Extracting comprehensive clinical information for breast cancer using deep learning methods

Zhang, Xiaohui; Zhang, Yaoyun; Zhang, Qin; Ren, Ying; Qiu, Tinglin; Ma, Jin; Sun, Qiang

doi:10.1016/j.ijmedinf.2019.103985

Cited by 121 publications

(72 citation statements)

References 12 publications

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“…BERT set new performance records across nearly all natural language processing benchmarks and, for the first time, achieved human‐level performance on several of these. BERT and related technologies are being applied to unstructured biomedical 215,216 and clinical texts 217,218 . Translation of these new technologies into the clinical domain is still in the early stages, but progress is accelerating.…”

Section: Data Mining Natural Language Processing and Ai Using Big Dmentioning

confidence: 99%

Innovations in research and clinical care using patient‐generated health data

Jim

Hoogland

Brownstein

et al. 2020

CA A Cancer J Clinicians

113

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Patient-generated health data (PGHD), or health-related data gathered from patients to help address a health concern, are used increasingly in oncology to make regulatory decisions and evaluate quality of care. PGHD include self-reported health and treatment histories, patient-reported outcomes (PROs), and biometric sensor data. Advances in wireless technology, smartphones, and the Internet of Things have facilitated new ways to collect PGHD during clinic visits and in daily life. The goal of the current review was to provide an overview of the current clinical, regulatory, technological, and analytic landscape as it relates to PGHD in oncology research and care. The review begins with a rationale for PGHD as described by the US Food and Drug Administration, the Institute of Medicine, and other regulatory and scientific organizations. The evidence base for clinic-based and remote symptom monitoring using PGHD is described, with an emphasis on PROs. An overview is presented of current approaches to digital phenotyping or device-based, real-time assessment of biometric, behavioral, self-report, and performance data. Analytic opportunities regarding PGHD are envisioned in the context of big data and artificial intelligence in medicine. Finally, challenges and solutions for the integration of PGHD into clinical care are presented. The challenges include electronic medical record integration of PROs and biometric data, analysis of large and complex biometric data sets, and potential clinic workflow redesign. In addition, there is currently more limited evidence for the use of biometric data relative to PROs. Despite these challenges, the potential benefits of PGHD make them increasingly likely to be integrated into oncology research and clinical care.

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Section: Data Mining Natural Language Processing and Ai Using Big Dmentioning

confidence: 99%

Innovations in research and clinical care using patient‐generated health data

Jim

Hoogland

Brownstein

et al. 2020

CA A Cancer J Clinicians

113

View full text Add to dashboard Cite

show abstract

“…For example, groups have used NLP across various medical disciplines to extract key clinical data from text documents such as patient records, 38 discharge summaries, [38][39][40] pathology reports, 40,41 and radiology reports. 40,42 These techniques can be adapted to assist spine surgeons via data extraction. For example, Tan et al 43 trained a model to identify information pertaining to low back pain (LBP) from lumbar spine imaging reports.…”

Section: Data Queryingmentioning

confidence: 99%

Applications of Machine Learning Using Electronic Medical Records in Spine Surgery

et al. 2019

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Developments in machine learning in recent years have precipitated a surge in research on the applications of artificial intelligence within medicine. Machine learning algorithms are beginning to impact medicine broadly, and the field of spine surgery is no exception. Electronic medical records are a key source of medical data that can be leveraged for the creation of clinically valuable machine learning algorithms. This review examines the current state of machine learning using electronic medical records as it applies to spine surgery. Studies across the electronic medical record data domains of imaging, text, and structured data are reviewed. Discussed applications include clinical prognostication, preoperative planning, diagnostics, and dynamic clinical assistance, among others. The limitations and future challenges for machine learning research using electronic medical records are also discussed.

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“…Of all the deep learning models, bidirectional long short-term memory (BiLSTM) is a variant of the Recurrent Neural Network (RNN), which could effectively capture long-range related information effectively in NER task. By splitting the neurons into two directions of a text sequence, BiLSTM could learn forward and backward information of input words, Furthermore, BiLSTM with CRF (BiLSTM-CRF), proved its validity that outperformed the traditional models especially in Chinese clinical NER tasks [17][18][19][20][21]. After information extraction to obtain the structured features, NLP can be further implemented on clinical tasks, such as disease studies [22][23][24], drug-related studies [25,26], and clinical workflow optimization [27].…”

Section: Introductionmentioning

confidence: 99%

A Natural Language Processing Pipeline of Chinese Free-Text Radiology Reports for Liver Cancer Diagnosis

Liu

Zhang

et al. 2020

IEEE Access

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Despite the rapid development of natural language processing (NLP) implementation in electronic medical records (EMRs), Chinese EMRs processing remains challenging due to the limited corpus and specific grammatical characteristics, especially for radiology reports. In this study, we designed an NLP pipeline for the direct extraction of clinically relevant features from Chinese radiology reports, which is the first key step in computer-aided radiologic diagnosis. The pipeline was comprised of named entity recognition, synonyms normalization, and relationship extraction to finally derive the radiological features composed of one or more terms. In named entity recognition, we incorporated lexicon into deep learning model bidirectional long short-term memory-conditional random field (BiLSTM-CRF), and the model finally achieved an F1 score of 93.00%. With the extracted radiological features, least absolute shrinkage and selection operator and machine learning methods (support vector machine, random forest, decision tree, and logistic regression) were used to build the classifiers for liver cancer prediction. For liver cancer diagnosis, random forest had the highest predictive performance in liver cancer diagnosis (F1 score 86.97%, precision 87.71%, and recall 86.25%). This work was a comprehensive NLP study focusing on Chinese radiology reports and the application of NLP in cancer risk prediction. The proposed NLP pipeline for the radiological feature extraction could be easily implemented in other kinds of Chinese clinical texts and other disease predictive tasks.

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Extracting comprehensive clinical information for breast cancer using deep learning methods

Cited by 121 publications

References 12 publications

Innovations in research and clinical care using patient‐generated health data

Innovations in research and clinical care using patient‐generated health data

Applications of Machine Learning Using Electronic Medical Records in Spine Surgery

A Natural Language Processing Pipeline of Chinese Free-Text Radiology Reports for Liver Cancer Diagnosis

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