Machine Learning and Rule-based Approaches to Assertion Classification

Download

index.pdf

Date

2009-01-01

Author

Uzuner, Oezlem
Zhang, Xiaoran
Sibanda, Tawanda

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

228
views

0
downloads

Objectives: The authors study two approaches to assertion classification. One of these approaches, Extended NegEx (ENegEx), extends the rule-based NegEx algorithm to cover alter-association assertions; the other, Statistical Assertion Classifier (StAC), presents a machine learning solution to assertion classification.

Subject Keywords

Health Informatics

URI

https://hdl.handle.net/11511/66514

Journal

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION

DOI

https://doi.org/10.1197/jamia.m2950

Collections

Engineering, Article

Suggestions

OpenMETU
Core

Recognizing Obesity and Comorbidities in Sparse Data Uzuner, Oezlem (Oxford University Press (OUP), 2009-07-01) In order to survey, facilitate, and evaluate studies of medical language processing on clinical narratives, i2b2 (Informatics for Integrating Biology to the Bedside) organized its second challenge and workshop. This challenge focused on automatically extracting information on obesity and fifteen of its most common comorbidities from patient discharge summaries. For each patient, obesity and any of the comorbidities could be Present, Absent, or Questionable (i.e., possible) in the patient, or Unmentioned in ...
Specializing for predicting obesity and its co-morbidities Goldstein, Ira; Uzuner, Oezlem (Elsevier BV, 2009-10-01) We present specializing, a method for combining classifiers for multi-class classification. Specializing trains one specialist classifier per class and utilizes each specialist to distinguish that class from all others in a one-versus-all manner. It then supplements the specialist classifiers with a catch-all classifier that performs multi-class classification across all classes. We refer to the resulting combined classifier as a specializing classifier.
Interactive Approaches to Multiple Criteria Sorting Problems: Entropy-Based Question Selection Methods Özarslan, Ali; Karakaya, Gülşah (2022-08-01) In this study, interactive approaches for sorting alternatives evaluated on multiple criteria are developed. The possible category ranges of alternatives are defined by mathematical models iteratively under the assumption that the preferences of the decision maker (DM) are consistent with an additive utility function. Simulation-based and model-based parameter generation methods are proposed to hypothetically assign the alternatives to categories. A practical approach to solve the incompatibility problem of...
Self-construals and values in different cultural and socioeconomic contexts İmamoğlu, Emine Olcay; Karakitapoglu-Aygun, Z (Informa UK Limited, 2004-11-01) In this study the authors investigated (a) how individuational and relational self-orientations, as well as self-directed and other-directed values, are related to one another, and (b) how these self- and value orientations differ across 2 cultural (i.e., 422 Turkish and 441 American university students) and 2 socioeconomic status (SES) groups (i.e., 186 lower SES and 167 upper SES Turkish high school students). Across cross-cultural and SES groups, individuational and relational self-orientations appeared ...
Probabilistic Slope Stability Analyses Using Limit Equilibrium and Finite Element Methods Akbas, Burak; Huvaj Sarıhan, Nejan (2015-10-16) This paper compares the results of different probabilistic approaches and emphasizes the necessity of probabilistic analyses in slope stability studies. To do that, Limit Equilibrium Method (LEM) and Finite Element Method (FEM) are utilized and their outputs are compared in terms of probability of failure (PF), reliability index (RI), factor of safety (FS) and the failure surface. Lastly, concept of Random Finite Element Method (RFEM) is studied and effects of spatial correlation distance are investigated.

Citation Formats

O. Uzuner, X. Zhang, and T. Sibanda, “Machine Learning and Rule-based Approaches to Assertion Classification,” JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, pp. 109–115, 2009, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/66514.