Longitudinal analysis of pain in patients with metastatic prostate cancer using natural language processing of medical record text

PURPOSE Prostate cancer (PCa) is among the leading causes of cancer deaths. While localized PCa has a 5-year survival rate approaching 100%, this rate drops to 31% for metastatic prostate cancer (mPCa). Thus, timely identification of mPCa is a crucial step toward measuring and improving access to innovations that reduce PCa mortality. Yet, methods to identify patients diagnosed with mPCa remain elusive. Cancer registries provide detailed data at diagnosis but are not updated throughout treatment. This study reports on the development and validation of a natural language processing (NLP) algorithm deployed on oncology, urology, and radiology clinical notes to identify patients with a diagnosis or history of mPCa in the Department of Veterans Affairs. PATIENTS AND METHODS Using a broad set of diagnosis and histology codes, the Veterans Affairs Corporate Data Warehouse was queried to identify all Veterans with PCa. An NLP algorithm was developed to identify patients with any history or progression of mPCa. The NLP algorithm was prototyped and developed iteratively using patient notes, grouped into development, training, and validation subsets. RESULTS A total of 1,144,610 Veterans were diagnosed with PCa between January 2000 and October 2020, among which 76,082 (6.6%) were identified by NLP as having mPCa at some point during their care. The NLP system performed with a specificity of 0.979 and sensitivity of 0.919. CONCLUSION Clinical documentation of mPCa is highly reliable. NLP can be leveraged to improve PCa data. When compared to other methods, NLP identified a significantly greater number of patients. NLP can be used to augment cancer registry data, facilitate research inquiries, and identify patients who may benefit from innovations in mPCa treatment.

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Natural Language Processing Performance for the Identification of Venous Thromboembolism in an Integrated Healthcare System

Clinical and Applied Thrombosis/Hemostasis ◽

10.1177/10760296211013108 ◽

2021 ◽

Vol 27 ◽

pp. 107602962110131

Author(s):

Bela Woller ◽

Austin Daw ◽

Valerie Aston ◽

Jim Lloyd ◽

Greg Snow ◽

...

Keyword(s):

Natural Language Processing ◽

Venous Thromboembolism ◽

Natural Language ◽

Medical Record ◽

Electronic Medical Record ◽

Ct Angiography ◽

Language Processing ◽

Predictive Value ◽

Chart Review ◽

Imaging Studies

Real-time identification of venous thromboembolism (VTE), defined as deep vein thrombosis (DVT) and pulmonary embolism (PE), can inform a healthcare organization’s understanding of these events and be used to improve care. In a former publication, we reported the performance of an electronic medical record (EMR) interrogation tool that employs natural language processing (NLP) of imaging studies for the diagnosis of venous thromboembolism. Because we transitioned from the legacy electronic medical record to the Cerner product, iCentra, we now report the operating characteristics of the NLP EMR interrogation tool in the new EMR environment. Two hundred randomly selected patient encounters for which the imaging report assessed by NLP that revealed VTE was present were reviewed. These included one hundred imaging studies for which PE was identified. These included computed tomography pulmonary angiography—CTPA, ventilation perfusion—V/Q scan, and CT angiography of the chest/ abdomen/pelvis. One hundred randomly selected comprehensive ultrasound (CUS) that identified DVT were also obtained. For comparison, one hundred patient encounters in which PE was suspected and imaging was negative for PE (CTPA or V/Q) and 100 cases of suspected DVT with negative CUS as reported by NLP were also selected. Manual chart review of the 400 charts was performed and we report the sensitivity, specificity, positive and negative predictive values of NLP compared with manual chart review. NLP and manual review agreed on the presence of PE in 99 of 100 cases, the presence of DVT in 96 of 100 cases, the absence of PE in 99 of 100 cases and the absence of DVT in all 100 cases. When compared with manual chart review, NLP interrogation of CUS, CTPA, CT angiography of the chest, and V/Q scan yielded a sensitivity = 93.3%, specificity = 99.6%, positive predictive value = 97.1%, and negative predictive value = 99%.

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THE USE OF NATURAL LANGUAGE PROCESSING TOOLS AND THE INTEGRATED ELECTRONIC MEDICAL RECORD (EMR) TO IDENTIFY PATIENTS AT HIGH-RISK FOR TRANSTHYRETIN CARDIAC AMYLOIDOSIS

Journal of the American College of Cardiology ◽

10.1016/s0735-1097(21)02198-7 ◽

2021 ◽

Vol 77 (18) ◽

pp. 839

Author(s):

Trejeeve Martyn ◽

Joshua Saef ◽

Jerry Estep ◽

Patrick Collier ◽

Deborah Kwon ◽

...

Keyword(s):

Natural Language Processing ◽

High Risk ◽

Natural Language ◽

Medical Record ◽

Electronic Medical Record ◽

Language Processing ◽

Cardiac Amyloidosis

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A Processing Model Using Natural Language Processing (NLP) For Narrative Text Of Medical Record For Producing Symptoms Of Mental Disorders

2019 Fourth International Conference on Informatics and Computing (ICIC) ◽

10.1109/icic47613.2019.8985862 ◽

2019 ◽

Author(s):

Sri Mulyana ◽

Sri Hartati ◽

Retantyo Wardoyo ◽

Subandi

Keyword(s):

Natural Language Processing ◽

Mental Disorders ◽

Natural Language ◽

Medical Record ◽

Language Processing ◽

Narrative Text

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Automated Identification of Postoperative Complications Within an Electronic Medical Record Using Natural Language Processing

JAMA ◽

10.1001/jama.2011.1204 ◽

2011 ◽

Vol 306 (8) ◽

Cited By ~ 137

Author(s):

Harvey J. Murff ◽

Fern FitzHenry ◽

Michael E. Matheny ◽

Nancy Gentry ◽

Kristen L. Kotter ◽

...

