Liat and Parush-Shear-Yashuv, Naama and Aharonov, Ranit and Neuvirth, Hani and Kohn, Martin S and Hu, Jianying
Big data 4(3), 148--159, Mary Ann Liebert, Inc. 140 Huguenot Street, 3rd Floor New Rochelle, NY 10801 USA, 2016
Abstract

Data Driven Analytics for Personalized Healthcare
Hu, Jianying and Perer, Adam and Wang, Fei
Healthcare Information Management Systems, pp. 529--554, Springer, 2016

2015

Synthesizing analytic evidence to refine care pathways
Adam Perer, Guo Tong Xie, Jianying Hu, Yiqin Yu, Jing Mei, Haifeng Liu, Xiang Li, Fei Wang
medical informatics europe, pp. 70-74, 2015
Abstract ambulatory care, cohort, data mining, medicine

A Graph Based Methodology for Temporal Signature Identification from HER
Fei Wang, Chuanren Liu, Yajuan Wang, Jianying Hu, Guoqiang Yu
AMIA ... Annual Symposium proceedings. AMIA Symposium, pp. 1269-1278, 2015
Abstract sequential pattern mining, quality of care, healthcare data, health records, graph based, graph, graph, data driven, data mining, computer science

Abstract 16364: Physician Documentation Behaviors in Electronic Health Records as a Potential Source of Noise for Early Detection of Heart Failure
Yajuan Wang, Kenney Ng, Jianying Hu, Roy J Byrd, Steven R Steinhubl, Christopher deFilippi, Walter F Stewart
Circulation132, 2015
Abstract stratified sampling, potential source, medicine, internal medicine, heart failure, health records, framingham risk score, emergency medicine, early detection, documentation, denial, cardiology

Introduction: 1 Electronic health records (EHRs) are a potentially rich source of data for developing predictive models for early detection of heart failure (HF). But, EHR documentation can vary both because of patient health and variation in clinical practice and behavior among physicians. 43 44 Hypothesis: 46 The "noise" contributed by variable physician behaviors, such as differences in the frequency and detail of documentation, could potentially confound predictive models for detection of HF. In this study, we characterized the documentation behaviors of primary care physicians (PCPs) in an effort to better understand this potential source of noise. 97 98 Methods: 100 We used longitudinal EHR data of a stratified random sample of 5,187 patients who were: 1) 50 years of age, and without a history of HF. PCPs (n=144) were identified and paired with the patients that they had treated for a minimum of 6 months. We derived 28 measures to characterize PCP behaviors - documentation frequencies of assertions and denials of selected Framingham HF signs and symptoms (FHFSS) in office visit encounter notes adjusted for patient comorbidities. Hierarchical clustering analyses (HCA) were performed on PCP documentation behaviors. 188 189 Results: 191 Based on HCA analyses, PCPs were clustered into 3 groups with distinct documentation behaviors. Group 1 PCPs (n=63) documented 10 out of 15 assertions, and 11 out of 13 denials of FHFSS significantly more frequently than Group 3 (n=20); while Group 2 PCPs (n=61) have significantly more frequent denial documentation behaviors than the other two (see Figure 1). No significant differences were found among patients chronic, episodic and cardio metabolic chronic disease counts (comorbidities) in each of the groups (p 272 273 Conclusions: 275 This study identified PCP groups with distinct documentation behaviors unrelated to patient complexity. This source of noise and potential confounder should be taken into account for predictive modeling.

Personalized Predictive Modeling and Risk Factor Identification using Patient Similarity
Ng K, Sun J, Hu J, Wang F
AMIA Jt Summits Transl Sci Proc. 2015 Mar 25;2015:132-6.

Abstract 17713: Data Driven Modeling of Electronic Health Record Data to Detect Pre-diagnostic Heart Failure in Primary Care
Steven R Steinhubl, Jianying Hu, Kenney Ng, Roy J Byrd, Yajuan Wang, Christopher deFilippi, Zahar Daar, Heather Law, Walter F Stewart
Circulation132, 2015
Abstract acute coronary syndrome, ejection fraction, primary care physician, heart failure, medical record, cross validation, feature vector, feature selection, medical emergency, internal medicine, cardiology, medicine

Introduction: 1 Electronic health records (EHRs) represent an opportunity for real-time early risk prediction. EHR records were used to determine the extent to which machine learning tools and a purely data driven approach to modeling (DDM) could detect heart failure subtypes, i.e., preserved ejection fraction (HFpEF) and reduced ejection fraction (HFrEF), 12 months before a clinical diagnosis. 57 58 Methods: 60 Incident HF cases were identified from Geisinger Clinic primary care patients age 50-85, diagnosed between 2001 and 2010 and further defined as HFrEF if LVEF40 and HFpEF if LVEF>50. Controls were chosen to match HF cases by age, gender, location and primary care physician. EHR data were extracted on demographics, ICD-9 codes, medication orders, clinical and behavioral measures. Modeling was completed to detect HF using data from a 24-month observation window 12 months before the HF diagnosis. Patient feature vectors were generated from the data and summarized by one or more aggregation functions (e.g., counts, means). For the HF endpoint, modeling was done with and without patients who had an acute coronary syndrome (ACS) event within 12 months of the diagnosis. Regularized logistic regression was applied using information gain feature selection and 10-fold cross validation. Model performance was assessed by the area under the ROC curve (AUC) and complexity by the number of selected features. 216 217 Results: 219 Performance for HFpEF is better than for HFrEF. The HFpEF model is more complex than the HFrEF model as indicated by more EHR information that was needed to discriminate the HFpEF cases from controls. Performance with and without ACS cases is similar though models including ACS cases are more complex than models excluding them (Table). 275 276 Conclusions: 278 Purely data driven approaches to modeling can be used to detect HF 12 months before clinical diagnosis. Model performance and complexity varies with the HF subtypes indicating differences in the complexity of modeling the HF subtypes.

