Dataset Information

Identifying Women at High Risk for Breast Cancer Using Data From the Electronic Health Record Compared With Self-Report.

ABSTRACT: PURPOSE:A barrier to chemoprevention uptake among high-risk women is the lack of routine breast cancer risk assessment in the primary care setting. We calculated breast cancer risk using the Breast Cancer Surveillance Consortium (BCSC) model, accounting for age, race/ethnicity, first-degree family history of breast cancer, benign breast disease, and mammographic density, using data collected from the electronic health records (EHRs) and self-reports. PATIENTS AND METHODS:Among women undergoing screening mammography, we enrolled those age 35 to 74 years without a prior history of breast cancer. Data on demographics, first-degree family history, breast radiology, and pathology reports were extracted from the EHR. We assessed agreement between the EHR and self-report on information about breast cancer risk. RESULTS:Among 9,514 women with known race/ethnicity, 1,443 women (15.2%) met high-risk criteria based upon a 5-year invasive breast cancer risk of 1.67% or greater according to the BCSC model. Among 1,495 women with both self-report and EHR data, more women with a first-degree family history of breast cancer (14.6% v 4.4%) and previous breast biopsies (21.3% v 11.3%) were identified by self-report versus EHR, respectively. However, more women with atypia and lobular carcinoma in situ were identified from the EHR. There was moderate agreement in identification of high-risk women between EHR and self-report data (?, 0.48; 95% CI, 0.42-0.54). CONCLUSION:By using EHR data, we determined that 15% of women undergoing screening mammography had a high risk for breast cancer according to the BCSC model. There was moderate agreement between information on breast cancer risk derived from the EHR and self-report. Examining EHR data may serve as an initial screen for identifying women eligible for breast cancer chemoprevention.

SUBMITTER: Jiang X

PROVIDER: S-EPMC6874029 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Identifying Women at High Risk for Breast Cancer Using Data From the Electronic Health Record Compared With Self-Report.

Jiang Xinyi X McGuinness Julia E JE Sin Margaret M Silverman Thomas T Kukafka Rita R Crew Katherine D KD

JCO clinical cancer informatics 20190301

<h4>Purpose</h4>A barrier to chemoprevention uptake among high-risk women is the lack of routine breast cancer risk assessment in the primary care setting. We calculated breast cancer risk using the Breast Cancer Surveillance Consortium (BCSC) model, accounting for age, race/ethnicity, first-degree family history of breast cancer, benign breast disease, and mammographic density, using data collected from the electronic health records (EHRs) and self-reports.<h4>Patients and methods</h4>Among wom ...[more]

PMID: 30869999

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Project description:BackgroundSuicide is an important public health concern in the United States and around the world. There has been significant work examining machine learning approaches to identify and predict intentional self-harm and suicide using existing data sets. With recent advances in computing, deep learning applications in health care are gaining momentum.ObjectiveThis study aimed to leverage the information in clinical notes using deep neural networks (DNNs) to (1) improve the identification of patients treated for intentional self-harm and (2) predict future self-harm events.MethodsWe extracted clinical text notes from electronic health records (EHRs) of 835 patients with International Classification of Diseases (ICD) codes for intentional self-harm and 1670 matched controls who never had any intentional self-harm ICD codes. The data were divided into training and holdout test sets. We tested a number of algorithms on clinical notes associated with the intentional self-harm codes using the training set, including several traditional bag-of-words-based models and 2 DNN models: a convolutional neural network (CNN) and a long short-term memory model. We also evaluated the predictive performance of the DNNs on a subset of patients who had clinical notes 1 to 6 months before the first intentional self-harm event. Finally, we evaluated the impact of a pretrained model using Word2vec (W2V) on performance.ResultsThe area under the receiver operating characteristic curve (AUC) for the CNN on the phenotyping task, that is, the detection of intentional self-harm in clinical notes concurrent with the events was 0.999, with an F1 score of 0.985. In the predictive task, the CNN achieved the highest performance with an AUC of 0.882 and an F1 score of 0.769. Although pretraining with W2V shortened the DNN training time, it did not improve performance.ConclusionsThe strong performance on the first task, namely, phenotyping based on clinical notes, suggests that such models could be used effectively for surveillance of intentional self-harm in clinical text in an EHR. The modest performance on the predictive task notwithstanding, the results using DNN models on clinical text alone are competitive with other reports in the literature using risk factors from structured EHR data.

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Dataset Information

Identifying Women at High Risk for Breast Cancer Using Data From the Electronic Health Record Compared With Self-Report.

Publications

Identifying Women at High Risk for Breast Cancer Using Data From the Electronic Health Record Compared With Self-Report.

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