Abstract
Publications home
Cohort representation and exploration
at
| Bernard Omidvar-Tehrani, Sihem Amer-Yahia, Laks V.S. Lakshmanan |
| 35ème Conférence sur la Gestion de Données – Principes, Technologies et Applications (BDA), Lyon, France, 15-18 October, 2019 |
@inproceedings{omidvar2018cohort,
title={Cohort representation and exploration},
author={Omidvar-Tehrani, Behrooz and Amer-Yahia, Sihem and Lakshmanan, Laks VS},
booktitle={2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)},
pages={169--178},
year={2018},
organization={IEEE}
}The abundant availability of health-care data calls for effective analysis methods which help medical experts gain a better understanding of their data. While the focus has been largely on prediction,
“representation” and “exploration” of health-care data have received little attention. In this paper, we introduce CORE, a framework for representing and exploring patient cohorts. Obtaining a readable and succinct representation of health data of a cohort is challenging because cohorts often consist of hundreds of patients whose medical actions are of various types and occur at different points in time. We extend the Needleman-Wunsch algorithm for sequence matching to handle temporal sequences, and propose “trajectory families”, a customized index to efficiently compare and aggregate patient trajectories into a cohort representation. We define cohort exploration as finding similar cohorts to a given cohort. This problem is challenging because the potential number of similar cohorts is huge. We propose a two-staged approach based on limiting the search space to “contrast cohorts” and then computing their similarity to the given cohort. To speed up cohort similarity computation, we use “event sets” in the same spirit as the double dictionary encoding proposed for keyword search. We run qualitative and quantitative experiments on real data to explore the efficiency and usefulness of CORE. We show that CORE representations reduce time-to-insight from hours to seconds and help medical experts find insights better than state-of-the-art Visual Analytics tools.
Related Content
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
This web site uses cookies for the site search, to display videos and for aggregate site analytics.
Learn more about these cookies in our privacy notice.
Cookie settings
You may choose which kind of cookies you allow when visiting this website. Click on "Save cookie settings" to apply your choice.
This content is currently blocked. To view the content please either 'Accept social media cookies' or 'Accept all cookies'.
For more information on cookies see our privacy notice.