Josline Otieno

My research interest is in predictive modeling and machine learning.

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Works at

Doctoral student at Umeå School of Business, Economics and Statistics (USBE) Section: Statistics

Samhällsvetarhuset, Biblioteksgränd 6, A20212 Umeå universitet, 901 87 Umeå

I am a doctoral student in the department of Statistics. I have a background in Mathematical sciences, Applied statistics, and Biostatistics. My interest is to explore and apply innovative statistical methods to data analysis, including clinical, time-to-event, and epidemiological data. My current research aims at improving the quality of care and prediction of stroke outcome using modern machine learning algorithms. I work with both simulated and real (Riksstroke) datasets.

Developing machine learning models to predict multi-class functional outcomes and death three months after stroke in Sweden

PLOS ONE, Public Library of Science (PLoS) 2024, Vol. 19, (5)

Otieno, Josline Adhiambo; Häggström, Jenny; Darehed, David; et al.

Developing and externally validating a machine learning risk prediction model for 30-day mortality after stroke using national stroke registers in the UK and Sweden.

BMJ Open, BMJ Publishing Group Ltd 2023, Vol. 13, (11)

Wang, Wenjuan; Otieno, Josline A.; Eriksson, Marie; et al.

View publications in DiVA

I have been part of the teaching team for various courses, including Statistics for Business and Economics (undergraduate/graduate), Quantitative Research Methods for the Social Sciences (graduate/postgraduate), and Statistics in Medicine (undergraduate). Additionally, from 2017 to 2019, I served as a tutorial fellow for mathematics and statistics courses at Kenya Methodist University.