Background We assessed attitudes of breast cancer patients regarding molecular testing for personalized therapy and research. Methods A questionnaire was given to female breast cancer patients presenting to a cancer center. Associations between demographic, clinical variables and attitudes towards molecular testing were evaluated. Results 308 patients were approached and 100 completed the questionnaire (32% response rate). Most participants were willing to undergo molecular testing to assist in selection of approved drugs (81%) and experimental therapy (59%) if testing was covered by insurance. Most participants were white (71%). Even if testing was financially covered, non-white participants were less willing to undergo molecular testing for selection of approved drugs (nonwhites vs. whites, 54% vs. 90%, OR=0.13; p=0.0004) or experimental drugs (35% vs. 68%, OR=0.26; p=0.0072). Most participants (75%) were willing to undergo a biopsy to guide therapy, and 46% were willing to undergo research biopsies. Non-white participants were less willing to undergo research biopsies (17% vs. 55%, OR=0.17; p=0.0033). Most participants wanted to be informed when research results had implications for treatment (91%), new cancer risk (90%), and other preventable/treatable diseases (87%). Conclusions Most patients are willing to undergo molecular testing and minimally invasive procedures to guide approved or experimental therapy. There are significant differences in attitudes towards molecular testing between racial groups; non-whites are less willing to undergo testing even if the results would guide their own therapy. Novel approaches are needed to prevent disparities in delivery of genomically informed care and to increase minority participation in biomarker-driven trials.

Objective Recent studies on electronic health records (EHRs) started to learn deep generative models and synthesize a huge amount of realistic records, in order to address significant privacy issues surrounding the EHR. However, most of them only focus on structured records about patients’ independent visits, rather than on chronological clinical records. In this article, we aim to learn and synthesize realistic sequences of EHRs based on the generative autoencoder. Materials and Methods We propose a dual adversarial autoencoder (DAAE), which learns set-valued sequences of medical entities, by combining a recurrent autoencoder with 2 generative adversarial networks (GANs). DAAE improves the mode coverage and quality of generated sequences by adversarially learning both the continuous latent distribution and the discrete data distribution. Using the MIMIC-III (Medical Information Mart for Intensive Care-III) and UT Physicians clinical databases, we evaluated the performances of DAAE in terms of predictive modeling, plausibility, and privacy preservation. Results Our generated sequences of EHRs showed the comparable performances to real data for a predictive modeling task, and achieved the best score in plausibility evaluation conducted by medical experts among all baseline models. In addition, differentially private optimization of our model enables to generate synthetic sequences without increasing the privacy leakage of patients’ data. Conclusions DAAE can effectively synthesize sequential EHRs by addressing its main challenges: the synthetic records should be realistic enough not to be distinguished from the real records, and they should cover all the training patients to reproduce the performance of specific downstream tasks.

Our manual review shows that the ingestion pipeline could achieve an accuracy of 90% and core elements of DATS had varied frequency across repositories. On a manually curated benchmark dataset, the DataMed search engine achieved an inferred average precision of 0.2033 and a precision at 10 (P@10, the number of relevant results in the top 10 search results) of 0.6022, by implementing advanced natural language processing and terminology services. Currently, we have made the DataMed system publically available as an open source package for the biomedical community.

Background Understanding patients’ knowledge and prior information-seeking regarding personalized cancer therapy (PCT) may inform future patient information systems, consent for molecular testing and PCT protocols. We evaluated breast cancer patients’ knowledge and information-seeking behaviors regarding PCT. Methods Newly registered female breast cancer patients (n=100) at a comprehensive cancer center completed a self-administered questionnaire prior to their first clinic visit. Results Knowledge regarding cancer genetics and PCT was moderate (mean 8.7 +/− 3.8 questions correct out of 16). A minority of patients (27%) indicated that they had sought information regarding PCT. Higher education (p=0.009) and income levels (p=0.04) were associated with higher knowledge scores and with seeking PCT information (p=0.04). Knowledge was not associated with willingness to participate in PCT research. Conclusion Educational background and financial status impact patient knowledge as well as information-seeking behavior. For most patients, clinicians are likely to be patients’ initial source of information about PCT. Understanding patients’ knowledge deficits at presentation may help inform patient education efforts.

Patients generally expressed low levels of concern regarding privacy of genomic data, and many expressed willingness to consent to sharing their genomic data with researchers.