Symbolic rule-based classification of lung cancer stages from free-text pathology reports

Nguyen, Anthony; Lawley, Michael; Hansen, David; Bowman, Rayleen V.; Clarke, Belinda; Duhig, Edwina; Colquist, Shoni

doi:10.1136/jamia.2010.003707

Cited by 122 publications

(74 citation statements)

References 5 publications

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“…In biomedicine, condition classification follows two main approaches: 1) manual approaches, where experts manually assign labels to conditions [18][19][20][21][22]; and 2) passive classification approaches that require a labeled training set and are based on machine learning approaches and text classification [23][24].…”

Section: Classification Of Conditionsmentioning

confidence: 99%

An Active Learning Framework for Efficient Condition Severity Classification

Nissim

Boland

Moskovitch

et al. 2015

Artificial Intelligence in Medicine

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Abstract. Understanding condition severity, as extracted from Electronic Health Records (EHRs), is important for many public health purposes. Methods requiring physicians to annotate condition severity are time-consuming and costly. Previously, a passive learning algorithm called CAESAR was developed to capture severity in EHRs. This approach required physicians to label conditions manually, an exhaustive process. We developed a framework that uses two Active Learning (AL) methods (Exploitation and Combination_XA) to decrease manual labeling efforts by selecting only the most informative conditions for training. We call our approach CAESAR-Active Learning Enhancement (CAESAR-ALE). As compared to passive methods, CAESAR-ALE's first AL method, Exploitation, reduced labeling efforts by 64% and achieved an equivalent true positive rate, while CAESAR-ALE's second AL method, Combination_XA, reduced labeling efforts by 48% and achieved equivalent accuracy. In addition, both these AL methods outperformed the traditional AL method (SVM-Margin). These results demonstrate the potential of AL methods for decreasing the labeling efforts of medical experts, while achieving greater accuracy and lower costs.

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Section: Classification Of Conditionsmentioning

confidence: 99%

An Active Learning Framework for Efficient Condition Severity Classification

Nissim

Boland

Moskovitch

et al. 2015

Artificial Intelligence in Medicine

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show abstract

“…However, the authors explain that the annotation cost is high, and the performance for "Stage T" was still low (65% accuracy). In their latest work [9] rely heavily on the SNOMED (Systematized Nomenclature of Medicine -Clinical Terms) 4 concepts and relationships to identify the relevant entities. They argue that this approach is more portable than fine-grained annotation, although there is a loss in accuracy with respect to their best ML approach, and it requires involvement from the experts.…”

Section: Introductionmentioning

confidence: 99%

Information extraction from pathology reports in a hospital setting

Martínez

2011

Proceedings of the 20th ACM International Conference on Information and Knowledge Management

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As more health data becomes available, information extraction aims to make an impact on the workflows of hospitals and care centers. One of the targeted areas is the management of pathology reports, which are employed for cancer diagnosis and staging. In this work we integrate text mining tools in the workflow of the Royal Melbourne Hospital, to extract information from pathology reports with minimal expert intervention. Our framework relies on coarsegrained annotation (at document level), making it highly portable. Our evaluation shows that the kind of language used in these reports makes it feasible to extract information with high precision and recall, by means of state-of-the-art classification methods, and feature engineering.

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“…This strategy improved T and N accuracy to 74% and 87%. In their latest work, Nguyen et al (2010) used a symbolic logic approach. Rules leveraged concept-normalization, negation, and normalization through the SNOMED-CT hierarchy.…”

Section: Introductionmentioning

confidence: 99%

In-depth annotation for patient level liver cancer staging

Yim

Kwan

Yetisgen

2015

Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis

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Cancer stages, which summarizes extent of cancer progression, is an important tool for evidence-based medical research. However, they are not always recorded in the electronic medical record. In this paper, we describe work for annotating a medical text corpus with the goal of predicting patient level liver cancer staging in hepatocellular carcinoma (HCC) patients.Our annotation consisted of identifying 11 parameters, used to calculate liver cancer staging, at the text span level as well as at the patient level. Also at the patient level, we annotated stages for three commonly-used liver cancer staging schemes. Our inter-rater agreement showed text annotation consistency 0.73 F1 for partial text match and 0.91 F1 at the patient level.After annotation, we performed several document classification experiments for the text span annotations using standard machine learning classifiers, including decision trees, maximum entropy, naive Bayes and support vector machines. Thereby, we identified baseline performances for our task at 0.63 F1 as well as strategies for future improvement.

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Symbolic rule-based classification of lung cancer stages from free-text pathology reports

Cited by 122 publications

References 5 publications

An Active Learning Framework for Efficient Condition Severity Classification

An Active Learning Framework for Efficient Condition Severity Classification

Information extraction from pathology reports in a hospital setting

In-depth annotation for patient level liver cancer staging

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