Elaine O. Nsoesie

Lyse Naomi Wamba Momo, Nyalleng Moorosi, Elaine O. Nsoesie et al.

Inpatient length of stay (LoS) is an important managerial metric which if known in advance can be used to efficiently plan admissions, allocate resources and improve care. Using historical patient data and machine learning techniques, LoS prediction models can be developed. Ethically, these models can not be used for patient discharge in lieu of unit heads but are of utmost necessity for hospital management systems in charge of effective hospital planning. Therefore, the design of the prediction system should be adapted to work in a true hospital setting. In this study, we predict early hospital LoS at the granular level of admission units by applying domain adaptation to leverage information learned from a potential source domain. Time-varying data from 110,079 and 60,492 patient stays to 8 and 9 intensive care units were respectively extracted from eICU-CRD and MIMIC-IV. These were fed into a Long-Short Term Memory and a Fully connected network to train a source domain model, the weights of which were transferred either partially or fully to initiate training in target domains. Shapley Additive exPlanations (SHAP) algorithms were used to study the effect of weight transfer on model explanability. Compared to the benchmark, the proposed weight transfer model showed statistically significant gains in prediction accuracy (between 1% and 5%) as well as computation time (up to 2hrs) for some target domains. The proposed method thus provides an adapted clinical decision support system for hospital management that can ease processes of data access via ethical committee, computation infrastructures and time.

Nuredin Ali Abdelkadir, Tianling Yang, Shivani Kapania et al.

Content moderators review disturbing content to protect social media users, often at significant cost to their mental health. Recent reports document the mental health conditions of African moderators as notably problematic. Beyond the content itself, what factors contribute to the deteriorating mental health of these workers? We surveyed 134 moderators across Africa to understand their mental health and interviewed 15 moderators to contextualize their experiences. We found that African moderators suffer from high psychological distress and lower well-being compared to moderators in other areas. Former moderators showed significantly higher distress levels, demonstrating long term impact that extends beyond their moderation work. Our interviews showed that systemic and structural labor conditions contribute to moderators' severe psychological distress and diminished mental well-being. Corporate wellness programs promoted by platforms were found ineffective and inadequate. We discuss how this requires holistic attention and structural solutions by all involved parties to improve moderators' mental health.

Kaihong Wang, Mona Jalal, Sankara Jefferson et al.

Monitoring population-level changes in diet could be useful for education and for implementing interventions to improve health. Research has shown that data from social media sources can be used for monitoring dietary behavior. We propose a scrape-by-location methodology to create food image datasets from Instagram posts. We used it to collect 3.56 million images over a period of 20 days in March 2019. We also propose a scrape-by-keywords methodology and used it to scrape ~30,000 images and their captions of 38 Kenyan food types. We publish two datasets of 104,000 and 8,174 image/caption pairs, respectively. With the first dataset, Kenya104K, we train a Kenyan Food Classifier, called KenyanFC, to distinguish Kenyan food from non-food images posted in Kenya. We used the second dataset, KenyanFood13, to train a classifier KenyanFTR, short for Kenyan Food Type Recognizer, to recognize 13 popular food types in Kenya. The KenyanFTR is a multimodal deep neural network that can identify 13 types of Kenyan foods using both images and their corresponding captions. Experiments show that the average top-1 accuracy of KenyanFC is 99% over 10,400 tested Instagram images and of KenyanFTR is 81% over 8,174 tested data points. Ablation studies show that three of the 13 food types are particularly difficult to categorize based on image content only and that adding analysis of captions to the image analysis yields a classifier that is 9 percent points more accurate than a classifier that relies only on images. Our food trend analysis revealed that cakes and roasted meats were the most popular foods in photographs on Instagram in Kenya in March 2019.

Saurav Ghosh, Prithwish Chakraborty, Elaine O. Nsoesie et al.

In retrospective assessments, internet news reports have been shown to capture early reports of unknown infectious disease transmission prior to official laboratory confirmation. In general, media interest and reporting peaks and wanes during the course of an outbreak. In this study, we quantify the extent to which media interest during infectious disease outbreaks is indicative of trends of reported incidence. We introduce an approach that uses supervised temporal topic models to transform large corpora of news articles into temporal topic trends. The key advantages of this approach include, applicability to a wide range of diseases, and ability to capture disease dynamics - including seasonality, abrupt peaks and troughs. We evaluated the method using data from multiple infectious disease outbreaks reported in the United States of America (U.S.), China and India. We noted that temporal topic trends extracted from disease-related news reports successfully captured the dynamics of multiple outbreaks such as whooping cough in U.S. (2012), dengue outbreaks in India (2013) and China (2014). Our observations also suggest that efficient modeling of temporal topic trends using time-series regression techniques can estimate disease case counts with increased precision before official reports by health organizations.