Machine Learning to Derive Complex Behaviour in Agent-Based Modellzing

The use of machine learning algorithms to enrich agent-based models has increased over the past years. This integration adds value when combining the advantages of the data-driven approach and the possibilities to explore future situations and human interventions. However, this integrating is still in its infant stage. Full integration of learning algorithms and agent-based models is often technically challenging and can make the behavioural rules of the agents less transparent. Experiments are needed in which different integration strategies are compared using the same agent-based model to determine when each of these approaches is most effective. In this paper, we present a comparison of two versions of the same cholera model. In the initial version, agent behaviour was driven directly by a learning algorithm. In our experiments, we replace this strategy by applying a learning algorithm directly on the data and implement the outcomes as behaviour rules in the model. The results showed that when the integration aims to create agents that show characteristics that are data-driven, deriving rules based on these data is a good alternative. In addition, a key element in this strategy is the dataset. A large dataset representing the behaviour of different types of agents over the complete time period is needed

Bayesian networks for spatial learning: a workflow on using limited survey data for intelligent learning in spatial agent-based models

Machine learning (ML) algorithms steer agent decisions in agent-based models (ABMs), serving as a vehicle for implementing behaviour changes during simulation runs. However, when training an ML algorithm, obtaining large sets of micro-level human behaviour data is often problematic. Information on human behaviour is often collected via surveys of relatively small sample sizes. This paper presents a methodology for training a learning algorithm to guide agent behaviour in a spatial ABM using a limited survey data sample. We apply different implementation strategies using survey data and Bayesian networks (BNs). By being grounded in probabilistic directed graphical models, BNs stand out among other learning algorithms in that they can be based on expert knowledge and/or known datasets. This paper presents four alternative implementations of data-driven BNs to support agent decisions in a spatial ABM. We differentiate between training BNs prior to, or during the simulation runs, using only survey data or a combination of survey data and expert knowledge. The four different implementations are then illustrated using a spatial ABM of cholera diffusion for Kumasi, Ghana. The results indicate that a balance between expert knowledge and survey data provides the best control over the learning process of the agents and produces the most realistic agent behaviour

Rituximab as a rescue therapy in patients with glomerulonephritis

To evaluate the use of rituximab in the treatment of severe glomerulonephritis (GN) in order to prevent progression of kidney disease toward the end stage, we designed a multicenter, retrospective study in Saudi Arabia about the efficacy and safety of the use of "off label" rituximab in a variety of severe refractory GN to conventional treatment and the progression of kidney disease for at least one year of follow-up. All the patients had kidney biopsies before treatment with rituximab, and proteinuria and glomerular filtration rate (GFR) were followed-up for the period of the study. The immediate side-effect at the time of administration of rituximab included itching in three patients, hypotension in one patient and anaphylaxis in one patient (dropped out from the study). After the administration of rituximab in 42 patients and during the first six months of therapy, 16 (38%) patients had complete remission (CR), 13 (31%) patients had partial remission (PR) and 13 (31%) patients had no remission. The mean follow-up period for the patients was 19.0 ± 6.97 months (median 18.0 months). The long-term follow-up during the study period disclosed a good hospitalization record for almost all of the patients. Membranous GN (MGN) was the largest group in the cohort (58% of the patients), and we observed CR and PR in 40% and 28% of them, respectively, which was comparable with the previous experience with rituximab in MGN patients with more CR than PR in our cohort. We conclude that our study suggests the safety and efficacy of the use of rituximab in patients with refractory GN and that larger and long-term prospective studies are required to define the role of rituximab in the different categories of these diseases