Research

HAC faculty engage in bi-directional healthcare, nursing science, and biomedical research activities on a broad range of applications. Methods and techniques developed by HAC investigators are tested and validated on specific pilot projects and then translated into clinical settings. HAC investigators provide consultation on innovative approaches (e.g., visualization, connectomics, data mining and classification, pattern excenttraction, motion-charts, computable phenotypes, predictive big data analytics). For interrogating complex studies involving information obtained from multiple-sources (e.g., unstructured clinical notes, imaging, genetics, cognitive assessments), HAC faculty provide resources to manage, model and interpret the data. Below are some examples of case-studies, involving big healthcare data, that HAC faculty have recently investigated and published in peer-reviewed journals.

Big Alzheimer’s Disease Data

ADNI Archive

www.adni-info.org

Types of Data

Clinical data: demographics, clinical assessments, cognitive assessments
Imaging data: sMRI, fMRI, DTI, PiB/FDG PET
Genetics data: Ilumina SNP genotyping
Chemical biomarker: lab tests, proteomics

Sample Size

Each data modality comes with a different number of cohorts. Generally, 200 ≤ N ≤ 2,500 .
For instance, we had previously conducted ADNI studies with N > 500 [PMIDs: 26918190, 26444770, 25940587]

Clinical Relevance

ADNI provides interesting data modalities, multiple cohorts (e.g., early-onset, mild, and severe dementia, controls) that allow effective model training and validation

NACC Archive

www.alz.washington.edu

Types of Data

Clinical data: treatments, diagnosis, clinical assessments, cognitive tests
Imaging data: sMRI, fMRI
Genetics data: SNP genotypes
Biospecimen: blood, buffy coat, brain tissue
Demographics: family history, phenotypes

Sample Size

This collection includes over 5,000 unique subjects with an average of 6 years of follow-up. Not all data elements/cases are complete

Clinical Relevance

NACC data facilitates the exploration of GWAS and diverse phenotypes. These data provide means to do exploratory neuropathological studies of dementia and cognitive decline, accounting for risk alleles like APOE ε4 and protective alleles APOE ε2

Questions

• Identify the relation between observed clinical phenotypes and expected behavior
• Prognosticate future cognitive decline (3-12 months prospectively) as a function of imaging data and clinical assessment (both model-based and model-free machine learning prediction methods will be used)
• Derive and interpret the classifications of subjects into clusters using the harmonized and aggregated data from multiple sources

Big Autism Research Data

ABIDE

www.goo.gl/QC6poj

Types of Data

sMRI (N=476)
fMRI (N=1,159)
Meta-data (CSV)
Pheno (lots of missing data)
Dictionary: ABIDE_Disctionary_V1.02

Sample Size

ABIDE (Autism Brain Imaging Data Exchange) includes functional and structural brain imaging data collected at 23 international brain imaging laboratories
and N = 1,100 and k= 2,100

Clinical Relevance

Aims to accelerate the understanding of the neural bases of autism. ABIDE ultimate goal is to facilitate discovery science and comparisons across samples

Questions

• Identify the specific neural processes discriminating between HCs and ASDs subjects by examining performance brain activation (fMRI) and brain anatomy (sMRI)
• Examine disease (ASD) heterogeneity, e.g., symptoms, symptom severity, differences in IQ, total brain volume, surface area, GM cortical thickness, and psychiatric comorbidity
• Examine the effects of demographic (e.g., age, gender) and phenotypic/clinical factors
• Examine differences (HC vs. ASD) in various functional domains (visual, executive, language, social processing, cognition), DSM-Dx, hand, FIQ/VIQ/PIQ, ADI-R Social/Verbal/RRB/Onset, ADOS, SRS, SCQ, AQ, EyesStatus rs-fMRI

