Katherine E. Niehaus

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BACKGROUND Diagnosing drug-resistance remains an obstacle to the elimination of tuberculosis. Phenotypic drug-susceptibility testing is slow and expensive, and commercial genotypic assays screen only common resistance-determining mutations. We used whole-genome sequencing to characterise common and rare mutations predicting drug resistance, or consistency(More)
After psychological trauma, why do some only some parts of the traumatic event return as intrusive memories while others do not? Intrusive memories are key to cognitive behavioural treatment for post-traumatic stress disorder, and an aetiological understanding is warranted. We present here analyses using multivariate pattern analysis (MVPA) and a machine(More)
Clinical data management systems typically provide caregiver teams with useful information, derived from large, sometimes highly heterogeneous, data sources that are often changing dynamically. Over the last decade there has been a significant surge in interest in using these data sources, from simply re-using the standard clinical databases for event(More)
OBJECTIVES To review how health informatics systems based on machine learning methods have impacted the clinical management of patients, by affecting clinical practice. METHODS We reviewed literature from 2010-2015 from databases such as Pubmed, IEEE xplore, and INSPEC, in which methods based on machine learning are likely to be reported. We bring(More)
The prevalence of antibiotic resistance in pathogens is far outpacing our ability to develop new antibiotics. This necessitates the development of diagnostic tests that can determine bacterial susceptibility. For Mycobacterium tuberculosis (MTB), this is particularly urgent given that current methods for testing susceptibility take up to two months. The(More)
Many cognitive processes are challenging to study due to their scarce occurrence. Here we demonstrate how pattern recognition and brain imaging can enhance the study of such processes by providing fast, sensitive, and non-intrusive detection of these events. This can enable efficient experimental and clinical intervention. We focus on the study of traumatic(More)
Crohn's disease (CD) is a highly heterogeneous disease, with great variation in patient severity. Using supervised machine learning techniques to predict severity from common laboratory and clinical measurements, we found that high levels of C-reactive protein and low levels of lymphocytes and albumin are important predictive factors. Building upon this(More)
1. The effects of isosmotic substitution of choline for sodium on resting tension and on relaxation after noradrenaline-induced contraction was studied in rabbit isolated aortic strips immersed in Hepes-buffered physiologic salt solution (PSS) warmed to 37 degrees C and gassed with 100% O2. 2. Isosmotic substitution of choline for sodium produced a(More)
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