Cross-hospital portability of information extraction of cancer staging information

Martínez, David; Pitson, Graham; MacKinlay, Andrew; Cavedon, Lawrence

doi:10.1016/j.artmed.2014.06.002

Cited by 22 publications

(9 citation statements)

References 13 publications

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“…The goals of the systems cover a very broad range of clinical tasks across multiple domains, but there are a few areas that have been the focus of more than one system. For example, multiple systems have been developed to identify medication information [27–34] and to extract tumor and cancer characteristics from pathology reports [35–39]. There are also systems attempting to process clinical trial eligibility criteria for easier cohort matching [40,41] and to determine the smoking history of patients [42,43].…”

Section: Resultsmentioning

confidence: 99%

Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review

Kreimeyer

Foster

Pandey

et al. 2017

Journal of Biomedical Informatics

454

255

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We followed a systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses to identify existing clinical natural language processing (NLP) systems that generate structured information from unstructured free text. Seven literature databases were searched with a query combining the concepts of natural language processing and structured data capture. Two reviewers screened all records for relevance during two screening phases, and information about clinical NLP systems was collected from the final set of papers. A total of 7149 records (after removing duplicates) were retrieved and screened, and 86 were determined to fit the review criteria. These papers contained information about 71 different clinical NLP systems, which were then analyzed. The NLP systems address a wide variety of important clinical and research tasks. Certain tasks are well addressed by the existing systems, while others remain as open challenges that only a small number of systems attempt, such as extraction of temporal information or normalization of concepts to standard terminologies. This review has identified many NLP systems capable of processing clinical free text and generating structured output, and the information collected and evaluated here will be important for prioritizing development of new approaches for clinical NLP.

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Section: Resultsmentioning

confidence: 99%

Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review

Kreimeyer

Foster

Pandey

et al. 2017

Journal of Biomedical Informatics

454

255

View full text Add to dashboard Cite

show abstract

“…The authors obtained an accuracy of 72%, 78%, and 94% for tumor, node, and metastases staging, respectively. Martínez et al (2014), obtained F-scores of 81%, 85%, and 94% for staging tumor, node, and metastases respectively for colorectal cancer pathology reports. The authors used 200 pathology reports for training and evaluation.…”

Section: Related Researchmentioning

confidence: 99%

“…McCowan et al (2007), Nguyen et al (2010) and Martínez et al (2014) use text mining to perform cancer classification according to the TNMscale (Tumor Node Metastases) (Wittekind et al, 2014). McCowan et al (2007), trained on 710 pathology reports for lung cancer using the SVM algorithm and evaluated on 179 reports.…”

Section: Related Researchmentioning

confidence: 99%

Creating a rule based system for text mining of Norwegian breast cancer pathology reports

Weegar

Dalianis

2015

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

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National cancer registries collect cancer related information from multiple sources and make it available for research. Part of this information originates from pathology reports, and in this pre-study the possibility of a system for automatic extraction of information from Norwegian pathology reports is investigated. A set of 40 pathology reports describing breast cancer tissue samples has been used to develop a rule based system for information extraction. To validate the performance of this system its output has been compared to the data produced by experts doing manual encoding of the same pathology reports. On average, a precision of 80%, a recall of 98% and an F-score of 86% has been achieved, showing that such a system is indeed feasible.

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“…Therefore, most medical scientists are attracted to the new technologies of predictive models in disease forecasting [12]. These new advancements in medical care have been expanding the accessibility of electronic data and opening new doors for decision support and productivity improvements [13]. ML methods have been effectively utilized in the computerized interpretation of pneumonic capacity tests for the differential analysis of CDs.…”

Section: Introductionmentioning

confidence: 99%

Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis

Battineni

Sagaro

Chinatalapudi

et al. 2020

JPM

237

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This paper reviews applications of machine learning (ML) predictive models in the diagnosis of chronic diseases. Chronic diseases (CDs) are responsible for a major portion of global health costs. Patients who suffer from these diseases need lifelong treatment. Nowadays, predictive models are frequently applied in the diagnosis and forecasting of these diseases. In this study, we reviewed the state-of-the-art approaches that encompass ML models in the primary diagnosis of CD. This analysis covers 453 papers published between 2015 and 2019, and our document search was conducted from PubMed (Medline), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) libraries. Ultimately, 22 studies were selected to present all modeling methods in a precise way that explains CD diagnosis and usage models of individual pathologies with associated strengths and limitations. Our outcomes suggest that there are no standard methods to determine the best approach in real-time clinical practice since each method has its advantages and disadvantages. Among the methods considered, support vector machines (SVM), logistic regression (LR), clustering were the most commonly used. These models are highly applicable in classification, and diagnosis of CD and are expected to become more important in medical practice in the near future.

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Cross-hospital portability of information extraction of cancer staging information

Cited by 22 publications

References 13 publications

Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review

Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review

Creating a rule based system for text mining of Norwegian breast cancer pathology reports

Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis

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