Entity Aware Modelling: A Survey

Personalized prediction of responses for individual entities caused by external drivers is vital across many disciplines. Recent machine learning (ML) advances have led to new state-of-the-art response prediction models. Models built at a population level often lead to sub-optimal performance in many personalized prediction settings due to heterogeneity in data across entities (tasks). In personalized prediction, the goal is to incorporate inherent characteristics of different entities to improve prediction performance. In this survey, we focus on the recent developments in the ML community for such entity-aware modeling approaches. ML algorithms often modulate the network using these entity characteristics when they are readily available. However, these entity characteristics are not readily available in many real-world scenarios, and different ML methods have been proposed to infer these characteristics from the data. In this survey, we have organized the current literature on entity-aware modeling based on the availability of these characteristics as well as the amount of training data. We highlight how recent innovations in other disciplines, such as uncertainty quantification, fairness, and knowledge-guided machine learning, can improve entity-aware modeling.Comment: Submitted to IJCAI, Survey Trac

An intelligent interface for integrating climate, hydrology, agriculture, and socioeconomic models

Understanding the interactions between natural processes and human activities poses major challenges as it requires the integration of models and data across disparate disciplines. It typically takes many months and even years to create valid end-to-end simulations as different models need to be configured in consistent ways and generate data that is usable by other models. MINT is a novel framework for model integration that captures extensive knowledge about models and data and aims to automatically compose them together. MINT guides a user to pose a well-formed modeling question, select and configure appropriate models, find and prepare appropriate datasets, compose data and models into end-to-end workflows, run the simulations, and visualize the results. MINT currently includes hydrology, agriculture, and socioeconomic models.Office of the VP for Researc

Clinical features as predictors of bacteraemia in febrile children

Objective: To determine the prevalence, clinical features and various risk factors of bacteraemia among hospitalized febrile children aged between 3 months to 36 months, in a tertiary care center. Methods A cross-sectional, non-interventional, observational study consisting of 88 cases were included in our study and evaluated for the determination of prevalence of bacteraemia and its clinical correlates. Clinical Examination was then carried out and temperature, weight, length, clinical state, respiratory rate, heart rate was recorded. Yale score was assessed at time of admission and recorded. It is composed of 6 clinical parameters i.e. Quality of cry, Reaction to parent, State variation, Colour, Hydration & Social response. Results Among these, blood culture was positive in 24 cases, while in 64 cases blood culture showed no growth of any pathogenic organism. Staphylococcus aureus was one of the most common pathogenic organism (25%) seen among febrile children. Conclusion Empiric antibiotic therapy must include anti-Staphylococcal antibiotic in our setting in a febrile child without apparent focus of infection. Vaccination has a definite protective role and incomplete vaccination status of a child can be regarded as a strong predictor of presence of bacteraemia