Synthesizing analytic evidence to refine care pathways.
Liu, Haifeng and Li, Xiang and Yu, Yiqin and Mei, Jing and Xie, Guo Tong and Perer, Adam and Wang, Fei and Hu, Jianying
MIE, pp. 70--74, 2015
Abstract

Analysis of care pathway variation patterns in patient records.
Li, Xiang and Mei, Jing and Liu, Haifeng and Yu, Yiqin and Xie, Guotong and Hu, Jianying and Wang, Fei
MIE, pp. 692--696, 2015
Abstract

Early detection of heart failure with varying prediction windows by structured and unstructured data in electronic health records
Wang, Yajuan and Ng, Kenney and Byrd, Roy J and Hu, Jianying and Ebadollahi, Shahram and Daar, Zahra and Steinhubl, Steven R and Stewart, Walter F and others
Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, pp. 2530--2533
Abstract

A system for identifying and investigating unexpected response to treatment
Ozery-Flato, Michal and Ein-Dor, Liat and Neuvirth, Hani and Parush, Naama and Kohn, Martin S and Hu, Jianying and Aharonov, Ranit
AMIA Summits on Translational Science Proceedings2015, 137, American Medical Informatics Association

Towards actionable risk stratification
Robert Sorrentino, Jianying Hu, Xiang Wang, Fei Wang
Journal of Biomedical Informatics53, 147-155, 2015
Abstract framingham risk score, clinical decision support system, cohort, data mining, medicine

Label Propagation Prediction of Drug-Drug Interactions Based on Clinical Side Effects
Zhang, Ping and Wang, Fei and Hu, Jianying and Sorrentino, Robert
Scientific reports5, Nature Publishing Group, 2015

Physician Documentation Behaviors in Electronic Health Records as a Potential Source of Noise for Early Detection of Heart Failure
Wang, Yajuan and Ng, Kenney and Hu, Jianying and Byrd, Roy J and Steinhubl, Steven R and Stewart, Walter F and others
Circulation 132(Suppl 3), A16364--A16364, Am Heart Assoc, 2015

Data Driven Modeling of Electronic Health Record Data to Detect Pre-diagnostic Heart Failure in Primary Care
Ng, Kenney and Stewart, Walter F and Wang, Yajuan and Byrd, Roy J and Steinhubl, Steven R and Daar, Zahar and Law, Heather and Hu, Jianying and others
Circulation 132(Suppl 3), A17713--A17713, Am Heart Assoc, 2015

Temporal Phenotyping from Longitudinal Electronic Health Records: A Graph Based Framework
Liu, Chuanren and Wang, Fei and Hu, Jianying and Xiong, Hui
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 705--714, 2015

Analysis of Care Pathway Variation Patterns in Patient Records
Li, Xiang and Mei, Jing and Liu, Haifeng and Yu, Yiqin and Xie, Guotong and Hu, Jianying and Wang, Fei
Studies in health technology and informatics210, 692, 2015

LINKAGE: An Approach for Comprehensive Risk Prediction for Care Management
Sun, Zhaonan and Wang, Fei and Hu, Jianying
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1145--1154, 2015

Towards actionable risk stratification: a bilinear approach
Wang, Xiang and Wang, Fei and Hu, Jianying and Sorrentino, Robert
Journal of biomedical informatics53, 147--155, Elsevier, 2015

Mining and exploring care pathways from electronic medical records with visual analytics
Perer, Adam and Wang, Fei and Hu, Jianying
Journal of biomedical informatics56, 369--378, Elsevier, 2015

2014

Towards Personalized Medicine: Leveraging Patient Similarity and Drug Similarity Analytics
Ping Zhang, Fei Wang, Jianying Hu, Robert Sorrentino
AMIA Summit on Translational Bioinformatics (TBI), 2014
Abstract

Clinical Risk Prediction by Exploring High-Order Feature Correlations
Fei Wang, Ping Zhang, Xiang Wang, Jianying Hu
American Medical Informatics Association Annual Symposium (AMIA), 2014
Abstract

Care pathway workbench: evidence harmonization from guideline and data
Yiqin Yu, Haifeng Liu, Jing Li, Xiang Li, Jing Mei, Adam Perer, Fei Wang, Jianying Hu
Medical Informatics Europe Conference (MIE), 2014