Publications

• Zhou, Y, Zhao, Zhou, N, Zhao, Yi, Marino, S, Wang, T, Sun, H, Toga, AW, Dinov, ID. (2019). Predictive Big Data Analytics using the UK Biobank Data, Scientific Reports, 9(1): 6012, https://doi.org/10.1038/s41598-019-41634-y.
• Dinov, ID. (2019) Quant data science meets dexterous artistry, International Journal of Data Science and Analytics, 7(2):81–86, https://doi.org/10.1007/s41060-018-0138-6.
• Marino, S, Zhou, N, Zhao, Yi, Wang, L, Wu Q, and Dinov, ID. (2019) DataSifter: Statistical Obfuscation of Electronic Health Records and Other Sensitive Datasets, Journal of Statistical Computation and Simulation, 89(2): 249–271, http://doi.org/10.1080/00949655.2018.1545228.
• Naylor KB, Tootoo J, Yakusheva O, Shipman SA, Bynum JPW, et al. (2019) Geographic variation in spatial accessibility of U.S. healthcare providers. PLOS ONE 14(4): e0215016. https://doi.org/10.1371/journal.pone.0215016
• Sirihorachai, R., Aebersold, M. L., Sarter, N. B., Harris, M., Marsh, V., & Redman, R. W. (2018, November). Examining interruptions in the operating room using simulation. Clinical Simulation in Nursing, 24(C), 14-24. https://doi.org/10.1016/j.ecns.2018.08.004.
• Bushnell, C. D., Chaturvedi, S., Gage, K. R., Herson, P. S., Hurn, P. D., Jiménez, M. C., … Rundek, T. (2018). Sex differences in stroke: Challenges and opportunities. Journal of Cerebral Blood Flow & Metabolism, 38(12), 2179–2191. https://doi.org/10.1177/0271678X18793324
• Yakusheva O, Hoffman GJ. Does a Reduction in Readmissions Result in Net Savings for Most Hospitals? An Examination of Medicare’s Hospital Readmissions Reduction Program. Med Care Res Rev. 2018 Aug 24:1077558718795745. http://doi.org/10.1177/1077558718795745.
• Hoffman GJ, Tilson S, Yakusheva O. The Financial Impact of an Avoided Readmission for Teaching and Safety-Net Hospitals Under Medicare’s Hospital Readmission Reduction Program. Med Care Res Rev. 2018 Aug 24:1077558718795733. https://doi.org/10.1177/1077558718795733.
• Abbott P, Banerjee T, Aruquipa Yujra AC, Xie B, Piette J (2018) Exploring chronic disease in Bolivia: A cross-sectional study in La Paz. PLOS ONE 13(2): e0189218. https://doi.org/10.1371/journal.pone.0189218
• Dinov, ID, 2018. Data Science and Predictive Analytics: Biomedical and Health Applications using R, Springer, Computer Science, ISBN 978-3-319-72346-4.
• Marino, S, Zhou, N, Zhao, Yi, Wang, L, Wu Q, and Dinov, ID. (2018) DataSifter: Statistical Obfuscation of Electronic Health Records and Other Sensitive Datasets, Journal of Statistical Computation and Simulation, pp: 1-23, DOI: 10.1080/00949655.2018.1545228.
• Kalinin, AA, Allyn-Feuer, A, Ade, A, Fon, GV, Meixner, W, Dilworth, D, Husain, SS, de Wett, JR, Higgins, GA, Zheng, G, Creekmore, A, Wiley, JW, Verdone, JA, Veltri, RW, Pienta, KJ, Coffey, DS, Athey, BD, and Dinov, ID. (2018) 3D Shape Modeling for Cell Nuclear Morphological Analysis and Classification, Scientific Reports, 8(1): 13658.
• Marino S, Xu J, Zhao Y, Zhou N, Zhou Y, Dinov, ID. (2018) Controlled feature selection and compressive big data analytics: Applications to biomedical and health studies, PLoS ONE 13(8): e0202674, DOI: 10.1371/journal.pone.0202674
• Zheng G, Kalinin AA, Dinov, ID, Meixner W, Zhu S, Wiley JW. (2018) Hypothesis: Caco‐2 cell rotational 3D mechanogenomic turing patterns have clinical implications to colon crypts, J Cell Mol Med. 2018;00:1–6, DOI: 10.1111/jcmm.13853.
