Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer

Bitterman, Danielle S.; Miller, Timothy A.; Mak, Raymond H.; Savova, Guergana

doi:10.1016/j.ijrobp.2021.01.044

Cited by 48 publications

(28 citation statements)

References 124 publications

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“…Such methods are used in other areas of radiotherapy and could be adapted to incident reporting. 21 Some limitations are also revealed by this article. The dataset used in this article was collected from one cancer center with only five linacs in service.…”

Section: Discussionmentioning

confidence: 85%

Topic modeling of maintenance logs for linac failure modes and trends identification

Yun

Carlone

Liu

2021

J Applied Clin Med Phys

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Medical linear accelerators (linacs) can fail in a multitude of different manners due to complex structures. An unclear identification of failure modes occurring constantly is a major obstacle to maintenance arrangements, thereby may increasing downtime. This study aims to use natural language processing techniques to deal with the unformatted maintenance logs to identify the linac failure modes and trends over time. Materials and methods:The data used in our study are unformatted narrative maintenance logs recording linac conditions and repair actions. The latent Dirichlet allocation-based topic modeling method was used to identify topics and keywords regarding the failure modes. The temporal analysis method was applied to examine the variation of failure modes over 20 years. Results: Based on the output of the topic modeling, 28 topics and keywords with frequency ranking were generated automatically. The latent failure modes in topics were identified and classified into six main subsystems of linacs. Furthermore, by using the temporal analysis method, the trends of all failure modes over 20 years were illustrated. Half of the topics demonstrated variations with three different patterns, namely periodic, increasing, and decreasing. Conclusions:The results of our study validated the effectiveness of using the topic modeling method to automatically analyze narrative maintenance logs. With domain knowledge, failure modes of linacs can be identified and categorized quantitatively.

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Section: Discussionmentioning

confidence: 85%

Topic modeling of maintenance logs for linac failure modes and trends identification

Yun

Carlone

Liu

2021

J Applied Clin Med Phys

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“…A major limitation of disparities research is that much of the clinical information, and especially information regarding race, ethnicity, and social determinants of health, has traditionally been documented as unstructured data in clinical text, and therefore is not readily analyzable at large scales. NLP, which aims to convert human language into representations that can be extracted and analyzed by computers, offers an avenue to glean the wealth of data within these texts to further our understanding of cancer care and outcomes across disparate populations [45][46][47]. Owing to major advances in deep learning algorithms for textual analysis, especially large contextual language models [48], NLP is now primed to make meaningful inroads in improving RWD analysis.…”

Section: Natural Language Processingmentioning

confidence: 99%

“…Owing to major advances in deep learning algorithms for textual analysis, especially large contextual language models [48], NLP is now primed to make meaningful inroads in improving RWD analysis. There is an emerging body of work on cancer phenotyping and cohort development, but limited research into NLP methods to measure and assess cancer disparities [45][46][47]. One recent study used NLP to assist assessment of breast cancer guideline-concordant care from free text components of a cancer registry and found that receipt of non-guideline concordant care did not explain breast cancer mortality disparities across race [49].…”

Section: Natural Language Processingmentioning

confidence: 99%

Clinical Informatics Approaches to Understand and Address Cancer Disparities

2022

Self Cite

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Objectives: Disparities in cancer incidence and outcomes across race, ethnicity, gender, socioeconomic status, and geography are well-documented, but their etiologies are often poorly understood and multifactorial. Clinical informatics can provide tools to better understand and address these disparities by enabling high-throughput analysis of multiple types of data. Here, we review recent efforts in clinical informatics to study and measure disparities in cancer. Methods: We carried out a narrative review of clinical informatics studies related to cancer disparities and bias published from 2018-2021, with a focus on domains such as real-world data (RWD) analysis, natural language processing (NLP), radiomics, genomics, proteomics, metabolomics, and metagenomics. Results: Clinical informatics studies that investigated cancer disparities across race, ethnicity, gender, and age were identified. Most cancer disparities work within clinical informatics used RWD analysis, NLP, radiomics, and genomics. Emerging applications of clinical informatics to understand cancer disparities, including proteomics, metabolomics, and metagenomics, were less well represented in the literature but are promising future research avenues. Algorithmic bias was identified as an important consideration when developing and implementing cancer clinical informatics techniques, and efforts to address this bias were reviewed. Conclusions: In recent years, clinical informatics has been used to probe a range of data sources to understand cancer disparities across different populations. As informatics tools become integrated into clinical decision-making, attention will need to be paid to ensure that algorithmic bias does not amplify existing disparities. In our increasingly interconnected medical systems, clinical informatics is poised to untap the full potential of multi-platform health data to address cancer disparities.

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“…2,3 NLP can expedite the extraction of study variables i.e., progression status and derivation of cancer outcomes from clinical notes for more timely, scalable, and cost-effective to support use cases including but not limited to clinical trial matching, genotype/phenotype studies, pharmacoepidemiology, and cancer registries. [4][5][6] Leveraging natural language processing for lung cancer research Although NLP has been used to support cancer research more broadly, [5][6][7][8][9][10] the development of NLP algorithms to extract evidence of progression from clinical notes to support lung cancer research is still in its infancy. For example, Krishner et al trained a logistic regression model using metastatic terms related to progression in bladder cancer, melanoma, non-small cell lung cancer (NSCLC), breast cancer, colorectal cancer, prostate cancer, and renal cell carcinoma.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classifying progression status statements from radiology exams among non-small cell lung cancer patients using natural language processing

Davoudi

Doucette

et al. 2021

Preprint

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Although NLP has been used to support cancer research more broadly, the development of NLP algorithms to extract evidence of progression from clinical notes to support lung cancer research is still in its infancy. In this study, we trained supervised machine learning classifiers using rich semantic features to detect and classify statements of progression status from radiology exams. Our progression status classifier achieves high F1-scores for detecting and discerning progression (0.80), stable (0.82), and not relevant (0.92) sentences, demonstrating promising performance. We are actively integrating these extractions with structured electronic health record data using ontologies to instantiate a longitudinal model of progression among non-small cell lung cancer patients.

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Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer

Cited by 48 publications

References 124 publications

Topic modeling of maintenance logs for linac failure modes and trends identification

Topic modeling of maintenance logs for linac failure modes and trends identification

Clinical Informatics Approaches to Understand and Address Cancer Disparities

Classifying progression status statements from radiology exams among non-small cell lung cancer patients using natural language processing

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