Collection of Cancer Stage Data by Classifying Free-text Medical Reports

McCowan, Iain; Moore, Darren; Nguyen, Anthony; Bowman, Rayleen V.; Clarke, Belinda; Duhig, Edwina; Fry, Mary-Jane

doi:10.1197/jamia.m2130

Cited by 61 publications

(39 citation statements)

References 22 publications

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“…The most common approach for entity identification is to map tokens from segmented text into a target dictionaries such as SNOMED-CT, ICD, UMLS etc. Tools for mapping concepts in biomedical literature such as MetaMap can also process tokenized clinical texts [49, 50], but other clinically customized tools exist [51, 52] such as the HITex system [53] and the Knowledge Map Concept Identifier [53, 54]. …”

Section: 3 Phenotype Extraction From Ehr Textmentioning

confidence: 99%

Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health

Simmons

Singhal

2016

Advances in Experimental Medicine and Biology

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The key question of precision medicine is whether it is possible to find clinically actionable granularity in diagnosing disease and classifying patient risk. The advent of next generation sequencing and the widespread adoption of electronic health records (EHRs) have provided clinicians and researchers a wealth of data and made possible the precise characterization of individual patient genotypes and phenotypes. Unstructured text — found in biomedical publications and clinical notes — is an important component of genotype and phenotype knowledge. Publications in the biomedical literature provide essential information for interpreting genetic data. Likewise, clinical notes contain the richest source of phenotype information in EHRs. Text mining can render these texts computationally accessible and support information extraction and hypothesis generation. This chapter reviews the mechanics of text mining in precision medicine and discusses several specific use cases, including database curation for personalized cancer medicine, patient outcome prediction from EHR-derived cohorts, and pharmacogenomic research. Taken as a whole, these use cases demonstrate how text mining enables effective utilization of existing knowledge sources and thus promotes increased value for patients and healthcare systems. Text mining is an indispensable tool for translating genotype-phenotype data into effective clinical care that will undoubtedly play an important role in the eventual realization of precision medicine.

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Section: 3 Phenotype Extraction From Ehr Textmentioning

confidence: 99%

Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health

Simmons

Singhal

2016

Advances in Experimental Medicine and Biology

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“…The concepts in the Metathesaurus originate from terminologies used in different areas 6 , and due to the diversity of this database, we also explored using a subset that comprises a medical terminology: SNOMED 7 . In this case we also rely on MetaMap to extract the concepts, but we filter out those that are not present in SNOMED.…”

Section: Knowledge Sources and Feature Representationmentioning

confidence: 99%

“…Their initial experiments showed the difficulty of the primary tumour stage detection (T), with a top accuracy of 64%. In a follow-up paper they explored richer annotation, and a combination of ML and rulebased post-processing [7]. They performed fine-grained annotation of stage details for each sentence in order to build their system, and they observed improvements over a coarse-grained (documentlevel) multiclass classifier.…”

Section: Introductionmentioning

confidence: 96%

“…Only in some cases the information is converted into structured format, and even then, we face the problems of different standards, data entry errors, and incomplete entries [7]. For this reason, the Australian National Cancer Data Strategy has identified the collection of population-based structured data as a fundamental strategical problem 1 .…”

Section: Introductionmentioning

confidence: 99%

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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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“…In a previous study, McCowan et al. extracted cancer staging information from pathology reports using support vector machines (SVMs) . Dublin et al.…”

Section: Introductionmentioning

confidence: 99%

Screening of anticancer drugs to detect drug‐induced interstitial pneumonia using the accumulated data in the electronic medical record

Shimai

Takeda

Okada

et al. 2018

Pharmacology Res & Perspec

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Because drug‐induced interstitial pneumonia (DIP) is a serious adverse drug reaction, its quantitative risk with individual medications should be taken into due consideration when selecting a medicine. We developed an algorithm to detect DIP using medical record data accumulated in a hospital. Chest computed tomography (CT) is mainly used for the diagnosis of IP, and chest X‐ray reports, KL‐6, and SP‐D values are used to support the diagnosis. The presence of IP in the reports was assessed by a method using natural language‐processing, in which IP was estimated according to the product of the likelihood ratio of characteristic keywords in each report. The sensitivity and the specificity of the method for chest CT reports were 0.92 and 0.97, while those for chest X‐ray reports were 0.83 and 1, respectively. The occurrence of DIP was estimated by the patterns of presence of IP before, during, and after the administration of the target medicine. The occurrence rate of DIP in cases administered Gefitinib; Methotrexate (MTX); Tegafur, Gimeracil, and Oteracil potassium (TS‐1); and Tegafur and Uracil (UTF) was 6.0%, 2.3%, 1.4%, and 0.7%, respectively. The estimated DIP cases were checked by having the medical records independently reviewed by medical doctors. By chart review, the positive predictive values of DIP against Gefitinib, MTX, TS‐1, and UFT were 69.2%, 44.4%, 58.6%, and 77.8%, respectively. Although the cases extracted by this method included some that did not have DIP, this method can estimate the relative risk of DIP between medicines.

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Collection of Cancer Stage Data by Classifying Free-text Medical Reports

Cited by 61 publications

References 22 publications

Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health

Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health

Information extraction from pathology reports in a hospital setting

Screening of anticancer drugs to detect drug‐induced interstitial pneumonia using the accumulated data in the electronic medical record

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