• Gao C, Sun H, Wang T, Tang M, Bohnen NI, Müller MLTM, Herman, T, Giladi, N. Kalinin, A, Spino, C, Dauer, W, Hausdorff, JM, Dinov, ID. (2018) Model-based and Model-free Machine Learning Techniques for Diagnostic Prediction and Classification of Clinical Outcomes in Parkinson’s Disease, Scientific Reports, 8(1):7129. DOI: 10.1038/s41598-018-24783-4 2018.
• Dinov, ID, Palanimalai, S, Khare, A, and Christou, N. (2018) Randomization‐based Statistical Inference: A resampling and simulation infrastructure, Teaching Statistics, 40: 64–73. DOI: 10.1111/test.12156.
• Sepehrband, F., Lynch, K.M., Cabeen, R.P., González-Zacarías, C., Zhao, L., D’Arcy, M., Kesselman, C., Herting, M.M., Dinov, I.D., Toga, A.W., Clark, K.A., 2018. Neuroanatomical morphometric characterization of sex differences in youth using statistical learning, NeuroImage, 172:217–227, DOI: 10.1016/j.neuroimage.2018.01.065.
• Kalinin, AA, Higgins, GA, Reamaroon, N, Soroushmehr, SM, Allyn-Feuer, A, Dinov, ID, Najarian, K, Athey, BD. (2018). Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification, Pharmacogenomics 19:7, 629-650.
• Kapinos KA, Yakusheva O, Weiss M. Cesarean deliveries and maternal weight retention. BMC Pregnancy Childbirth. 2017 Oct 4;17(1):343. DOI: 10.1186/s12884-017-1527-x.
• Yakusheva O, Costa DK, Weiss M. Patients Negatively Impacted by Discontinuity of Nursing Care During Acute Hospitalization. Med Care. 2017 Apr;55(4):421-427. doi: 10.1097/MLR.0000000000000670.
• Yakusheva O, Kapinos K, Weiss M. Maternal Weight after Childbirth versus Aging-Related Weight Changes.Womens Health Issues. 2017 Mar – Apr;27(2):174-180. doi: 10.1016/j.whi.2016.12.001.
• Yakusheva O, Weiss M. Rankings matter: nurse graduates from higher-ranked institutions have higher productivity. BMC Health Serv Res. 2017 Feb 13;17(1):134. doi: 10.1186/s12913-017-2074-x.
• Yakusheva O. Health Spillovers among Hospital Patients: Evidence from Roommate Assignments. American Journal of Health Economics. 2017; 3(1):76-107
• Amiri, S and Dinov, ID. 2017. msktuple: An integrated R library for alignment-free multiple sequence k-tuple analysis, Chemometrics and Intelligent Laboratory Systems 168:84-88, DOI: 10.1016/j.chemolab.2017.07.012.
• Huang Z, Zhang H, Boss J, Goutman SA, Mukherjee B, Dinov ID, Guan, Y. (2017) Complete hazard ranking to analyze right-censored data: An ALS survival study . PLoS Comput Biol 13(12): e1005887, DOI: 10.1371/journal.pcbi.1005887.
• Stelmokas J, Yassay L, Giordani B, Dodge H, Dinov, ID, Bhaumik A, Sathian, K, Hampstead, BM. 2017. Translational MRI Volumetry with NeuroQuant: Effects of Version and Normative Data on Relationships with Memory Performance in Healthy Older Adults and Patients with Mild Cognitive Impairment, Journal of Alzheimer’s disease 60(4):1499-1510, DOI: 10.3233/JAD-170306.
• Kalinin AA, Allyn-Feuer A, Ade A, Fon G-V, Meixner W, Dilworth D, de Wet, JR, Higgins, GA, Zheng, G, Creekmore, A, Wiley, JW, Verdone, JE, Veltri, RW, Pienta, KJ, Coffey, DS, Athey, BD, Dinov, ID. 2017. 3D cell nuclear morphology: microscopy imaging dataset and voxel-based morphometry classification results, bioRxiv 168:84-88, DOI: 10.1101/208207.
• Kalinin, AA, Palanimalai, S, Dinov, ID. 2017. SOCRAT Platform Design: A Web Architecture for Interactive Visual Analytics Applications. In Proceedings of HILDA’17, Chicago, IL, USA, May 14, 2017, 6 pages. DOI: 10.1145/3077257.3077262.
