Machine Learning
Making large-scale support vector machine learning practical
Advances in kernel methods
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Evaluating similarity measures: a large-scale study in the orkut social network
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Ex-ray: Data mining and mental health
Applied Soft Computing
IEEE Transactions on Neural Networks
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People with chronic health conditions require support beyond normal health care systems. Social networking has shown great potential to provide the needed support. Because of the privacy and security issues of health information systems, it is often difficult to find patients who can support each other in the community. We propose a social-networking framework for patient care, in particular for parents of children with Autism Spectrum Disorders (ASD). In the framework, health service providers facilitate social links between parents using similarities of assessment reports without revealing sensitive information. A machine learning approach was developed to generate explanations of ASD assessments in order to assist clinicians in their assessment. The generated explanations are then used to measure similarities between assessments in order to recommend a community of related parents. For the first time, we report on the accuracy of social linking using an explanation-based similarity measure.