Exploring Joint Disease Risk Prediction
Xiang Wang, Fei Wang, Jianying Hu, Robert Sorrentino
Proceedings of the American Medical Informatics Association Annual Symposium (AMIA), 2014

Towards drug repositioning: a unified computational framework for integrating multiple aspects of drug similarity and disease similarity
Zhang, Ping and Wang, Fei and Hu, Jianying
AMIA Annu Symp Proc, pp. 1258--67, 2014
Abstract

Analysis of documents born digital
Hu, Jianying and Liu, Ying
Handbook of Document Image Processing and Recognition, pp. 775--804, Springer, 2014

From micro to macro: data driven phenotyping by densification of longitudinal electronic medical records
Zhou, Jiayu and Wang, Fei and Hu, Jianying and Ye, Jieping
Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 135--144, 2014

A Multi-task Learning Framework for Joint Disease Risk Prediction and Comorbidity Discovery
Wang, Xiang and Wang, Fei and Hu, Jianying
2014 22nd International Conference on Pattern Recognition (ICPR), pp. 220--225

DensityTransfer: A Data Driven Approach for Imputing Electronic Health Records
Wang, Fei and Zhou, Jiayu and Hu, Jianying
2014 22nd International Conference on Pattern Recognition (ICPR), pp. 2763--2768

Care pathway workbench: evidence harmonization from guideline and data.
Yu, Yiqin and Liu, Haifeng and Li, Jing and Li, Xiang and Mei, Jing and Xie, Guotong and Perer, Adam and Wang, Fei and Hu, Jianying
MIE, pp. 23--27, 2014

Exploring the associations between drug side-effects and therapeutic indications
F. Wang, P. Zhang, N. Cao, J. Hu, and R. Sorrentino
Journal of Biomedical Informatics51, 15-23, 2014

2013

Exploring the Relationship Between Drug Side-Effects and Therapeutic Indications
Ping Zhang, Fei Wang, Jianying Hu, Robert Sorrentino
American Medical Informatics Association Annual Symposium (AMIA), 2013
Abstract

Patient risk prediction model via top-k stability selection
Zhou, Jiayu and Sun, Jimeng and Liu, Yashu and Hu, Jianying and Ye, Jieping
In Proceedings of the 13th SIAM International Conference on Data Mining, 2013

Exploring patient risk groups with incomplete knowledge
Wang, Xiang and Wang, Fei and Wang, Jun and Qian, Buyue and Hu, Jianying
Data Mining (ICDM), 2013 IEEE 13th International Conference on, pp. 1223--1228

A Framework for Mining Signatures from Event Sequences and Its Applications in Healthcare Data
Fei Wang, Noah Lee, Jianying hu, Jimeng Sun, Shahram Ebadollahi
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 35(2), 272-285, IEEE, 2013

2012

SOR: Scalable Orthogonal Regression for Low-Redundancy Feature Selection and its Healthcare Applications
Dijun Luo, Fei Wang, Jimeng Sun, Marianthi Markatou, Jianying Hu, Shahram Ebadollahi
Proceedings of the 12th SIAM Data Mining Conference (SDM), 2012

A Healthcare Utilization Analysis Framework for Hot Spotting and Contextual Anomaly Detection
J. Hu, F. Wang, J. Sun, R. Sorrentino, S. Ebadollahi
AMIA, 2012

Preface: Technologies for healthcare transformation
Chen-Ritzo, Ching-Hua and Hu, Jianying
IBM Journal of Research and Development 56(5), 0--1, IBM, 2012
Abstract

A healthcare utilization analysis framework for hot spotting and contextual anomaly detection.
Hu, Jianying and Wang, Fei and Sun, Jimeng and Sorrentino, Robert and Ebadollahi, Shahram
AMIA, 2012

Supervised patient similarity measure of heterogeneous patient records
Sun, Jimeng and Wang, Fei and Hu, Jianying and Edabollahi, Shahram
ACM SIGKDD Explorations Newsletter 14(1), 16--24, ACM, 2012
Abstract

SOR: scalable orthogonal regression for non-redundant feature selection and its healthcare applications
Luo, Dijun and Wang, Fei and Sun, Jimeng and Markatou, Marianthi and Hu, Jianying and Ebadollahi, Shahram
SIAM data mining conference, pp. 576--587, 2012

A healthcare utilization analysis framework for hot spotting and contextual anomaly detection.
Hu, Jianying and Wang, Fei and Sun, Jimeng and Sorrentino, Robert and Ebadollahi, Shahram
AMIA, 2012

Combining knowledge and data driven insights for identifying risk factors using electronic health records.
Sun, Jimeng and Hu, Jianying and Luo, Dijun and Markatou, Marianthi and Wang, Fei and Ebadollahi, Shahram and Daar, Zahra and Stewart, Walter F
AMIA, pp. 901--10, 2012

Medical prognosis based on patient similarity and expert feedback
Wang, Fei and Hu, Jianying and Sun, Jimeng
Pattern Recognition (ICPR), 2012 21st International Conference on, pp. 1799--1802

Real-time analysis for short-term prognosis in intensive care
DM Sow, J. Sun, A. Biem, J. Hu, ML Blount, S. Ebadollahi
IBM Journal of Research and Development 56(5), 3--1, IBM, 2012

