scholarly journals A temporal constraint structure for extracting temporal information from clinical narrative

2006 ◽  
Vol 39 (4) ◽  
pp. 424-439 ◽  
Author(s):  
Li Zhou ◽  
Genevieve B. Melton ◽  
Simon Parsons ◽  
George Hripcsak
Keyword(s):  
Temporal Information ◽  
Temporal Constraint ◽  
Clinical Narrative ◽  
Constraint Structure

scholarly journals Time event ontology (TEO): to support semantic representation and reasoning of complex temporal relations of clinical events

2020 ◽  
Vol 27 (7) ◽  
pp. 1046-1056
Author(s):  
Fang Li ◽  
Jingcheng Du ◽  
Yongqun He ◽  
Hsing-Yi Song ◽  
Mohcine Madkour ◽  
...  
Keyword(s):  
Temporal Information ◽  
Temporal Relation ◽  
Adverse Event Report ◽  
Temporal Relations ◽  
Electronic Health Record Data ◽  
Clinical Events ◽  
Clinical Narrative ◽  
Starting Point ◽  
Temporal Expressions ◽  
Event Ontology

Abstract Objective The goal of this study is to develop a robust Time Event Ontology (TEO), which can formally represent and reason both structured and unstructured temporal information. Materials and Methods Using our previous Clinical Narrative Temporal Relation Ontology 1.0 and 2.0 as a starting point, we redesigned concept primitives (clinical events and temporal expressions) and enriched temporal relations. Specifically, 2 sets of temporal relations (Allen’s interval algebra and a novel suite of basic time relations) were used to specify qualitative temporal order relations, and a Temporal Relation Statement was designed to formalize quantitative temporal relations. Moreover, a variety of data properties were defined to represent diversified temporal expressions in clinical narratives. Results TEO has a rich set of classes and properties (object, data, and annotation). When evaluated with real electronic health record data from the Mayo Clinic, it could faithfully represent more than 95% of the temporal expressions. Its reasoning ability was further demonstrated on a sample drug adverse event report annotated with respect to TEO. The results showed that our Java-based TEO reasoner could answer a set of frequently asked time-related queries, demonstrating that TEO has a strong capability of reasoning complex temporal relations. Conclusion TEO can support flexible temporal relation representation and reasoning. Our next step will be to apply TEO to the natural language processing field to facilitate automated temporal information annotation, extraction, and timeline reasoning to better support time-based clinical decision-making.

scholarly journals Spatial-Temporal Person Re-Identification

2019 ◽  
Vol 33 ◽  
pp. 8933-8940 ◽  
Author(s):  
Guangcong Wang ◽  
Jianhuang Lai ◽  
Peigen Huang ◽  
Xiaohua Xie
Keyword(s):  
Probability Distribution ◽  
Semantic Information ◽  
Temporal Information ◽  
Unified Framework ◽  
Temporal Constraint ◽  
Query Image ◽  
Similarity Metric ◽  
Heterogeneous Information ◽  
Large Margin ◽  
Previous State

Most of current person re-identification (ReID) methods neglect a spatial-temporal constraint. Given a query image, conventional methods compute the feature distances between the query image and all the gallery images and return a similarity ranked table. When the gallery database is very large in practice, these approaches fail to obtain a good performance due to appearance ambiguity across different camera views. In this paper, we propose a novel two-stream spatial-temporal person ReID (st-ReID) framework that mines both visual semantic information and spatial-temporal information. To this end, a joint similarity metric with Logistic Smoothing (LS) is introduced to integrate two kinds of heterogeneous information into a unified framework. To approximate a complex spatial-temporal probability distribution, we develop a fast Histogram-Parzen (HP) method. With the help of the spatial-temporal constraint, the st-ReID model eliminates lots of irrelevant images and thus narrows the gallery database. Without bells and whistles, our st-ReID method achieves rank-1 accuracy of 98.1% on Market-1501 and 94.4% on DukeMTMC-reID, improving from the baselines 91.2% and 83.8%, respectively, outperforming all previous state-of-theart methods by a large margin.

How Temporal Information Associated With Verbs Constrains the Ability to Imagine Events

2013 ◽  
Author(s):  
Jeffrey P. Hong ◽  
Todd R. Ferretti ◽  
Rachel Craven ◽  
Rachelle D. Hepburn
Keyword(s):  
Temporal Information

Propositional Constraint Graphs: An Intuitive, Domain-General Tool for Diagramming Knowledge, Assumptions, and Uncertainties

2020 ◽  
Vol 64 (1) ◽  
pp. 254-258
Author(s):  
Zachary H. Pugh ◽  
Douglas J. Gillan
Keyword(s):  
Theoretical Models ◽  
Modeling Language ◽  
Planning And Design ◽  
Constraint Structure ◽  
General Tool

A diagramming method called Propositional Constraint (PC) graphing was developed as an aid for tasks involving argumentation, planning, and design. Motivated by several AI models of defeasible (or non- monotonic) reasoning, PC graphs were designed to represent knowledge according to an analogical framework in which constraints (e.g., evidence, goals, system constraints) may elicit or deny possibilities (e.g., explanations, decisions, behaviors). In cases of underspecification, an absence of constraints yields uncertainty and competition among plausible outcomes. In cases of overspecification, no plausible outcome is yielded until one of the constraints is amended or forfeited. This framework shares features with theoretical models of reasoning and argumentation, but despite its intuitiveness and applicability, we know of no modeling language or graphical aid that explicitly depicts this defeasible constraint structure. We describe the syntax and semantics for PC graphing and then illustrate potential uses for it.

scholarly journals Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy

2021 ◽  
Author(s):  
Jorge F. Lazo ◽  
Aldo Marzullo ◽  
Sara Moccia ◽  
Michele Catellani ◽  
Benoit Rosa ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
State Of The Art ◽  
Automatic Segmentation ◽  
Temporal Information ◽  
Invasive Technique ◽  
Dice Similarity Coefficient ◽  
Specular Reflections ◽  
Lumen Segmentation ◽  
Previous State

Abstract Purpose Ureteroscopy is an efficient endoscopic minimally invasive technique for the diagnosis and treatment of upper tract urothelial carcinoma. During ureteroscopy, the automatic segmentation of the hollow lumen is of primary importance, since it indicates the path that the endoscope should follow. In order to obtain an accurate segmentation of the hollow lumen, this paper presents an automatic method based on convolutional neural networks (CNNs). Methods The proposed method is based on an ensemble of 4 parallel CNNs to simultaneously process single and multi-frame information. Of these, two architectures are taken as core-models, namely U-Net based in residual blocks ($$m_1$$ m 1 ) and Mask-RCNN ($$m_2$$ m 2 ), which are fed with single still-frames I(t). The other two models ($$M_1$$ M 1 , $$M_2$$ M 2 ) are modifications of the former ones consisting on the addition of a stage which makes use of 3D convolutions to process temporal information. $$M_1$$ M 1 , $$M_2$$ M 2 are fed with triplets of frames ($$I(t-1)$$ I ( t - 1 ) , I(t), $$I(t+1)$$ I ( t + 1 ) ) to produce the segmentation for I(t). Results The proposed method was evaluated using a custom dataset of 11 videos (2673 frames) which were collected and manually annotated from 6 patients. We obtain a Dice similarity coefficient of 0.80, outperforming previous state-of-the-art methods. Conclusion The obtained results show that spatial-temporal information can be effectively exploited by the ensemble model to improve hollow lumen segmentation in ureteroscopic images. The method is effective also in the presence of poor visibility, occasional bleeding, or specular reflections.

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