• Dinov, ID, Heavner, B, Tang, M, Glusman, G, Chard, K, Darcy, M, Madduri, R, Pa, J, Spino, C, Kesselman, C, Foster, I, Deutsch, EW, Price, ND, Van Horn, JD, Ames, J, Clark, K, Hood, L, Hampstead, BM, Dauer, W, and Toga, AW. (2016) Predictive Big Data Analytics: A Study of Parkinson’s Disease using Large, Complex, Heterogeneous, Incongruent, Multi-source and Incomplete Observations. PLoS ONE, 11(8):1-28, e0157077. DOI: 10.1371/journal.pone.0157077.
• Amiri, S, Dinov, ID. (2016) Comparison of Genomic Data via Statistical Distribution. Journal of Theoretical Biology, 407:318–327. DOI: 10.1016/j.jtbi.2016.07.032.
• Fu KA, Nathan R, Dinov I, Li J, Toga AW. (2016) T2-Imaging Changes in the Nigrosome-1 Relate to Clinical Measures of Parkinson’s Disease. Frontiers in Neurology, 7(174):1-27. DOI: 10.3389/fneur.2016.00174.
• Dinov, ID. (2016) Methodological challenges and analytic opportunities for modeling and interpreting Big Healthcare Data, GigaScience, 5(12):1-15, DOI: 10.1186/s13742-016-0117-6
• Dinov, ID. (2016) Health Science Analytics: Data- and Technology-Driven Approaches for Addressing Health Care Challenges, Advancing Well-Being, and Enhancing Nursing Education, in Shaping Nursing Science and Improving Health: The Michigan Legacy, (ed.) Shake Ketefian, DOI: 10.3998/mpub.9497632
• Dinov, ID. (2016) Volume and Value of Big Healthcare Data, Journal of Medical Statistics and Informatics, 4(3):1-7, DOI: 10.7243/2053-7662-4-3
• Husain, SS, Kalinin, A, Truong, A, Dinov, ID. (2015) SOCR data dashboard: an integrated big data archive mashing medicare, labor, census and econometric information. Journal of Big Data, 2(13):1-18. DOI: 10.1186/s40537-015-0018-z
• Moon, S., Dinov, ID, Kim, J, Zamanyan, A, Hobel, S, Thompson, PM, Toga, AW (2015) Structural Neuroimaging Genetics Interactions in Alzheimer’s Disease. Journal of Alzheimer’s Disease, 48(4):1051-63, doi: 10.3233/JAD-150335 (PMID: 26444770)
• Davis MA, Zheng K, Liu Y, Levy H. Public Response to Obamacare on Twitter, J Med Internet Res 2017;19(5):e167, DOI: 10.2196/jmir.6946
• Matthew A. Davis, Antonio J. Signes-Pastor, Maria Argos, Francis Slaughter, Claire Pendergrast, Tracy Punshon, Anala Gossai, Habibul Ahsan, Margaret R. Karagas, Assessment of human dietary exposure to arsenic through rice, Science of The Total Environment, Volume 586, 2017, Pages 1237-1244, ISSN 0048-9697, http://dx.doi.org/10.1016/j.scitotenv.2017.02.119.
• Abbott, P., & Xie, B. (2016). Big data in nursing research. In Bloch, J., Clark, C., & Courtney, M. (Eds), Practice-based clinical inquiry in nursing for Ph.D and DNP research: Looking beyond traditional methods (pp. 93-116). New York, NY: Springer Publishing Company, Ltd.
• Lee, S. M., Abbott, P., & Johantgen, M. (2005). A comparison of logistic regression and Bayesian networks for outcomes research in large datasets. Nursing Research, 54(2), 133-8.
• Abbott, P., & Lee, S. (2005). Data mining & knowledge discovery. In Saba, V. & McCormick K. (Eds), Essentials of nursing informatics (4th Ed.), (pp. 469-479). New York, NY: McGraw Hill.
• Lee, Sun Mi, & Abbott, P. (2003). Bayesian networks for knowledge discovery in large datasets: Basics for nurse researchers. Journal of Biomedical Informatics, 36(4-5), 389-399. http://dx.doi.org/10.1016/j.jbi.2003.09.022