Towards heterogeneous temporal clinical event pattern discovery: a convolutional approach
F. Wang, N. Lee, J. Hu, J. Sun, S. Ebadollahi
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 453--461, 2012

Combining Knowledge and Data Driven Insights for Identifying Risk Factors using Electronic Health Records
J. Sun, J. Hu, D. Luo, M. Markatou, F. Wang, S. Edabollahi, S.E. Steinhubl, Z. Daar, W.F. Stewart
AMIA, 2012

Case-based reasoning in comparative effectiveness research
Marianthi Markatou, Prabani Kuruppumullage Don, Jianying Hu, Fei Wang, Jimeng Sun, Robert Sorrentino, Shahram Ebadollahi
IBM Journal of Development and Research 56(5), 4:1-4:12, IBM, 2012

2011

Proceedings of the 2011 workshop on Data mining for medicine and healthcare
Ramasamy Uthurusamy, Balaji Krishnapuram, Nitesh Chawla, Jianying Hu, Jonathan Silverstein, Jimeng Sun, Rayid Ghani, John Younger, David Madigan, Fei Wang, Mohit Kumar, K. P. Unnikrishnan
KDD 11 The 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011
Abstract collaborative care, comparative effectiveness research, pay for performance, clinical decision support system, data quality, teamwork, government, text mining, knowledge management, data mining, computer science

Healthcare systems around the world are struggling to keep up with patient needs, and improve quality of care while reducing costs at the same time. At the same time, more and more data is being captured around healthcare processes in the form of Electronic Health Records (EHR), health insurance claims, medical imaging databases, disease registries, spontaneous reporting sites, and clinical trials. As this data gets collected, government regulations are requiring healthcare providers to not only store it in an electronic format but also use it in meaningful ways. Using this data in an effective way to improve quality of care and reduce costs requires innovation in data mining as well as academic, industry and government partnerships. The goals of this workshop are to: Bring together the KDD community and the medical researchers & practitioners to discuss and explore mutual benefits of applying KDD to the right medical challenges and to collaborate on identifying and developing promising new techniques and methodologies.Bring together researchers (from both academia and industry) as well as practitioners from all three different groups in medicine and healthcare (payers, providers, and pharmaceuticals) to talk about their different perspectives and to share their latest problems and ideas.Attract healthcare professionals who have access to interesting sources of data and problems but not the expertise in data mining to solve them effectively. This group would otherwise not attend KDD and we believe through our personal experiences that it is essential for KDD research community to interact with them.This workshop serves as a bridge between the traditional KDD community and professionals in medicine and healthcare - two groups of participants that have a lot to learn from and share with each other. We aim to emphasize the following aspects: Addressing the fundamental challenges in improving healthcare and how data mining technologies will helpPresenting recent advances in data mining algorithms and methods for healthcare transformationIdentifying the next step of healthcare solutions and the possible data driven solutionsFostering interactions and collaborations among researchers and practitioners (from different backgrounds), and healthcare professionals, to promote cross-fertilization of ideas.Exploring unified platforms and data for better evaluation of the techniquesDeployed healthcare applications of data miningNew classes of research problems motivated by real-world business problemsData mining applications as components of healthcare business processesHow data mining is useful for various participants in the healthcare systemProviders (hospitals, labs, clinics)Payers (Insurance companies)PharmaceuticalsTopic areas for the workshop include (but are not limited to) the following: Statistical analysis and characterization of healthcare dataMeaningful use of healthcare data for improved patient care and cost-reductionData quality assessment and improvement: preprocessing, cleaning, missing data treatment etc. Pattern detection and hypothesis generation from observational dataComparative effectiveness researchMedical information retrievalCloud-computing models and scalabilityPrivacy and security issues in healthcareInformation visualization for healthcare dataInformation fusion and knowledge transfer in healthcareEvolutionary and longitudinal patient and disease modelsMining knowledge from medical imaging dataMedical fraud detectionClinical decision supportBio-surveillanceIntelligent payment modelsCollaborative care delivery modelsPost-market surveillance of medical interventionsText mining - mining free text in electronic medical recordsHelp with ICD 9 to ICD 10 conversionsImproving Clinical trial management and designPay for performance models in healthcareFeasibility of Health Information exchanges

Automatic Group Sparse Coding.
Wang, Fei and Lee, Noah and Sun, Jimeng and Hu, Jianying and Ebadollahi, Shahram
AAAI, 2011
Abstract

iMet: Interactive Metric Learning in Healthcare Applications.
Wang, Fei and Sun, Jimeng and Hu, Jianying and Ebadollahi, Shahram
SDM, pp. 944--955, 2011

Information technology for healthcare transformation
JP Bigus, M Campbell, B Carmeli, M Cefkin, H Chang, C-H Chen-Ritzo, WF Cody, S Ebadollahi, A Evfimievski, A Farkash, others
IBM Journal of Research and Development 55(5), 6--1, IBM, 2011

iMet: Interactive Metric Learning in Healthcare Applications
F. Wang, J. Sun, J. Hu, S. Ebadollahi
SIAM Conference on Data Mining, 2011

