Probabilistic Models for Exploring, Predicting, and Influencing Health Trajectories

Over the past decade, healthcare systems around the world have transitioned from paper to electronic health records. The majority of healthcare systems today now host large, on-premise clusters that support an institution-wide network of computers deployed at the point of care. A stream of transactions pass through this network each minute, recording information about what medications a patient is receiving, what procedures they have had, and the results of hundreds of physical examinations and laboratory tests. There is increasing pressure to leverage these repositories of data as a means to improve patient outcomes, drive down costs, or both. To date, however, there is no clear answer on how to best do this. In this thesis, we study two important problems that can help to accomplish these goals: disease subtyping and disease trajectory prediction. In disease subtyping, the goal is to better understand complex, heterogeneous diseases by discovering patient populations with similar symptoms and disease expression. As we discover and refine subtypes, we can integrate them into clinical practice to improve management and can use them to motivate new hypothesis-driven research into the genetic and molecular underpinnings of the disease. In disease trajectory prediction, our goal is to forecast how severe a patient's disease will become in the future. Tools to make accurate forecasts have clear implications for clinical decision support, but they can also improve our process for validating new therapies through trial enrichment. We identify several characteristics of EHR data that make it to difficult to do subtyping and disease trajectory prediction. The key contribution of this thesis is a collection of novel probabilistic models that address these challenges and make it possible to successfully solve the subtyping and disease trajectory prediction problems using EHR data

Robust Audio-Codebooks for Large-Scale Event Detection in Consumer Videos

Abstract In this paper we present our audio based system for detecting "events" within consumer videos (e.g. You Tube) and report our experiments on the TRECVID Multimedia Event Detection (MED) task and development data. Codebook or bag-of-words models have been widely used in text, visual and audio domains and form the state-of-the-art in MED tasks. The overall effectiveness of these models on such datasets depends critically on the choice of low-level features, clustering approach, sampling method, codebook size, weighting schemes and choice of classifier. In this work we empirically evaluate several approaches to model expressive and robust audio codebooks for the task of MED while ensuring compactness. First, we introduce the Large Scale Pooling Features (LSPF) and Stacked Cepstral Features for encoding local temporal information in audio codebooks. Second, we discuss several design decisions for generating and representing expressive audio codebooks and show how they scale to large datasets. Third, we apply text based techniques like Latent Dirichlet Allocation (LDA) to learn acoustictopics as a means of providing compact representation while maintaining performance. By aggregating these decisions into our model, we obtained 11% relative improvement over our baseline audio systems

Noisemes: Manual Annotation of Environmental Noise in Audio Streams

<p>Audio information retrieval is a difficult problem due to the highly unstructured nature of the data. A general labeling system for identifying audio patterns could unite research efforts in the field. This paper introduces 42 distinct labels, the “noisemes”, developed for the manual annotation of noise segments as they occur in audio streams of consumer captured and semiprofessionally produced videos. The labels describe distinct noise units based on audio concepts, independent of visual concepts as much as possible. We trained a recognition system using 5.6 hours of manually labeled data, and present recognition results</p

Robust Audio-Codebooks for Large-Scale Event Detection in Consumer Videos

<p>In this paper we present our audio based system for detecting "events" within consumer videos (e.g. You Tube) and report our experiments on the TRECVID Multimedia Event Detection (MED) task and development data. Codebook or bag-of-words models have been widely used in text, visual and audio domains and form the state-of-the-art in MED tasks. The overall effectiveness of these models on such datasets depends critically on the choice of lowlevel features, clustering approach, sampling method, codebook size, weighting schemes and choice of classifier. In this work we empirically evaluate several approaches to model expressive and robust audio codebooks for the task of MED while ensuring compactness. First, we introduce the Large Scale Pooling Features (LSPF) and Stacked Cepstral Features for encoding local temporal information in audio codebooks. Second, we discuss several design decisions for generating and representing expressive audio codebooks and show how they scale to large datasets. Third, we apply text based techniques like Latent Dirichlet Allocation (LDA) to learn acoustic-topics as a means of providing compact representation while maintaining performance. By aggregating these decisions into our model, we obtained 11% relative improvement over our baseline audio systems.</p

Event-based Video Retrieval Using Audio

<p>Multimedia Event Detection (MED) is an annual task in the NIST TRECVID evaluation, and requires participants to build indexing and retrieval systems for locating videos in which certain predefined events are shown. Typical systems focus heavily on the use of visual data. Audio data, however, also contains rich information that can be effectively used for video retrieval, and MED could benefit from the attention of researchers in audio analysis. We present several systems for performing MED using only audio data, report the results of each system on the TRECVID MED 2011 development dataset, and compare the strengths and weaknesses of each approach.</p

Informedia E-Lamp@TRECVID 2012: Multimedia Event Detection and Recounting (MED and MER)

<p>We report on our system used in the TRECVID 2012 Multimedia Event Detection (MED) and Multimedia Event Recounting (MER) tasks. For MED, generally, it consists of three main steps: extracting features, training detectors and fusion. In the feature extraction part, we extract many low-level, high-level features and text features. Those features are then represented in three different ways which are spatial bag-of words with standard tiling, spatial bag-of-words with feature and event specific tiling and the Gaussian Mixture Model Super Vector. In the detector training and fusion, two classifiers and three fusion methods are employed. The results from both of the official sources and our internal evaluations show good performance of our system. For our MER system, it takes some of the features and detection results from the MED system from which the recount is then generated.</p