Browsing by Subject "Lasso"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    The effect of surgeon-scrub nurse collaboration on predicting operative times
    (2013-09) Feng, Yuanyuan
    Background: Operating room managers need to construct the surgery schedule for the next day by synthesizing information on estimated surgery duration, staff information, and surgeons' information. The purpose of this study is to assist operating room managers' decision making one day before the surgery by developing the predictive model for operative times taking into account the staff information. Methods: 10,960 cases in a health system in Middlewest are analyzed. The outcomes are the mean absolute errors of the predictions and the correlation between the predicted operative time and the observed durations, and the predictors include surgeon-scrub nurse pair IDs, individual surgeon IDs and individual scrub nurse IDs. Lasso regression modeling on the logarithm of the operative time is performed. Results: The unexplained variation of the residuals of the model, which only includes log scheduled duration and procedure type, can be further explained by the surgeon-scrub nurse collaboration frequency. Besides, the model that include surgeon-scrub nurse pair IDs, surgeon IDs and scrub nurse IDs can reduce the mean absolute errors by 8.47 minutes, compared with the scheduled procedure duration. Conclusion: The more surgeons and scrub nurses collaborate, the less time a surgery will take. Including surgeon-scrub nurse pairs in the predictive model, the prediction errors can be reduced.
  • Thumbnail Image
    Item
    Sparsity-cognizant algorithms with applications to communications, signal processing, and the smart grid.
    (2012-08) Zhu, Hao
    Sparsity plays an instrumental role in a plethora of scientific fields, including statistical inference for variable selection, parsimonious signal representations, and solving under-determined systems of linear equations - what has led to the ground-breaking result of compressive sampling (CS). This Thesis leverages exciting ideas of sparse signal reconstruction to develop sparsity-cognizant algorithms, and analyze their performance. The vision is to devise tools exploiting the ‘right’ form of sparsity for the ‘right’ application domain of multiuser communication systems, array signal processing systems, and the emerging challenges in the smart power grid. Two important power system monitoring tasks are addressed first by capitalizing on the hidden sparsity. To robustify power system state estimation, a sparse outlier model is leveraged to capture the possible corruption in every datum, while the problem nonconvexity due to nonlinear measurements is handled using the semidefinite relaxation technique. Different from existing iterative methods, the proposed algorithm approximates well the global optimum regardless of the initialization. In addition, for enhanced situational awareness, a novel sparse overcomplete representation is introduced to capture (possibly multiple) line outages, and develop real-time algorithms for solving the combinatorially complex identification problem. The proposed algorithms exhibit nearoptimal performance while incurring only linear complexity in the number of lines, which makes it possible to quickly bring contingencies to attention. This Thesis also accounts for two basic issues in CS, namely fully-perturbed models and the finite alphabet property. The sparse total least-squares (S-TLS) approach is proposed to furnish CS algorithms for fully-perturbed linear models, leading to statistically optimal and computationally efficient solvers. The S-TLS framework is well motivated for grid-based sensing applications and exhibits higher accuracy than existing sparse algorithms. On the other hand, exploiting the finite alphabet of unknown signals emerges naturally in communication systems, along with sparsity coming from the low activity of each user. Compared to approaches only accounting for either one of the two, joint exploitation of both leads to statistically optimal detectors with improved error performance.