OnTheMark: Integrated Stochastic Resource Planning of Human Capital Supply Chains
H Cao, J Hu, C Jiang, T Kumar, T H Li, Y Liu, Y Lu, S Mahatma, A Mojsilovic, M Sharma, others
Interfaces 41(5), 414--435, INFORMS, 2011

Mining electronic medical records to explore the linkage between healthcare resource utilization and disease severity in diabetic patients
N Lee, A F Laine, J Hu, F Wang, J Sun, S Ebadollahi
Healthcare Informatics, Imaging and Systems Biology (HISB), 2011 First IEEE International Conference on, pp. 250--257

Automatic Group Sparse Coding
F Wang, N Lee, J Sun, J Hu, S Ebadollahi
Twenty-Fifth AAAI Conference on Artificial Intelligence, 2011

Toward personalized care management of patients at risk: the diabetes case study
Hani Neuvirth, Michal Ozery-Flato, Jianying Hu, Jonathan Laserson, Martin S Kohn, Shahram Ebadollahi, Michal Rosen-Zvi
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 395--403, 2011

2010

Predicting Patients Trajectory of Physiological Data using Temporal Trends in Similar Patients: A System for Near-Term Prognostics
Shahram Ebadollahi, Chalapathy Neti, Jianying Hu, Daby Sow, Jimeng Sun, David Gotz
AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium, pp. 192-196, 2010
Abstract prognostics, cohort, similarity measure, decision support system, data mining, medicine

Performance Management of IT Services Delivery
J Hu, Y Lu, A Mojsilovic, M Sharma, M S Squillante
Performance Evaluation Review 37(4), 50--57, ACM, 2010

Real-time prognosis of ICU physiological data streams
D Sow, A Biem, J Sun, J Hu, S Ebadollahi
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE, pp. 6785--6788

Predicting patientâ€™s trajectory of physiological data using temporal trends in similar patients: A system for Near-Term prognostics
S Ebadollahi, J Sun, D Gotz, J Hu, D Sow, C Neti
AMIA Annual Symposium Proceedings, pp. 192, 2010

A System for Mining Temporal Physiological Data Streams for Advanced Prognostic Decision Support
J Sun, D Sow, J Hu, S Ebadollahi
Data Mining (ICDM), 2010 IEEE 10th International Conference on, pp. 1061--1066

One-class matrix completion with low-density factorizations
V Sindhwani, S S Bucak, J Hu, A Mojsilovic
Data Mining (ICDM), 2010 IEEE 10th International Conference on, pp. 1055--1060

Real-time prognosis of ICU physiological data stream
D. Sow, J. Sun, J. Hu, S. Ebadollahi, A. Biem
EMBC/EMBS 2010

Localized Supervised Metric Learning on Temporal Physiological Data
J Sun, D Sow, J Hu, S Ebadollahi
Pattern Recognition (ICPR), 2010 20th International Conference on, pp. 4149--4152

Real-time prognosis of ICU physiological data stream
D. Sow, J. Sun, J. Hu, S. Ebadollahi, A. Biem
EMBC/EMBS 2010

2009

Winning the KDD Cup Orange Challenge with Ensemble Selection.
Niculescu-Mizil, Alexandru and Perlich, Claudia and Swirszcz, Grzegorz and Sindhwani, Vikas and Liu, Yan and Melville, Prem and Wang, Dong and Xiao, Jing and Hu, Jianying and Singh, Moninder and others
KDD Cup, pp. 23--34, 2009
Abstract

A new framework for recognition of heavily degraded characters in historical typewritten documents based on semi-supervised clustering
S Pletschacher, J Hu, A Antonacopoulos
Document Analysis and Recognition, 2009, pp. 506--510

Winning the KDD cup orange challenge with ensemble selection
A Niculescu-Mizil, C Perlich, G Swirszcz, V Sindhwani, Y Liu, P Melville, D Wang, J Xiao, J Hu, M Singh, others
KDD Cup and Workshop in conjunction with KDD, pp. 23--34, Citeseer, 2009

Leveraging social networks for corporate staffing and expert recommendation
V Chenthamarakshan, K Dey, J Hu, A Mojsilovic, W Riddle, V Sindhwani
IBM Journal of Research and Development 53(6), 915--924, IBM Corp., 2009

A framework for combined bayesian analysis and optimization for services delivery
A Shirazi, J Hu, M Singh, M Squillante, A Mojsilovic
Service Operations, Logistics and Informatics, 2009, pp. 382--387

2008

Information boosting methodologies for multiple data sources in workforce management
Hu, Jianying and Lu, Yingdong and Mojsilovic, Aleksandra and Radovanovic, Ana and Squillante, Mark S
Proc. INFORMS MSOM Conference, 2008

Using data mining for accurate resource and skill demand forecasting in services engagements
Hu, Jianying and Singh, Moninder and Mojsilovic, Aleksandra
KDD Workshop on Data Mining for Business Applications, 2008

Sequence mining for business analytics: Building project taxonomies for resource demand forecasting
Datta, Ritendra and Hu, Jianying and Ray, Bonnie
Frontiers in Artificial Intelligence and Applications, 133, 2008

