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Articles 1 - 3 of 3
Full-Text Articles in Life Sciences
Ordinal Convolutional Neural Networks For Predicting Rdoc Positive Valence Psychiatric Symptom Severity Scores, Anthony Rios, Ramakanth Kavuluru
Ordinal Convolutional Neural Networks For Predicting Rdoc Positive Valence Psychiatric Symptom Severity Scores, Anthony Rios, Ramakanth Kavuluru
Computer Science Faculty Publications
Background—The CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing (NLP) provided a set of 1000 neuropsychiatric notes to participants as part of a competition to predict psychiatric symptom severity scores. This paper summarizes our methods, results, and experiences based on our participation in the second track of the shared task.
Objective—Classical methods of text classification usually fall into one of three problem types: binary, multi-class, and multi-label classification. In this effort, we study ordinal regression problems with text data where misclassifications are penalized differently based on how far apart the ground truth and model predictions are …
Predicting Mental Conditions Based On "History Of Present Illness" In Psychiatric Notes With Deep Neural Networks, Tung Tran, Ramakanth Kavuluru
Predicting Mental Conditions Based On "History Of Present Illness" In Psychiatric Notes With Deep Neural Networks, Tung Tran, Ramakanth Kavuluru
Computer Science Faculty Publications
Background—Applications of natural language processing to mental health notes are not common given the sensitive nature of the associated narratives. The CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing (NLP) changed this scenario by providing the first set of neuropsychiatric notes to participants. This study summarizes our efforts and results in proposing a novel data use case for this dataset as part of the third track in this shared task.
Objective—We explore the feasibility and effectiveness of predicting a set of common mental conditions a patient has based on the short textual description of patient’s history …
Forest Understory Trees Can Be Segmented Accurately Within Sufficiently Dense Airborne Laser Scanning Point Clouds, Hamid Hamraz, Marco A. Contreras, Jun Zhang
Forest Understory Trees Can Be Segmented Accurately Within Sufficiently Dense Airborne Laser Scanning Point Clouds, Hamid Hamraz, Marco A. Contreras, Jun Zhang
Computer Science Faculty Publications
Airborne laser scanning (LiDAR) point clouds over large forested areas can be processed to segment individual trees and subsequently extract tree-level information. Existing segmentation procedures typically detect more than 90% of overstory trees, yet they barely detect 60% of understory trees because of the occlusion effect of higher canopy layers. Although understory trees provide limited financial value, they are an essential component of ecosystem functioning by offering habitat for numerous wildlife species and influencing stand development. Here we model the occlusion effect in terms of point density. We estimate the fractions of points representing different canopy layers (one overstory and …