Phenotypic modelling of Crohn's disease severity: a machine learning approach by Niehaus, K

Health informatics via machine learning for the clinical management of patients. by Clifton, D, Niehaus, K, Charlton, P, Colopy, G

Machine learning and decision support in critical care by Johnson, A, Ghassemi, M, Nemati, S, Niehaus, K, Clifton, D, Clifford, G

Patient-specific physiological monitoring and prediction using structured Gaussian processes by Zhu, T, Wright Colopy, G, Macewen, C, Niehaus, K, Yang, Y, Pugh, C, Clifton, D

MVPA to enhance the study of rare cognitive events: an investigation of experimental PTSD by Niehaus, K, Clark, I, Bourne, C, Mackay, C, Holmes, E, Smith, S, Woolrich, M, Duff, E, IEEE

First steps in using machine learning on fMRI data to predict intrusive memories of traumatic film footage by Clark, I, Niehaus, K, Duff, E, Simplicio, D, Clifford, G, Smith, S, Mackay, C, Woolrich, M, Holmes, E

First steps in using machine learning on fMRI data to predict intrusive memories of traumatic film footage. by Clark, I, Niehaus, K, Duff, E, Simplicio, D, Clifford, G, Smith, S, Mackay, C, Woolrich, M, Holmes, E

Machine learning for classifying tuberculosis drug-resistance from DNA sequencing data by Yang, Y, Niehaus, K, Walker, T, Iqbal, Z, Walker, A, Wilson, D, Peto, T, Crook, D, Smith, E, Zhu, T, Clifton, D

Whole-genome sequencing for prediction of Mycobacterium tuberculosis drug susceptibility and resistance: a retrospective cohort study. by Walker, T, Kohl, T, Omar, S, Hedge, J, Del Ojo Elias, C, Bradley, P, Iqbal, Z, Feuerriegel, S, Niehaus, K, Wilson, D, Clifton, D, Kapatai, G, Ip, C, Bowden, R, Drobniewski, F, Allix-Béguec, C, Gaudin, C, Parkhill, J, Diel, R, Supply, P, Crook, D, Smith, E, Walker, A, Ismail, N, Niemann, S, Peto, T