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We present supervised machine-learning approaches to automatically classify medical questions based on a hierarchical evidence taxonomy created by physicians. We show that SVMs is the best classifier for this task and that a ladder approach, which incorporates the knowledge representation of the hierarchical evidence taxonomy, leads to the highest… (More)
Physicians have many questions when caring for patients, and frequently need to seek answers for their questions. Information retrieval systems (e.g., PubMed) typically return a list of documents in response to a user's query. Frequently the number of returned documents is large and makes physicians' information seeking "practical only 'after hours' and not… (More)
The emergence of the Internet as today's primary medium of music distribution has brought about demands for fast and reliable ways to organize, access, and discover music online. To date, many applications designed to perform such tasks have risen to popularity; each relies on a specific form of music metadata to help consumers discover songs and artists… (More)
With the increasing trend of neural network models towards larger structures with more layers, we expect a corresponding exponential increase in the number of possible architectures. In this paper, we apply a hybrid evolutionary search procedure to define the initialization and architectural parameters of convolutional networks, one of the first successful… (More)
We are developing a biomedical question answering system. This paper describes our system's architecture and our question analysis component. Specifically, we have explored the use of various supervised machine learning approaches to filter out unanswerable questions based on physicians' annotations.
A 4-bit adaptive differential pulse-code modulation (AD-PCM) scheme applied to the sensor data of a Zigbee based wireless sensor network node is shown to decrease the energy consumption of the analog front-end of the node by 58%. Simulation results from an energy model of an 802.15.4 based analog front-end show that the energy consumed by the network node… (More)