An experiment on the impact of predictive analytics on kidney offers acceptance decisions.

Because of the breadth of factors that might affect kidney transplant decisions to accept an organ or wait for another, presumably "better" offer, a high degree of heterogeneity in decision making exists among transplant surgeons and hospitals. These decisions do not typically include objective predictions regarding the future availability of equivalent or better-quality organs or the likelihood of patient death while waiting for another organ. To investigate the impact of displaying such predictions on organ donation decision making, we conducted a statistically designed experiment involving 53 kidney transplant professionals, in which kidney offers were presented via an online application and systematically altered to observe the effects on decision making. We found that providing predictive analytics for time-to-better offers and patient mortality improved decision consensus and decision-maker confidence in their decisions. Providing a visual display of the patient's mortality slope under accept/reject conditions shortened the time-to-decide but did not have an impact on the decision itself. Presenting the risk of death in a loss frame as opposed to a gain frame improved decision consensus and decision confidence. Patient-specific predictions surrounding future organ offers and mortality may improve decision quality, confidence, and expediency while improving organ utilization and patient outcomes.

The connectome of an insect brain.

Brains contain networks of interconnected neurons and so knowing the network architecture is essential for understanding brain function. We therefore mapped the synaptic-resolution connectome of an entire insect brain (Drosophila larva) with rich behavior, including learning, value computation, and action selection, comprising 3016 neurons and 548,000 synapses. We characterized neuron types, hubs, feedforward and feedback pathways, as well as cross-hemisphere and brain-nerve cord interactions. We found pervasive multisensory and interhemispheric integration, highly recurrent architecture, abundant feedback from descending neurons, and multiple novel circuit motifs. The brain's most recurrent circuits comprised the input and output neurons of the learning center. Some structural features, including multilayer shortcuts and nested recurrent loops, resembled state-of-the-art deep learning architectures. The identified brain architecture provides a basis for future experimental and theoretical studies of neural circuits.