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Comparative Study
. 2019 Jun:94:103184.
doi: 10.1016/j.jbi.2019.103184. Epub 2019 Apr 20.

Comparison of orthogonal NLP methods for clinical phenotyping and assessment of bone scan utilization among prostate cancer patients

Jean Coquet  1 , Selen Bozkurt  2 , Kathleen M Kan  3 , Michelle K Ferrari  3 , Douglas W Blayney  4 , James D Brooks  5 , Tina Hernandez-Boussard  6
Affiliations
Comparative Study

Comparison of orthogonal NLP methods for clinical phenotyping and assessment of bone scan utilization among prostate cancer patients

Jean Coquet et al. J Biomed Inform. 2019 Jun.

Abstract

Objective: Clinical care guidelines recommend that newly diagnosed prostate cancer patients at high risk for metastatic spread receive a bone scan prior to treatment and that low risk patients not receive it. The objective was to develop an automated pipeline to interrogate heterogeneous data to evaluate the use of bone scans using a two different Natural Language Processing (NLP) approaches.

Materials and methods: Our cohort was divided into risk groups based on Electronic Health Records (EHR). Information on bone scan utilization was identified in both structured data and free text from clinical notes. Our pipeline annotated sentences with a combination of a rule-based method using the ConText algorithm (a generalization of NegEx) and a Convolutional Neural Network (CNN) method using word2vec to produce word embeddings.

Results: A total of 5500 patients and 369,764 notes were included in the study. A total of 39% of patients were high-risk and 73% of these received a bone scan; of the 18% low risk patients, 10% received one. The accuracy of CNN model outperformed the rule-based model one (F-measure = 0.918 and 0.897 respectively). We demonstrate a combination of both models could maximize precision or recall, based on the study question.

Conclusion: Using structured data, we accurately classified patients' cancer risk group, identified bone scan documentation with two NLP methods, and evaluated guideline adherence. Our pipeline can be used to provide concrete feedback to clinicians and guide treatment decisions.

Keywords: Electronic health records; Machine learning; Natural language processing; Prostate cancer.

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Conflict of interest statement

Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Figures

Figure 1.
Figure 1.
Illustration of our approach to detect if patients underwent a bone scan.
Figure 2.
Figure 2.
Flowchart to select the final cohort, to classify the patients and to detect if patients underwent a bone scan.
Figure 3.
Figure 3.
Evaluation of NLP model predictions in 100 manual annotated notes. Each node represents a note. If a note mentions that a bone scan was performed, the node is black. If a note mentions that the patient had not received a bone scan or that a bone scan is planned then the note is white.
Figure 4.
Figure 4.
Guideline adherence. Percentage of patients undergoing a bone scan stratified by risk group according to the NCCN and AUA guidelines.
See this image and copyright information in PMC

References

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