Patricia Ordóñez

MINING PATENTS USING MOLECULAR SIMILARITY SEARCH

Text analytics is becoming an increasingly important tool used in biomedical research. While advances continue to be made in the core algorithms for entity identification and relation extraction, a need for practical applications of these technologies arises. We developed a system that allows users to explore the US Patent corpus using molecular information. The core of our system contains three main technologies: A high performing chemical annotator which identifies chemical terms and converts them to structures, a similarity search engine based on the emerging IUPAC International Chemical Identifier (InChI) standard, and a set of on demand data mining tools. By leveraging this technology we were able to rapidly identify and index 3,623,248 unique chemical structures from 4,375,036 US Patents and Patent Applications. Using this system a user may go to a web page, draw a molecule, search for related Intellectual Property (IP) and analyze the results. Our results prove that this is a far more effective way for identifying IP than traditional keyword based approaches.

Using Modified Multivariate Bag-of-Words Models to Classify Physiological Data

In this paper we present two novel multivariate time series representations to classify physiological data of different lengths. The representations may be applied to any group of multivariate time series data that examine the state or health of an entity. Multivariate Bag-of-Patterns and Stacked Bags of-Patterns improve on their univariate counterpart, inspired by the bag-of-words model, by using multiple time series and analyzing the data in a multivariate fashion. We also borrow techniques from the natural language processing domain such as term frequency and inverse document frequency to improve classification accuracy. We introduce a technique named inverse frequency and present experimental results on classifying patients who have experienced acute episodes of hypotension.

An animated multivariate visualization for physiological and clinical data in the ICU

Current visualizations of electronic medical data in the Intensive Care Unit (ICU) consist of stacked univariate plots of variables over time and a tabular display of the current numeric values for the corresponding variables and occasionally an alarm limit. The value of information is dependent upon knowledge of historic values to determine a change in state. With the ability to acquire more historic information, providers need more sophisticated visualization tools to assist them in analyzing the data in a multivariate fashion over time. We present a multivariate time series visualization that is interactive and animated, and has proven to be as effective as current methods in the ICU for predicting an episode of acute hypotension in terms of accuracy, confidence, and efficiency with only 30-60 minutes of training.

Classification of Patients Using Novel Multivariate Time Series Representations of Physiological Data

In this paper we present two novel multivariate time series representations to classify physiological data of different lengths. The representations may be applied to any group of multivariate time series data that examine the state or health of an entity. Multivariate Bag-of-Patterns and Stacked Bags of-Patterns improve on their univariate counterpart, inspired by the bag-of-words model, by using multiple time series and analyzing the data in a multivariate fashion. We also borrow techniques from the natural language processing domain such as term frequency and inverse document frequency to improve classification accuracy. We introduce a technique named inverse frequency and present experimental results on classifying patients who have experienced acute episodes of hypotension.

Improving programming interfaces for people with limited mobility using voice recognition

Programming is an arduous task for individuals with motor impairments who rely on independent tools to interact with their digital environment. Providing a bimodal Integrated Development Environment is key to tackling a programs complex syntax and to improving the programming interface. This project is an effort to facilitate the interaction between programmers with motor impairments in their hands and Integrated Development Environments (IDEs) through the integration of modified versions of open source assistive technology software. We are working on the prototype for a specific user, who is a computer scientist with spinal muscular atrophy (SMA) that can no longer physically attend classes and can only type with one finger. The user is a crucial part of this project providing invaluable input into the design of the interface.

An immersive approach to visualizing perceptual disturbances

Through their experience with the ICERT REU program at the Texas Advanced Computing Center (TACC), two undergraduate students from the University of Puerto Rico and the University of Florida have initiated a collaboration between their home institutions and TACC exploring the possibility of using immersion to simulate perceptual disturbances. Perceptual disturbances are subjective in nature, and difficult to communicate verbally. Often caretakers or those closest to sufferers have difficulty understanding the nature of their suffering. Immersion provides an exciting opportunity to directly communicate percepts with clinicians and loved ones. Here, we present a prototype environment meant to simulate some of the perceptual disturbances associated with seizures and epilepsy. Following further validation of our approach, we hope to promote awareness and empathy for these often jarring phenomena.

OnScreenDualScribe with Point-and-Click Interface: A Viable Computer Interaction Alternative based on a Virtual Modified Numerical Keypad

This paper describes the experience of the first author with the Point-and-Click Interface of the OnScreenDualScribe, created by the last author. The new interface is an innovative extension to the previous interface which required the use of the DualPad. The main differences between the two interfaces are highlighted. The user took several writing tests with the Point-and Click Interface and compares her results with two of interfaces she uses the most for writing, Dragon NaturallySpeaking and SofType. Finally, the first author recommends several improvements to the interface which would make the software a better alternative for her.