Use of Natural Language Processing to Extract Clinical Cancer Phenotypes from Electronic Medical Records

Savova, Guergana; Danciu, Ioana; Alamudun, Folami; Miller, Timothy A.; Lin, Chen; Bitterman, Danielle S.; Tourassi, Georgia D.; Warner, Jeremy L.

doi:10.1158/0008-5472.can-19-0579

Cited by 121 publications

(94 citation statements)

References 66 publications

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“…Koleck et al [41] summarized the use of NLP to analyze symptom information documented in EHR free-text narratives as an indication of diseases and similar to the previous survey found little coverage of DL methods in this application area as well. Savova et al [42] reviewed the current state of clinical NLP with respect to oncology and cancer phenotyping from EHR. Datta et al [43] focused on an even more specialized use case-the lexical representation required for the extraction of cancer information from EHR notes in a frame-semantic format.…”

Section: Diseasesmentioning

confidence: 99%

Medical Information Extraction in the Age of Deep Learning

Hahn

Oleynik

2020

Yearb Med Inform

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Objectives: We survey recent developments in medical Information Extraction (IE) as reported in the literature from the past three years. Our focus is on the fundamental methodological paradigm shift from standard Machine Learning (ML) techniques to Deep Neural Networks (DNNs). We describe applications of this new paradigm concentrating on two basic IE tasks, named entity recognition and relation extraction, for two selected semantic classes—diseases and drugs (or medications)—and relations between them. Methods: For the time period from 2017 to early 2020, we searched for relevant publications from three major scientific communities: medicine and medical informatics, natural language processing, as well as neural networks and artificial intelligence. Results: In the past decade, the field of Natural Language Processing (NLP) has undergone a profound methodological shift from symbolic to distributed representations based on the paradigm of Deep Learning (DL). Meanwhile, this trend is, although with some delay, also reflected in the medical NLP community. In the reporting period, overwhelming experimental evidence has been gathered, as illustrated in this survey for medical IE, that DL-based approaches outperform non-DL ones by often large margins. Still, small-sized and access-limited corpora create intrinsic problems for data-greedy DL as do special linguistic phenomena of medical sublanguages that have to be overcome by adaptive learning strategies. Conclusions: The paradigm shift from (feature-engineered) ML to DNNs changes the fundamental methodological rules of the game for medical NLP. This change is by no means restricted to medical IE but should also deeply influence other areas of medical informatics, either NLP- or non-NLP-based.

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Section: Diseasesmentioning

confidence: 99%

Medical Information Extraction in the Age of Deep Learning

Hahn

Oleynik

2020

Yearb Med Inform

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“…Recent advancements in machine learning (ML) reveal opportunities for ML to transform cancer care. The field has seen progress in early detection of cancers, and improved diagnostic accuracy, personalized therapeutics, and clinical workflows 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 . In the last two years, the field has also experienced substantial growth in FDA approvals for algorithms including oncology applications for the detection of suspicious lesions and clinical decision support (CDS) 47 .…”

Section: Resultsmentioning

confidence: 99%

“…Recent reviews have highlighted a number of promising ML-based knowledge extractions from unstructured and semi-structured oncology data from EHRs, social media platforms, and online health communities 20 21 . Using EHR clinical documents, Savova et al , created a natural language processing (NLP) system, DeepPhe, to generate cancer phenotypes 22 , which could help reduce time spent reviewing clinical documents.…”

Section: Resultsmentioning

confidence: 99%

From Patient Engagement to Precision Oncology: Leveraging Informatics to Advance Cancer Care

et al. 2020

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Objectives: Conduct a survey of the literature for advancements in cancer informatics over the last three years in three specific areas where there has been unprecedented growth: 1) digital health; 2) machine learning; and 3) precision oncology. We also highlight the ethical implications and future opportunities within each area. Methods: A search was conducted over a three-year period in two electronic databases (PubMed, Google Scholar) to identify peer-reviewed articles and conference proceedings. Search terms included variations of the following: neoplasms[MeSH], informatics[MeSH], cancer, oncology, clinical cancer informatics, medical cancer informatics. The search returned too many articles for practical review (23,994 from PubMed and 23,100 from Google Scholar). Thus, we conducted searches of key PubMed-indexed informatics journals and proceedings. We further limited our search to manuscripts that demonstrated a clear focus on clinical or translational cancer informatics. Manuscripts were then selected based on their methodological rigor, scientific impact, innovation, and contribution towards cancer informatics as a field or on their impact on cancer care and research. Results: Key developments and opportunities in cancer informatics research in the areas of digital health, machine learning, and precision oncology were summarized. Conclusion: While there are numerous innovations in the field of cancer informatics to advance prevention and clinical care, considerable challenges remain related to data sharing and privacy, digital accessibility, and algorithm biases and interpretation. The implementation and application of these findings in cancer care necessitates further consideration and research.

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“…A variety of NLP methods, with an overall shift towards deep learning, were observed in the last four years. Deep learning can automatically find mathematically and computationally convenient phrases or abstracts from raw data that can be used for classification without having explicit defined feature [25].…”

Section: Wieneke Et Al 2015 (Unnamed)mentioning

confidence: 99%

Machine Learning in Healthcare Communication

Siddique

Chow

2021

Encyclopedia

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Machine learning (ML) is a study of computer algorithms for automation through experience. ML is a subset of artificial intelligence (AI) that develops computer systems, which are able to perform tasks generally having need of human intelligence. While healthcare communication is important in order to tactfully translate and disseminate information to support and educate patients and public, ML is proven applicable in healthcare with the ability for complex dialogue management and conversational flexibility. In this topical review, we will highlight how the application of ML/AI in healthcare communication is able to benefit humans. This includes chatbots for the COVID-19 health education, cancer therapy, and medical imaging.

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Use of Natural Language Processing to Extract Clinical Cancer Phenotypes from Electronic Medical Records

Cited by 121 publications

References 66 publications

Medical Information Extraction in the Age of Deep Learning

Medical Information Extraction in the Age of Deep Learning

From Patient Engagement to Precision Oncology: Leveraging Informatics to Advance Cancer Care

Machine Learning in Healthcare Communication

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