"Information Boosting" Methodologies for Multiple Data Sources in Workforce Management
J Hu, Y Lu, A Mojsilovic, A Radovanovic, M S Squillante
MSOM Proceedings, 2008

On efficient Viterbi decoding for hidden semi-Markov models
R Datta, J Hu, B Ray
Pattern Recognition, 2008, pp. 1--4

Using data mining for accurate resource and skill demand forecasting in services engagements
J Hu, M Singh, A Mojsilovic
KDD Workshop on Data Mining for Business Applications, pp. 12, 2008

K-means clustering of proportional data using L1 distance
H Kashima, J Hu, B Ray, M Singh
Pattern Recognition, 2008, pp. 1--4

Categorization using semi-supervised clustering
J Hu, M Singh, A Mojsilovic
Pattern Recognition, 1--4, 2008

Regularized co-clustering with dual supervision
V Sindhwani, J Hu, A Mojsilovic
Advances in Neural Information Processing Systems21, 2008

2007

Quantitative structure-activity relationship model for prediction of genotoxic potential for quinolone antibacterials
Hu, Jianying and Wang, Wanfeng and Zhu, Zhou and Chang, Hong and Pan, Feng and Lin, Binle
Environmental science & technology 41(13), 4806--4812, ACS Publications, 2007

Web document analysis
A Antonacopoulos, J Hu
Digital Document Processing, 407--419, Springer, 2007

High-utility pattern mining: A method for discovery of high-utility item sets
J Hu, A Mojsilovic
Pattern Recognition 40(11), 3317--3324, Elsevier, 2007

Statistical methods for automated generation of service engagement staffing plans
J Hu, BK Ray, M Singh
IBM journal of research and development 51(3.4), 281--293, IBM, 2007

2006

An Interweaved HMM/DTW Approach to Robust Time Series Clustering.
Hu, Jianying and Ray, Bonnie K and Han, Lanshan
ICPR (3)6, 145--148, 2006
Abstract

Offering Pattern Mining Using High Yield Partition Trees
J Hu, A Mojsilovic
Acoustics, Speech and Signal Processing, 2006, pp. V--V

A Resource Planning Model for Professional Services Organizations
K Balaji, R DeSouza, M Goh, B T Eck, J Hu, B Ray
Automation Science and Engineering, 2006, pp. 636--639

An interweaved hmm/dtw approach to robust time series clustering
J Hu, B Ray, L Han
Pattern Recognition, 2006, pp. 145--148

2005

Dynamic signature verification using discriminative training
G F Russell, J Hu, A Biem, A Heilper, D Markman
Document Analysis and Recognition, 2005, pp. 1260--1264, IEEE Computer Society

2004

Retail applications of signature verification
Thomas G Zimmerman, Gregory F Russell, Andre Heilper, Barton A Smith, Jianying Hu, Dmitry Markman, Jon E Graham, Clemens Drews
Defense and Security, pp. 206--214, Citeseer, 2004

Probability table compression using distributional clustering for scanning n-tuple classifiers
J Hu, E Ratzlaff
Pattern Recognition, 2004, pp. 533--536

Retail applications of signature verification
T G Zimmerman, G F Russell, A Heilper, B A Smith, J Hu, D Markman, J E Graham, C Drews
Biometric Technology for Human Identification, Proceedings of the SPIE, Vol5404, 206--14, 2004

Categorizing images in web documents
J Hu, A Bagga
Multimedia, IEEE 11(1), 22--30, IEEE, 2004

2003

Document recognition and retrieval X:(Santa Clara CA, 22-24 January 2003)
T Kanungo, E H B Smith, J Hu, P B Kantor
SPIE proceedings series, 2003

Document Recognition and Retrieval X (Proceedings Volume)
S Rosset, J Zhu, T J Hastie, J Fan, T Pham, B Zhang, S N Srihari, H Liu, X Ding, C Fang, others
2003 - spie.org

Automatic table detection in html documents
Y Wang, J Hu
SERIES IN MACHINE PERCEPTION AND ARTIFICIAL INTELLIGENCE55, 135--154, WORLD SCIENTIFIC PUBLISHING, 2003

Identifying story and preview images in news web pages
J Hu, A Bagga
7th Intern, pp. 640--644, 2003

Functionality-based web image categorization
J Hu, A Bagga
WWW2003

Document Recognition and Retrieval XI
E H Barney Smith, J Hu, J Allan
Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, 2003

2002

Extraction of perceptually important colors and similarity measurement for image matching, retrieval and analysis
Mojsilovi, Aleksandra and Hu, Jianying and Soljanin, Emina
Image Processing, IEEE Transactions on 11(11), 1238--1248, IEEE, 2002

Document Analysis Systems V: 5th International Workshop, DAS 2002, Princeton, NJ, USA, August 19-21, 2002. Proceedings
D. Lopresti, J. Hu, R. Kashi
2002 - books.google.com, Springer

Evaluating the performance of table processing algorithms
J. Hu, RS Kashi, D. Lopresti, GT Wilfong
International Journal on Document Analysis and Recognition 4(3), 140--153, Springer, 2002

