scholarly journals Using Natural Language Processing to Improve Efficiency of Manual Chart Abstraction in Research: The Case of Breast Cancer Recurrence

2014 ◽  
Vol 179 (6) ◽  
pp. 749-758 ◽  
Author(s):  
David S. Carrell ◽  
Scott Halgrim ◽  
Diem-Thy Tran ◽  
Diana S. M. Buist ◽  
Jessica Chubak ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Cancer Recurrence ◽  
Breast Cancer Recurrence ◽  
Improve Efficiency ◽  
Chart Abstraction

Using natural language processing to assess palliative care processes in cancer patients receiving venting gastrostomy tube.

2017 ◽  
Vol 35 (31_suppl) ◽  
pp. 7-7
Author(s):  
Charlotta Lindvall ◽  
Elizabeth J. Lilley ◽  
Zara Cooper ◽  
Alexander W. Forsyth ◽  
Regina Barzilay ◽  
...  
Keyword(s):  
Palliative Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Cancer Patients ◽  
Advanced Cancer ◽  
High Precision ◽  
Language Processing ◽  
Gastrostomy Tube ◽  
Hospice Referral ◽  
Chart Abstraction

7 Background: Natural Language Processing (NLP) presents a novel method of extracting text-embedded information from the electronic health record (EHR) to improve routine assessment of palliative quality metrics such as timely advance care planning (ACP), palliative care provision (PC), and hospice referral. Methods: We identified cancer patients (ICD-9-CM codes 140-209) who received a gastrostomy tube (ICD-9-CM 43.11, 43.19, 44.32; CPT code 49440) from Jan 1, 2012, to Mar 31, 2016 at an academic medical center. We used NLP to identify palliative indication for gastrostomy tube placement by labeling clinical notes from the EHR containing the key word “venting” near the time of the procedure. Documentation of ACP, PC, and hospice referral was identified by NLP using a validated key term library. The sensitivity and specificity of the NLP method was determined by comparing outcome identification to manual chart abstraction performed by two clinicians. All NLP code was written in the open-source programming language Python. Results: NLP was performed for 75,626 documents. Among 305 cancer patients who underwent gastrostomy, 75 (24.6%) were classified by NLP as having a palliative indication for the procedure compared to 72 patients (23.6%) classified by human coders. Manual chart abstraction took > 2,600 times longer than NLP (28 hrs vs. 38 seconds). NLP identified the correct patients with high precision (0.92) and recall (0.96). ACP was documented during the index admission for 89.3% of patients. PC was documented for 85.7% and hospice referral was documented for 64.3% of these patients with advanced cancer during the index hospitalization. NLP identified ACP, PC and hospice referral with high precision (0.88-1.0) and recall (0.92-1.0) compared to human coders. Median survival was 37 days following gastrostomy tube procedure. Conclusions: NLP can greatly speed the assessment of established palliative quality metrics with an accuracy approaching that of human coders. These methods offer opportunities for facilitate quality improvement in palliative care for patients with advanced cancer.

scholarly journals Using natural language processing and machine learning to identify breast cancer local recurrence

2018 ◽  
Vol 19 (S17) ◽  
Author(s):  
Zexian Zeng ◽  
Sasa Espino ◽  
Ankita Roy ◽  
Xiaoyu Li ◽  
Seema A. Khan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Local Recurrence ◽  
Natural Language ◽  
Language Processing

Women screened for breast cancer are dying from lung cancer: An opportunity to improve lung cancer screening in a mammography population

2021 ◽  
pp. 096914132110130
Author(s):  
Kim L Sandler ◽  
Diane N Haddad ◽  
Alexis B Paulson ◽  
Travis J Osterman ◽  
Carolyn C Scott ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lung Cancer ◽  
Natural Language Processing ◽  
Cancer Screening ◽  
Natural Language ◽  
Language Processing ◽  
Chart Review ◽  
Lung Cancer Screening ◽  
Smoking History ◽  
Lung Screening

Objective Lung cancer is the leading cancer killer in women, resulting in more deaths than breast, cervical and ovarian cancer combined. Screening for lung cancer has been shown to significantly reduce mortality, with some evidence that women may have a greater benefit. This study demonstrates that a population of women being screened for breast cancer may greatly benefit from screening for lung cancer. Methods Data from 18,040 women who were screened for breast cancer in 2015 at two imaging facilities that also performed lung screening were reviewed. A natural language-processing algorithm followed by a manual chart review identified women eligible for lung cancer screening by U.S. Preventive Services Task Force (USPSTF) criteria. A chart review of these eligible women was performed to determine subsequent enrollment in a lung screening program (2016–2019), current screening eligibility, cancer diagnoses and cancer-related outcomes. Results Natural language processing identified 685 women undergoing screening mammography who were also potentially eligible for lung screening based on age and smoking history. Manual chart review confirmed 251 were eligible under USPSTF criteria. By June 2019, 63 (25%) had enrolled in lung screening, of which three were diagnosed with screening-detected lung cancer resulting in zero deaths. Of 188 not screened, seven were diagnosed with lung cancer resulting in five deaths by study end. Four women received a diagnosis of breast cancer with no deaths. Conclusion Women screened for breast cancer are dying from lung cancer. We must capitalize on reducing barriers to improve screening for lung cancer among high-risk women.

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