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Research Interests:

kate-niehaus.jpgI am interested in the hypothesis that many disease states, as classified today, are actually due to collections of similar, but distinct, mechanistic pathway irregularities.  For instance, there is great heterogeneity across inflammatory bowel disease (IBD) patients in terms of their symptoms and disease progression - this suggests the possibility of multiple disease subtypes.  I am interested in developing tools to help us identify and characterize such patient subgroups, which can eventually allow for more targeted treatments.  

Current Project:

I use tools from the fields of machine learning and clinical informatics to examine patterns within IBD patient phenotypic characteristics and to probe how these are related to underlying genetics and gene expression patterns. 

Publications:

- Johnson, A.E.W … Niehaus, K.E., et al. “Machine learning and decision support in critical care.” Proceedings of the IEEE 104, no. 2 (2016): 444-466.

- Walker, T.M., … Niehaus, K.E., et al. “Whole-genome sequencing for prediction of Mycobacterium tuberculosis drug susceptibility and resistance: a retrospective cohort study.” Lancet Infectious Diseases15, no. 10 (2015): 1193-1202.

- Niehaus, K.E., Uhlig, H.H., Clifton, D.A., “Phenotypic characterization of Crohn’s disease severity.” In Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, pp. 7023-7026. IEEE, 2015.

- Clifton, D.A., Niehaus, K.E., Charlton, P., Colopy, G.W. “Health informatics for the clinical management of patients.” Yearbook of Medical Informatics, International Medical Informatics Association. 2015.

- Clark, I.A., Niehaus, K.E., Duff, E.P., Di Simplicio, M.C., Clifford, G.D., Mackay, C.E., Woolrich, M.W., Holmes, E.A. First steps in using machine learning on fMRI data to predict intrusive memories of traumatic film footage.” Behaviour Research and Therapy 62 (2014): 37-46.
- Niehaus, K.E., Clark, I.A., Bourne, C., Mackay, C.E., Holmes, E.A., Smith, S.M., Woolrich, M.W., Duff, E.P. Multi-voxel pattern analysis (MVPA) to enhance the study of rare cognitive events: An investigation of experimental post-traumatic stress disorder.” In 2014 International Workshop on Pattern Recognition in Neuroimaging, pp.1-4. IEEE, 2014.
 
- Niehaus, K.E., Walker, T.M., Crook, D.W., Peto, T.E.A., Clifton, D.A. Machine learning for the prediction of antibacterial susceptibility in Mycobacterium tuberculosis.” In 2014 IEEE-EMBC International Conference on Biomedical and Health Informatics (BHI), pp. 618-621. IEEE, 2014.
 
- Bavinger, J.C., Bendavid, E., Niehaus, K., et al. “Risk of Cardiovascular Disease from Antiretroviral Therapy for HIV: A Systematic Review.” PloS one 8, no. 3 (2013): e59551.