Medical Data Science

AI-driven analysis in Neuroscience/Cancer/Cardiology, combining clinical insight with complex data to advance medical research and care. 

This research area brings together key projects in medical data science, addressing both clinical and public health challenges through AI-driven methods and interdisciplinary collaboration: 

1. Therapeutic outcomes in relation to empathy in counseling 
2. CDSS ontologies for public and private healthcare systems 
3. Breast cancer in the context of racial disparities and parental smoking 
4. Genetic predictions for intervention success in cardiology 

• Opening the Black Box of family-based treatments: An Artificial Intelligence Framework to Examine Therapeutic Alliance and Therapist Empathy

Cunningham PB, Gilmore J, Naar S, Preston SD, Eubanks CF, Hubig NC, McClendon J, Ghosh S, Ryan-Pettes S 

Clin Child Fam Psychol Rev. 2023 

• Unveiling the Dynamics of Family-Based Psychotherapy Process: A Novel Multimodal Approach Leveraging Artificial Intelligence

Phillippe B. Cunningham, Jordon Gilmore, Stacy R. Ryan-Pettes, Jerome McClendon, Nina Hubig, Esther Lynch, and Sylvie Naar

NIMH under review

• Interpretable Machine Learning Predicts Cardiac Resynchronization Therapy Responses from Personalized Biochemical and Biomechanical Features 

Anamul Haque, Doug Stubbs, Nina C. Hubig, Francis G. Spinale, William J. Richardson 

BMC Medical Informatics and Decision Making 2022

• Evaluation of breast tumor morphologies from African Americans and Caucasian patients   

A. Stone, C. Kalahiki, L. Li, N. Hubig, F. Iuricich, H. Dunn  

Computational and Structural Biotechnology Journal 2023

• Ontologies Applied in Clinical Decision Support System Rules: Systematic Review

Goli R, Hubig N, Min H, Gong Y, Sittig DF, Robinson D, Biondich P, Wright A, Nøhr C, Law T, Faxvaag A, Gimbel R, Rennert L, Jing X  

JMIR Medical Informatics 2023

• A systematic approach to configuring MetaMap for optimal performance. 

Jing X, Indani A, Hubig NC, Min H, Gong Y, Cimino JJ, Sittig D, Rennert L, Robinson D, Biondich P, Wright A, Nøhr C, Law TD, Faxvaag A, Gimbel RW 

Methods of Information in Medicine 2022 

• Keyphrase Identification Using Minimal Labeled Data with Hierarchical Context and Transfer Learning.

Goli R*, Hubig N, Min H, Gong Y, Sittig DF, Rennert L, Robinson D, Biondich P, Wright A, Nøhr C, Law T, Faxvaag A, Weaver A*, Gimbel R, Jing X 

Artificial Intelligence in Medicine under review

• Keyphrase identification with limited labeled dataset using deep active learning and domain adaptation 

Goli R*, Hubig N, Min H, Gong Y, Sittig DF, Robinson D, Biondich P, Wright A, Nøhr C, Law T, Faxvaag A, Gimbel R, Rennert L, Jing X 

AMIA 2022

• A clinical decision support system (CDSS) ontology to facilitate portable vaccination CDSS.

Jing X, Hua M, Gong Y, Cimino JJ, Robinson D, Sittig D, Biondich P, Wright A, Nøhr C, Law T, Faxvaag A, Indani A, Hubig N, Gimbel R, Rennert L.

AMIA Annu Symp Proc. 2022

• Using Semantic Web technology to leverage interoperable clinical decision support system rules: A pathway to interoperable patient. Jing X, Cimino JJ, Sittig DF, Min H, Gong Y, Boyce R, Robinson D, Biondich P, Wright A, Nøhr C, Law T, Faxvaag A, Hubig N, Goli R*, Nedunchezhiyan K*, Boobalan AS*, Orlioglu S*, Crozier C*, Rennert L, Gimbel R.

2023 Big Data Health Science Conference

• Literature on ontologies applied in clinical decision support systems demonstrates global collaborations.

Jing X, Min H, Gong Y, Biondich P, Robinson D, Law T, Nohr C, Faxvaag A, Rennert L, Hubig N, Gimbel R.

MEDINFO 2023, under review

• Influences Beyond the Genetic Code: Tobacco User’s Paternal Contribution to the Offspring’s Methylome 

C Stagnar, S Sarasua, N Hubig, D Ivancovic

NIH Environmental Health Perspectives 

• Centroid clustering of cellular lineage trees

C Khakutskyy, N Hubig, C Plant

ITBAM 2017

• A systematic review of deep learning based Cytomorphology for Leukemia Diagnosis

S Riechert, M Riechert, N Humaira, J Niklas, K Wendt, J Moritz, N Hubig

Under Review

• phenoMetabodiff-R: R package for analysis and visualization of Phenotype Microarray data: with example usage of samples with Autism Spectrum Disorder (ASD), Snyder-Robinson Syndrome (SRS), and Kosaki syndrome

R Pauli, M.Iqbal, N.Hubig, S. Sarasua, L.Boccutto

Under Review