Keyword(s):

Natural Language Processing ◽

Postoperative Complications ◽

Natural Language ◽

Medical Record ◽

Electronic Medical Record ◽

Language Processing ◽

Automated Identification

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A natural language processing algorithm to measure quality prostate cancer care.

Journal of Clinical Oncology ◽

10.1200/jco.2017.35.8_suppl.232 ◽

2017 ◽

Vol 35 (8_suppl) ◽

pp. 232-232 ◽

Cited By ~ 4

Author(s):

Tina Hernandez-Boussard ◽

Panagiotis Kourdis ◽

Rajendra Dulal ◽

Michelle Ferrari ◽

Solomon Henry ◽

...

Keyword(s):

Prostate Cancer ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Specific Antigen ◽

Quality Metrics ◽

Free Text ◽

Data Mining Algorithm ◽

Quality Metric ◽

Quality Reporting

232 Background: Electronic health records (EHRs) are a widely adopted but underutilized source of data for systematic assessment of healthcare quality. Barriers for use of this data source include its vast complexity, lack of structure, and the lack of use of standardized vocabulary and terminology by clinicians. This project aims to develop generalizable algorithms to extract useful knowledge regarding prostate cancer quality metrics from EHRs. Methods: We used EHR ICD-9/10 codes to identify prostate cancer patients receiving care at our academic medical center. Patients were confirmed in the California Cancer Registry (CCR), which provided data on tumor characteristics, treatment data, treatment outcomes and survival. We focused on three potential pretreatment process quality measures, which included documentation within 6 months prior to initial treatment of prostate-specific antigen (PSA), digital rectal exam (DRE) performance, and Gleason score. Each quality metric was defined using target terms and concepts to extract from the EHRs. Terms were mapped to a standardized medical vocabulary or ontology, enabling us to represent the metric elements by a concept domain and its permissible values. The structured representation of the quality metric included rules that accounted for the temporal order of the metric components. Our algorithms used natural language processing for free text annotation and negation, to ensure terms such as ‘DRE deferred’ are appropriately categorized. Results: We identified 2,123 patients receiving prostate cancer treatment between 2008-2016, of whom 1413 (67%) were matched in the CCR. We compared accuracy of our data mining algorithm, a random sample of manual chart review, and the CCR. (See Table.) Conclusions: EHR systems can be used to assess and report quality metrics systematically, efficiently, and with high accuracy. The development of such systems can improve and reduce the burden of quality reporting and potentially reduce costs of measuring quality metrics through automation. [Table: see text]

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Development of an algorithm to identify metastatic prostate cancer in electronic medical records using natural language processing.

Journal of Clinical Oncology ◽

10.1200/jco.2014.32.30_suppl.164 ◽

2014 ◽

Vol 32 (30_suppl) ◽

pp. 164-164 ◽

Cited By ~ 1

Author(s):

Lauren P. Wallner ◽

Julia R. Dibello ◽

Bonnie H. Li ◽

Chengyi Zheng ◽

Wei Yu ◽

...

Keyword(s):

Prostate Cancer ◽

Natural Language Processing ◽

Language Processing ◽

Metastatic Prostate Cancer ◽

Metastatic Cancer ◽

Quality Care ◽

Training Set ◽

High Quality ◽

Radiology Reports ◽

Pathology Reports

164 Background: Prostate cancer patients who develop metastases are a difficult population to identify through administrative diagnostic codes, due to their protracted time to metastases, limited survival and the inconsistent use of specific codes. As a result, research that is needed to inform the delivery of high-quality care in this setting is limited. Therefore, the goal of this study was to develop an algorithm, which utilizes EMR data to identify men who progress to metastatic prostate cancer after diagnosis using natural language processing (NLP). Methods: An electronic algorithm was developed to search unstructured text using NLP to identify progression to metastases among men with a diagnosis of prostate cancer between 1992 and 2010 in a large, diverse cohort of men who were part of an ongoing study focused on prostate cancer mortality. A training set of 449 men who were diagnosed as early stage prostate cancer was used for development. Pathology, radiology and clinic notes were searched from diagnosis until death or loss to follow-up. Pathology reports were searched for mention of adenocarcinoma in the metastatic lesion, radiology reports were searched for abnormal findings consistent with metastases, and clinic notes were searched for mentions of increasing pain or narcotic use related to metastases. Each NLP component was validated against manual review of the corresponding records. Results: Of the 449 men in the training set, 40 (8.9%) were found to have metastatic prostate cancer. The majority of cases had evidence of metastases in their clinic notes (98%). Radiology reports identified 18% of cases, and pathology reports identified 5%. Of the 40 cases identified, 25% did not have a corresponding ICD-9 codes for metastatic cancer. However, 7.5% used ADT, 37.5% had increasing oncology visits and 22.5% had rapidly rising PSA levels. Conclusions: Our results suggest that NLP can be used to identify men with metastatic prostate cancer in the EMR more accurately than diagnosis codes alone. The automated identification of patients with metastatic cancer facilitates quality of care research in this setting to ensure the delivery of appropriate and high-quality care.

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