Identifying sections is one of the critical components of understanding medical information from unstructured clinical notes and developing assistive technologies for clinical notewriting tasks.Most state-of-the-art text classification systems require thousands of indomain text data to achieve high performance.However, collecting in-domain and recent clinical note data with section labels is challenging given the high level of privacy and sensitivity.This paper proposes an algorithmic way to improve the task transferability of meta-learningbased text classification in order to address the issue of low-resource target data.Specifically, we explore how to make the best use of the source dataset and propose a unique task transferability measure named Normalized Negative Conditional Entropy (NNCE).Leveraging the NNCE, we develop strategies for selecting clinical categories and sections from source task data to boost cross-domain meta-learning accuracy.Experimental results show that our task selection strategies improve section classification accuracy significantly compared to meta-learning algorithms.

Hospital emergency department (ED) operations are affected when critically ill or injured patients arrive. Such events often lead to the initiation of specific protocols, referred to as Resuscitation-team Activation (RA), in the ED of Mayo Clinic, Rochester, MN where this study was conducted. RA events lead to the diversion of resources from other patients in the ED to provide care to critically ill patients; therefore, it has an impact on the entire ED system. This paper presents a data-driven and flexible statistical learning model to quantify the impact of RA on the ED. The model learns the pattern of operations in the ED from historical patient arrival and departure timestamps and quantifies the impact of RA by measuring the deviation of the departure of patients during RA from normal processes. The proposed method significantly outperforms baseline methods based on measuring the average time patients spend in the ED.

Wireless Video Sensor Networks (WVSNs) provide opportunities to use large number of low-cost low-resolution wireless camera sensors for large-scale outdoor remote surveillance missions. Camera sensor deployment is crucial in achieving good coverage, accuracy and fault tolerance. In particular, with the decreased costs of wireless cameras, redundant camera deployment is attractive in order to get multiple disparate views of events for improved event identification. If the capturing of an event spans${\ 360^\circ}$, this is referred to as angular coverage. In this paper, we consider the problem of determining optimal camera placement to achieve angular coverage continuously over a given region. We develop a bi-level algorithm to find the minimum-cost camera placement. In the first level, we run a master problem that identifies the camera placement points to achieve angular coverage of a discrete set of points selected from the region of interest. Next, we use a sub-problem to identify points in the continuous region that are not covered by the cameras placed in the previous run of the master problem. We then add these uncovered points to discrete point set of the master problem and re-run the master problem. We continue running the master and sub-problems iteratively until the sub-problem becomes infeasible indicating that the entire region is covered. In the numerical experiments, we consider two cases 1) placement of homogeneous cameras with fixed resolutions; and 2) placement of heterogeneous cameras with different characteristics and resolutions. We also introduce varying resolution requirements for different parts of the region and place the cameras such that the required resolution is satisfied. The numerical results show the superiority of the bi-level approach respect to existing approaches.

In this paper, we tackle the problem of providing coverage for video panorama generation in Wireless Heterogeneous Visual Sensor Networks (VSNs) where cameras may have different price, resolution, Field-of-View (FoV) and Depth-of-Field (DoF). We utilize multi-perspective coverage (MPC) which refers to the coverage of a point from given disparate perspectives simultaneously. For a given minimum average resolution, area boundaries, and variety of camera sensors, we propose a deployment algorithm which minimizes the total cost while guaranteeing full MPC of the area (i.e., the coverage needed for video panorama generation) and the minimum required resolution. Specifically, the approach is based on a bi-level mixed integer program (MIP), which runs two models, namely master problem and sub-problem, iteratively. Master-problem provides coverage for initial set of identified points while meeting the minimum resolution requirement with minimum cost. Sub-problem which follows the master-problem finds an uncovered point and extends the set of points to be covered. It then sends this set back to the master-problem. Master-problem and sub-problem continue to run iteratively until sub-problem becomes infeasible, which means full MPC has been achieved with the resolution requirements. The numerical results show the superiority of our approach with respect to existing approaches.

Deployment of cameras in Wireless Multimedia Sensor Networks (WMSNs) is crucial in achieving good coverage, accuracy and fault tolerance. With the decreased costs of wireless cameras, WMSNs provide opportunities for redundant camera deployment in order to get multiple disparate views of events. Referred to as multi-perspective coverage (MPC), this paper proposes an optimal solution for camera deployment that can achieve full MPC for a given region. The solution is based on a Bi-Level mixed integer program (MIP) which works by solving two sub-problems named master and sub-problems. The master problem identifies a solution based on an initial set of points and then calls the sub-problem to cover the uncovered points iteratively. Experiments show that our solution can provide full MPC with less number of cameras compared to traditional solutions.