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.

Abstract WMP92: Towards High-Precision Stroke Classification Using Natural Language Processing

Stroke ◽  
2018 ◽  
Vol 49 (Suppl_1) ◽  
Author(s):  
Jennifer J Majersik ◽  
Danielle Mowery ◽  
Mingyuan Zhang ◽  
Brent Hill ◽  
Lisa A Cannon-Albright ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
High Precision ◽  
Language Processing ◽  
Stroke Classification

Relational paraphrase acquisition from Wikipedia: The WRPA method and corpus

2013 ◽  
Vol 21 (3) ◽  
pp. 355-389 ◽  
Author(s):  
M. VILA ◽  
H. RODRÍGUEZ ◽  
M. A. MARTÍ
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
High Precision ◽  
Language Processing ◽  
The Body ◽  
Free Text ◽  
String Similarity ◽  
Distant Learning ◽  
Structured Information ◽  
Anchor Points

AbstractParaphrase corpora are an essential but scarce resource in Natural Language Processing. In this paper, we present the Wikipedia-based Relational Paraphrase Acquisition (WRPA) method, which extracts relational paraphrases from Wikipedia, and the derived WRPA paraphrase corpus. The WRPA corpus currently covers person-related and authorship relations in English and Spanish, respectively, suggesting that, given adequate Wikipedia coverage, our method is independent of the language and the relation addressed. WRPA extracts entity pairs from structured information in Wikipedia applying distant learning and, based on the distributional hypothesis, uses them as anchor points for candidate paraphrase extraction from the free text in the body of Wikipedia articles. Focussing on relational paraphrasing and taking advantage of Wikipedia-structured information allows for an automatic and consistent evaluation of the results. The WRPA corpus characteristics distinguish it from other types of corpora that rely on string similarity or transformation operations. WRPA relies on distributional similarity and is the result of the free use of language outside any reformulation framework. Validation results show a high precision for the corpus.

scholarly journals Natural language processing and recurrent network models for identifying genomic mutation-associated cancer treatment change from patient progress notes

JAMIA Open ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 139-149 ◽  
Author(s):  
Meijian Guan ◽  
Samuel Cho ◽  
Robin Petro ◽  
Wei Zhang ◽  
Boris Pasche ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Cancer Patients ◽  
Language Processing ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Free Text ◽  
Treatment Change ◽  
Progress Notes

Abstract Objectives Natural language processing (NLP) and machine learning approaches were used to build classifiers to identify genomic-related treatment changes in the free-text visit progress notes of cancer patients. Methods We obtained 5889 deidentified progress reports (2439 words on average) for 755 cancer patients who have undergone a clinical next generation sequencing (NGS) testing in Wake Forest Baptist Comprehensive Cancer Center for our data analyses. An NLP system was implemented to process the free-text data and extract NGS-related information. Three types of recurrent neural network (RNN) namely, gated recurrent unit, long short-term memory (LSTM), and bidirectional LSTM (LSTM_Bi) were applied to classify documents to the treatment-change and no-treatment-change groups. Further, we compared the performances of RNNs to 5 machine learning algorithms including Naive Bayes, K-nearest Neighbor, Support Vector Machine for classification, Random forest, and Logistic Regression. Results Our results suggested that, overall, RNNs outperformed traditional machine learning algorithms, and LSTM_Bi showed the best performance among the RNNs in terms of accuracy, precision, recall, and F1 score. In addition, pretrained word embedding can improve the accuracy of LSTM by 3.4% and reduce the training time by more than 60%. Discussion and Conclusion NLP and RNN-based text mining solutions have demonstrated advantages in information retrieval and document classification tasks for unstructured clinical progress notes.

Natural Language Processing to Assess End-of-Life Quality Indicators in Cancer Patients Receiving Palliative Surgery

2019 ◽  
Vol 22 (2) ◽  
pp. 183-187 ◽  
Author(s):  
Charlotta Lindvall ◽  
Elizabeth J. Lilley ◽  
Sophia N. Zupanc ◽  
Isabel Chien ◽  
Brooks V. Udelsman ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
End Of Life ◽  
Cancer Patients ◽  
Quality Indicators ◽  
Language Processing ◽  
Life Quality ◽  
Palliative Surgery

scholarly journals Deep-Learning-Based Natural Language Processing of Serial Free-Text Radiological Reports for Predicting Rectal Cancer Patient Survival

2021 ◽  
Vol 11 ◽  
Author(s):  
Sunkyu Kim ◽  
Choong-kun Lee ◽  
Yonghwa Choi ◽  
Eun Sil Baek ◽  
Jeong Eun Choi ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Overall Survival ◽  
Natural Language Processing ◽  
Natural Language ◽  
Cancer Patients ◽  
Transfer Learning ◽  
Language Processing ◽  
Rectal Cancer Patient ◽  
Linguistic Knowledge ◽  
Free Text

Most electronic medical records, such as free-text radiological reports, are unstructured; however, the methodological approaches to analyzing these accumulating unstructured records are limited. This article proposes a deep-transfer-learning-based natural language processing model that analyzes serial magnetic resonance imaging reports of rectal cancer patients and predicts their overall survival. To evaluate the model, a retrospective cohort study of 4,338 rectal cancer patients was conducted. The experimental results revealed that the proposed model utilizing pre-trained clinical linguistic knowledge could predict the overall survival of patients without any structured information and was superior to the carcinoembryonic antigen in predicting survival. The deep-transfer-learning model using free-text radiological reports can predict the survival of patients with rectal cancer, thereby increasing the utility of unstructured medical big data.

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