Proceedings of the 5th International Workshop on Document Analysis Systems V
D P Lopresti, J Hu, R S Kashi
2002 - dl.acm.org, Springer-Verlag

Spatial color component matching of images
J Hu, E Hadjidemetriou
Pattern Recognition, 2002, pp. 948--951

Document nalysis Systems
D Lopresti, J Hu, R Kashi
2002 - retepuglia.uniba.it

Multi-source Combined-Media Video Tracking for Summarization
A B J H J Zhong, G Ramesh
Pattern Recognition, 2002, pp. 818--821

Detecting tables in html documents
Y Wang, J Hu
Document Analysis Systems V, 609--614, Springer, 2002

A machine learning based approach for table detection on the web
Y Wang, J Hu
Proceedings of the 11th international conference on World Wide Web, pp. 242--250, 2002

2001

Combined-media video tracking for summarization
J Hu, J Zhong, A Bagga
Proceedings of the ninth ACM international conference on Multimedia, pp. 502--505, 2001

EXTRACTION OF KEY FRAMES FROM VIDEOS BY OPTIMAL COLOR COMPOSITION MATCHING AND POLYGON SIMPLIFICATION
L J L D Wildt de, J Hu
2001 IEEE Fourth Workshop on Multimedia Signal Processing: October 3-5, 2001, Cannes, France, pp. 245

Table structure recognition and its evaluation
J Hu, R Kashi, D Lopresti, G Wilfong
Proceedings of Document Recognition and Retrieval VIII, pp. 44--55, 2001

Flexible web document analysis for delivery to narrow-bandwidth devices
G Penn, J Hu, H Luo, R McDonald
Document Analysis and Recognition, 2001, pp. 1074--1078

Why table ground-truthing is hard
J. Hu, R. Kashi, D. Lopresti, G. Nagy, G. Wilfong
Document Analysis and Recognition, 2001. Proceedings. Sixth International Conference on, pp. 129--133

2000

Document Recognition and Retrieval VIII (Proceedings Volume)
T Kanungo, C H Lee, J Czorapinski, I Bella, S H Cha, S N Srihari, O G Okun, A Vesanen, M Pietikainen, J C Handley, others
2000 - spie.org

Comparison and classification of documents based on layout similarity
J Hu, R Kashi, G Wilfong
Information Retrieval 2(2), 227--243, Springer, 2000

A system for understanding and reformulating tables
J Hu, R Kashi, D Lopresti, G Wilfong
Proceedings of the Fourth IAPR International Workshop on Document Analysis Systems, pp. 361--372, 2000

Medium-independent table detection
J Hu, R Kashi, D Lopresti, G Wilfong
SPIE Document Recognition and Retrieval VII, pp. 291--302, 2000

Writer independent on-line handwriting recognition using an HMM approach
J Hu, S Gek Lim, M K Brown
Pattern Recognition 33(1), 133--147, Elsevier, 2000

Optimal color composition matching of images
J Hu, A Mojsilovic
Pattern Recognition, 2000, pp. 47--50

Matching and retrieval based on the vocabulary and grammar of color patterns
A Mojsilovic, J Kovacevic, J Hu, R J Safranek, S K Ganapathy
Multimedia Computing and Systems, 1999, pp. 38--54, IEEE, 2000

A method for color content matching of images
A Mojsilovic, J Hu
Multimedia and Expo, 2000, pp. 649--652

Extraction of perceptually important colors and similarity measurement for image matching
A Mojsilovic, J Hu
Image Processing, 2000, pp. 61--64

1999

Retrieval of color patterns based on perceptual dimensions of texture and human similarity rules
A. Mojsilovic, J. Kovacevic, J. Hu, R.J. Safranek, K. Ganapathy
Proc. SPIE Human Vision and Electronic Imaging, pp. 441--452, 1999

Document image layout comparison and classification
J. Hu, R. Kashi, G. Wilfong
Document Analysis and Recognition, 1999. ICDAR'99. Proceedings of the Fifth International Conference on, pp. 285--288

Table detection across multiple media
J Hu, R Kashi, D Lopresti, G Wilfong
Proceedings of the Workshop on Document Layout Interpretation and its Applications, 1999

Perceptually based color texture features and metrics for image retrieval
A Mojsilovic, J Hu, R J Safranek
Image Processing, 1999, pp. 588--592

Retrieval of color patterns based on perceptual dimensions of texture and human similiarity rules
A Mojsilovic, J Kovacevic, J Hu, R J Safranek, S K Ganapathy
Proceedings of SPIE, pp. 441, 1999

Integrating geometrical and linguistic analysis for email signature block parsing
H Chen, J Hu, R W Sproat
ACM Transactions on Information Systems (TOIS) 17(4), 343--366, ACM, 1999

User interface for a PCS smart phone
S Narayanaswamy, J Hu, R Kashi
Multimedia Computing and Systems, 1999, pp. 777--781

Document classification using layout analysis
J Hu, R Kashi, G Wilfong
Database and Expert Systems Applications, 1999, pp. 556--560

1998

Combining data and voice communications on digital wireless networks
S Narayanaswamy, J Hu, R Kashi
Global Telecommunications Conference, 1998, pp. 1449--1454

