Bridging Data Gaps: Predicting Sub-national Maternal Mortality Rates in Kenya Using Machine Learning Models.

Introduction Maternal mortality remains a critical global health issue, with ongoing efforts to reduce its incidence as part of international health priorities. Kenya, a sub-Saharan country that has a disproportionate number of maternal mortality is likely to miss this target unless evidence-based interventions are deployed. The paucity of reliable maternal health data calls for the development of alternative predictive models to complement the impaired civil registration system and the aperiodic national surveys.

Using self-supervised feature learning to improve the use of pulse oximeter signals to predict paediatric hospitalization.

: The success of many machine learning applications depends on knowledge about the relationship between the input data and the task of interest (output), hindering the application of machine learning to novel tasks. End-to-end deep learning, which does not require intermediate feature engineering, has been recommended to overcome this challenge but end-to-end deep learning models require large labelled training data sets often unavailable in many medical applications. In this study, we trained self-supervised learning (SSL) models for automatic feature extraction from raw photoplethysmography (PPG) obtained using a pulse oximeter, with the aim of predicting paediatric hospitalization.

Using Machine Learning Methods Incorporating Individual Reader Annotations to Classify Paediatric Chest Radiographs in Epidemiological Studies.

: Epidemiological studies that involve interpretation of chest radiographs (CXRs) suffer from inter-reader and intra-reader variability. Inter-reader and intra-reader variability hinder comparison of results from different studies or centres, which negatively affects efforts to track the burden of chest diseases or evaluate the efficacy of interventions such as vaccines. This study explores machine learning models that could standardize interpretation of CXR across studies and the utility of incorporating individual reader annotations when training models using CXR data sets annotated by multiple readers.

Draft Genome Sequence of an Enterotoxigenic Escherichia coli Strain Carrying Genes for Colonization Surface Antigen 13 and a Heat-Labile Toxin.

Here, we report the draft genome of ESEI_597, an enterotoxigenic Escherichia coli (ETEC) strain harboring genes encoding colonization surface antigen 13 (CS13) and a heat-labile toxin. The ESEI_597 strain was isolated from an 8-month-old child living in Korogocho, Kenya, in 2013.

Levels of PM and PM and Respiratory Health Impacts on School-Going Children in Kenya.

Background: The respiratory system of children is vulnerable to exposure to particulate matter (PM) with a diameter of less than 2.5 and 10 μm (PM and PM) or even lower.

Objective: This study assessed PM and PM levels and respiratory health impacts on children in schools located in an industrialized suburb in Kenya.

Exploration of miRNA families for hypotheses generation.

Technological improvements have resulted in increased discovery of new microRNAs (miRNAs) and refinement and enrichment of existing miRNA families. miRNA families are important because they suggest a common sequence or structure configuration in sets of genes that hint to a shared function. Exploratory tools to enhance investigation of characteristics of miRNA families and the functions of family-specific miRNA genes are lacking.