Information Extraction for Tracking Liver Cancer Patients' Statuses: From Mixture of Clinical Narrative Report Types

Ping, Xiao; Tseng, Yi-Ju; Chung, Yu-Fang; Wu, Yung‐Fu; Hsu, Ching Wei; Yang, Pei‐Ling; Huang, Guan-Tarn; Lai, Feipei; Liang, Ja-Der

doi:10.1089/tmj.2012.0241

Cited by 18 publications

(23 citation statements)

References 28 publications

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“…Although existing work extracted cancer information for numerous cancer types, we only created child frames for those types where any type-specific information was extracted. There were papers related to cancer types such as lung cancer [40][41], liver cancer [42] and ovarian cancer [43], but no frames were created for these types as they all extract the general cancer-related elements described in the 'CANCER DIAGNOSIS' frame. Table 1 summarizes the frames created from the 79 selected papers, with corresponding perelement references.…”

Section: Frame Relationsmentioning

confidence: 99%

“…Frame Elements References CANCER DIAGNOSIS NAME: cancer type [44], [45], [42], [46], [47], [48], [49], [50], [51] ANATOMICAL SITE: the location description of the finding (including primary and metastatic sites) [45], [52], [42], [53], [54], [55], [25], [27], [56], [57] HISTOLOGY: histological description (e.g. carcinoma) [44], [52], [58], [55], [53], [54], [4], [27], [59], [43], [57] GRADE: appearance of the cancerous cells, can be frame with further information (GRADING VALUE) [44], [52], [54], [4], [48], [27], [59], [60], [43], [61], [62] INVASION TYPE: the stage or level of invasion [52] TUMOR BLOCK: tissue cores removed from regions of interest in paraffinembedded tissues (e.g.…”

Section: Framementioning

confidence: 99%

“…0.6 mm in diameter) [52] TISSUE BANK: identifiers about location of tissue samples within an institution [52] STATUS: whether confirmed, suspected and there is no evidence of finding (e.g. probable, definite, without) [42], [57] RECURRENT STATUS: the value of recurrent status [42], [63], [57] TEMPORAL INFORMATION: refers to information about time (e.g., year, month, and date, 2007-08-04) [42], [57] SPECIMEN TYPE: the type of specimen involved in diagnosis [53] LATERALITY: describes the side of a paired organ associated with origin of the primary cancer [53], [54], [25], [48], [64], [27], [51] TUMOR SIZE: how large across the tumor is at its widest point (part of cancer staging) [52], [53], [54], [25], [48], [65], [59], [60], [62], [57] TNM STAGE: cancer staging system, can be a separate frame with further information (TNM CLASSIFICATION) [55], [53], [2], [3], [25], [66], [67], [60], [50], [40], [61] EXTENSION: direct extension of tumor [53] UNCERTAINTY: used to differentiate clinical suspicions from conclusive findings (e.g., possible, likely) [68],…”

Section: Framementioning

confidence: 99%

“…NEGATION: existence/negation of diagnosis (e.g., no, positive) [68], [57] DISEASE: disease related concepts related to diagnosis (disease stage and severity) [11], [25] DISEASE EXTENT: determine extent of disease (e.g., non-invasive, invasive, or metastatic) [68], [69], [57] STAGE: the overall stage of cancer (e.g. Stage 0, Stage I) [70], [52], [42], [54], [25], [69], [40], [43], [61], [57] EXISTENCE: existence description of the finding [45] TEMPORAL MODIFIER: temporal modifiers of the finding [45] ASSOCIATION: associations with other findings (may or may not be related to cancer) (e.g. causal, differential interpretation, and co-occurring) [45] BREAST CANCER DIAGNOSIS STATUS OF CANCER TYPES: presence or absence of various types of breast cancer (e.g.…”

Section: Frame Frame Elements Referencesmentioning

confidence: 99%

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A frame semantic overview of NLP-based information extraction for cancer-related EHR notes