E-mail signature block analysis
H Chen, J Hu, R W Sproat
Pattern Recognition, 1998, pp. 1153--1156, Google Patents
US Patent 6,373,985

HMM based writer independent on-line handwritten character and word recognition
J Hu, S G Lim, M K Brown
Proc, pp. 143--155, 1998

Emu: An e-mail preprocessor for text-to-speech
R Sproat, J Hu, H Chen
Multimedia Signal Processing, 1998 IEEE Second Workshop on, pp. 239--244

A Hidden Markov Model approach to online handwritten signature verification
R Kashi, J Hu, WL Nelson, W Turin
International Journal on Document Analysis and Recognition 1(2), 102--109, Springer, 1998

Using data on digital cellular and PCS voice networks
S Narayanaswamy, J Hu, R S Kashi
Bell Labs technical journal 3(2), 58--75, Wiley Online Library, 1998

1997

Next-generation multimedia messaging
H S Baird, J Hu, R S Kashi
Multimedia Signal Processing, 1997, pp. 501--506

Size and orientation normalization of on-line handwriting using Hough transform
A S Rosenthal, J Hu, M K Brown
Acoustics, Speech, and Signal Processing, 1997, pp. 3077--3080

Combining high-level features with sequential local features for on-line handwriting recognition
J Hu, A Rosenthal, M Brown
Image Analysis and Processing, pp. 647--654, 1997

Language modeling using stochastic automata with variable length contexts
J Hu, W Turin, M K Brown
Computer Speech \& Language 11(1), 1--16, Elsevier, 1997

On-line handwritten signature verification using hidden Markov model features
R S Kashi, J Hu, WL Nelson, W Turin
Document Analysis and Recognition, 1997, pp. 253--257

1996

Language modeling with stochastic automata
J Hu, W Turin, M K Brown
Spoken Language, 1996, pp. 406--409

A hierarchical approach to efficient curvilinear object searching
J Hu, T Pavlidis
Computer vision and image understanding 63(2), 208--220, Elsevier, 1996

On-line handwriting recognition with constrained n-best decoding
J Hu, M K Brown
Pattern Recognition, 1996, pp. 23--27

HMM based online handwriting recognition
H Jianying, MK Brown, W Turin
IEEE Trans. Pattern Anal. Mach. Intell 18(10), 1039--1045, IEEE, 1996

1995

Use of segmental features in HMM based handwriting recognition
J Hu, M K Brown, W Turin
Systems, Man and Cybernetics, 1995, pp. 2778--2782

Invariant features for HMM based on-line handwriting recognition
J Hu, M Brown, W Turin
Image Analysis and Processing, pp. 588--593, 1995

1994

Handwriting recognition with hidden Markov models and grammatical constraints
J Hu, M K Brown, W Turin
Proc, pp. 195--205, 1994

1993

Feature extraction and indexing techniques for pictorial database retrieval
J Hu
1993 - dl.acm.org, State University of New York at Stony Brook

Computer assisted tracing of faint roads in satellite images
B Sakoda, J Hu, T Pavlidis
1993 ACSM/ASPRS Annual Convention \& Exposition, New Orleans, LA, pp. 311--323

1992

Interactive road finding for aerial images
J Hu, B Sakoda, T Pavlidis
Applications of Computer Vision, Proceedings, 1992, pp. 56--63

1991

Function plotting using conic splines
J Hu, T Pavlidis
Computer Graphics and Applications, IEEE 11(1), 89--94, IEEE, 1991

Year Unknown

â€œInformation Boostingâ€ Methodologies for Multiple Data Sources in Workforce Management
Hu, Jianying and Lu, Yingdong and Mojsilovic, Aleksandra and Radovanovic, Ana and Squillante, Mark S
J Hu, Y Lu, A Mojsilovic, A Radovanovic, MS Squillante, 0

A Resource Planning Model for Professional Services Organizations
Balaji, Kannan and DeSouza, Robert and Goh, Mark and Eck, Brian T and Hu, Jianying and Ray, Bonnie
K Balaji, R DeSouza, M Goh, BT Eck, J Hu, B Ray, 0

Discover Temporal Dynamics of Biomarkers in Predictive Modeling with Longitudinal Data
Zhou, Jiayu and Sun, Jimeng and Wang, Fei and Hu, Jianying and Ebadollahi, Shahram and Ye, Jieping
Citeseer, Citeseer, 0

Appendix to â€œTowards Drug Repositioning: A Unified Computational Framework for Integrating Multiple Aspects of Drug Similarity and Disease Similarityâ€
Zhang, Ping and Wang, Fei and Hu, Jianying
Citeseer, Citeseer, 0

Dydruma: A Dynamic Drug Map for Exploring the Associations Among Drug Therapeutic Indications and Side-Effects
Wang, Fei and Zhang, Ping and Cao, Nan and Hu, Jianying and Sorrentino, Robert
astro.temple.edu, 0

A METHOD FOR COLOR CONTENT MATCHING OF
A. Mojsilovic, J. Hu
reference.kfupm.edu.sa, 0

Projects and Groups

Center for Computational Health

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