Enhancing email text production for users with motor impairments

People with motor impairments who use a switch device to interface with computers have poor access to affordable software for email communication. The MultiMail email package was developed with government support to provide email access solutions for these users and for others with a range of disabilities. In this paper, the development of accessible on-screen keyboards and a word prediction program which facilitates email text production is discussed. Technology solutions were informed by people with disabilities through focus group and survey data. The resulting cross-disability design of MultiMail provides innovative and cost-free solutions to email text production.

Toxicity prediction in cancer using multiple instance learning in a multi-task framework

Treatments of cancer cause severe side effects called toxicities. Reduction of such effects is crucial in cancer care. To impact care, we need to predict toxicities at fortnightly intervals. This toxicity data differs from traditional time series data as toxicities can be caused by one treatment on a given day alone, and thus it is necessary to consider the effect of the singular data vector causing toxicity. We model the data before prediction points using the multiple instance learning, where each bag is composed of multiple instances associated with daily treatments and patient-specific attributes, such as chemotherapy, radiotherapy, age and cancer types. We then formulate a Bayesian multi-task framework to enhance toxicity prediction at each prediction point. The use of the prior allows factors to be shared across task predictors. Our proposed method simultaneously captures the heterogeneity of daily treatments and performs toxicity prediction at different prediction points. Our method was evaluated on a real-word dataset of more than 2000 cancer patients and had achieved a better prediction accuracy in terms of AUC than the state-of-art baselines.