Datta

Bernstam

Roberts

2019

Journal of Biomedical Informatics

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Objective:There is a lot of information about cancer in Electronic Health Record (EHR) notes that can be useful for biomedical research provided natural language processing (NLP) methods are available to extract and structure this information. In this paper, we present a scoping review of existing clinical NLP literature for cancer. Methods:We identified studies describing an NLP method to extract specific cancer-related information from EHR sources from PubMed, Google Scholar, ACL Anthology, and existing reviews. Two exclusion criteria were used in this study. We excluded articles where the extraction techniques used were too broad to be represented as frames (e.g., document classification) and also where very low-level extraction methods were used (e.g. simply identifying clinical concepts). 79 articles were included in the final review. We organized this information according to frame semantic principles to help identify common areas of overlap and potential gaps.Results: Frames were created from the reviewed articles pertaining to cancer information such as cancer diagnosis, tumor description, cancer procedure, breast cancer diagnosis, prostate cancer diagnosis and pain in prostate cancer patients. These frames included both a definition as well as specific frame elements (i.e. extractable attributes). We found that cancer diagnosis was the most common frame among the reviewed papers (36 out of 79), with recent work focusing on extracting information related to treatment and breast cancer diagnosis. Conclusion:The list of common frames described in this paper identifies important cancerrelated information extracted by existing NLP techniques and serves as a useful resource for future researchers requiring cancer information extracted from EHR notes. We also argue, due to the heavy duplication of cancer NLP systems, that a general purpose resource of annotated cancer frames and corresponding NLP tools would be valuable.

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Section: Frame Relationsmentioning

confidence: 99%

Section: Framementioning

confidence: 99%

Section: Framementioning

confidence: 99%

Section: Frame Frame Elements Referencesmentioning

confidence: 99%

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A frame semantic overview of NLP-based information extraction for cancer-related EHR notes

Datta

Bernstam

Roberts

2019

Journal of Biomedical Informatics

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“…The classic case of report structuring for radiology is the MedLee system (Friedman, 2000), although there have been more recent examples that incorporate machine-learning approaches (Taira, Soderland, & Jakobovits, 2001). For cases which target cancer-related information, there are several cases of both rule-based and hybrid methods for structuring tumor information from pathology reports (Coden et al, 2009;Nguyen, Moore, O'dwyer, & Philpot, 2015;Ou & Patrick, 2014;Ping et al, 2013). Of course, there are other template extraction tasks from radiology reports, unrelated to cancer, such as for abnormal knee MRI findings (Spasić, Livsey, Keane, & Nenadić, 2014).…”

Section: Related Workmentioning

confidence: 99%

Classifying tumor event attributes in radiology reports

Yim

Kwan

Yetisgen

2017

Asso for Info Science & Tech

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Radiology reports contain vital diagnostic information that characterizes patient disease progression. However, information from reports is represented in free text, which is difficult to query against for secondary use. Automatic extraction of important information, such as tumor events using natural language processing, offers possibilities in improved clinical decision support, cohort identification, and retrospective evidence-based research for cancer patients. The goal of this work was to classify tumor event attributes: negation, temporality, and malignancy, using biomedical ontology and linguistically enriched features. We report our results on an annotated corpus of 101 hepatocellular carcinoma patient radiology reports, and show that the improved classification improves overall template structuring. Classification performances for negation identification, past temporality classification, and malignancy classification were at 0.94, 0.62, and 0.77 F1, respectively. Incorporating the attributes into full templates led to an improvement of 0.72 F1 for tumorrelated events over a baseline of 0.65 F1. Improvement of negation, malignancy, and temporality classifications led to significant improvements in template extraction for the majority of categories. We present our machinelearning approach to identifying these several tumor event attributes from radiology reports, as well as highlight challenges and areas for improvement.

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Natural Language Processing in Oncology

et al. 2016

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More automated means of leveraging unstructured data from daily clinical practice is crucial as therapeutic options and access to individual-level health information increase. Research-minded oncologists may push the avenues of evidence-based research by taking advantage of the new technologies available with clinical NLP. As continued progress is made with applying NLP toward oncological research, incremental gains will lead to large impacts, building a cost-effective infrastructure for advancing cancer care.

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Information Extraction for Tracking Liver Cancer Patients' Statuses: From Mixture of Clinical Narrative Report Types

Abstract: The application was successfully applied to the various types of narrative clinical reports. It might be applied to the key extraction for other types of cancer patients.

Cited by 18 publications

References 28 publications

A frame semantic overview of NLP-based information extraction for cancer-related EHR notes

A frame semantic overview of NLP-based information extraction for cancer-related EHR notes

Classifying tumor event attributes in radiology reports

Natural Language Processing in Oncology

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