A Kernel to Exploit Informative Missingness in Multivariate Time Series from EHRs

A large fraction of the electronic health records (EHRs) consists of clinical measurements collected over time, such as lab tests and vital signs, which provide important information about a patient's health status. These sequences of clinical measurements are naturally represented as time series, characterized by multiple variables and large amounts of missing data, which complicate the analysis. In this work, we propose a novel kernel which is capable of exploiting both the information from the observed values as well the information hidden in the missing patterns in multivariate time series (MTS) originating e.g. from EHRs. The kernel, called TCK$_{IM}$, is designed using an ensemble learning strategy in which the base models are novel mixed mode Bayesian mixture models which can effectively exploit informative missingness without having to resort to imputation methods. Moreover, the ensemble approach ensures robustness to hyperparameters and therefore TCK$_{IM}$ is particularly well suited if there is a lack of labels - a known challenge in medical applications. Experiments on three real-world clinical datasets demonstrate the effectiveness of the proposed kernel.Comment: 2020 International Workshop on Health Intelligence, AAAI-20. arXiv admin note: text overlap with arXiv:1907.0525

Chemotherapy effectiveness in trial-underrepresented groups with early breast cancer:A retrospective cohort study

BACKGROUND: Adjuvant chemotherapy in early stage breast cancer has been shown to reduce mortality in a large meta-analysis of over 100 randomised trials. However, these trials largely excluded patients aged 70 years and over or with higher levels of comorbidity. There is therefore uncertainty about whether the effectiveness of adjuvant chemotherapy generalises to these groups, hindering patient and clinician decision-making. This study utilises administrative healthcare data-real world data (RWD)-and econometric methods for causal analysis to estimate treatment effectiveness in these trial-underrepresented groups. METHODS AND FINDINGS: Women with early breast cancer aged 70 years and over and those under 70 years with a high level of comorbidity were identified and their records extracted from Scottish Cancer Registry (2001-2015) data linked to other routine health records. A high level of comorbidity was defined as scoring 1 or more on the Charlson comorbidity index, being in the top decile of inpatient stays, and/or having 5 or more visits to specific outpatient clinics, all within the 5 years preceding breast cancer diagnosis. Propensity score matching (PSM) and instrumental variable (IV) analysis, previously identified as feasible and valid in this setting, were used in conjunction with Cox regression to estimate hazard ratios for death from breast cancer and death from all causes. The analysis adjusts for age, clinical prognostic factors, and socioeconomic deprivation; the IV method may also adjust for unmeasured confounding factors. Cohorts of 9,653 and 7,965 were identified for women aged 70 years and over and those with high comorbidity, respectively. In the ≥70/high comorbidity cohorts, median follow-up was 5.17/6.53 years and there were 1,935/740 deaths from breast cancer. For women aged 70 years and over, the PSM-estimated HR was 0.73 (95% CI 0.64-0.95), while for women with high comorbidity it was 0.67 (95% CI 0.51-0.86). This translates to a mean predicted benefit in terms of overall survival at 10 years of approximately3% (percentage points) and 4%, respectively. A limitation of this analysis is that use of observational data means uncertainty remains both from sampling uncertainty and from potential bias from residual confounding. CONCLUSIONS: The results of this study, as RWD, should be interpreted with caution and in the context of existing and emerging randomised data. The relative effectiveness of adjuvant chemotherapy in reducing mortality in patients with early stage breast cancer appears to be generalisable to the selected trial-underrepresented groups.</p

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154448/1/sim8445_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154448/2/sim8445.pd

Track D Social Science, Human Rights and Political Science

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138414/1/jia218442.pd

Using Ancillary Sociodemographic Data to Identify Sexual Minority Adults among Those Responding “Something Else” or “Don’t Know” to Sexual Orientation Questions

Background: General population surveys are increasingly offering broader response options for questions on sexual orientation—e.g., not only gay or lesbian, but also “something else” (SE) and “don’t know” (DK). However, these additional response options are potentially confusing for those who may not know what the terms mean. Researchers studying sexual orientation-based disparities face difficult methodological trade-offs regarding how best to classify respondents identifying with the SE and DK categories. Objectives: Develop respondent-level probabilities of sexual minority orientation without excluding or misclassifying the potentially ambiguous SE and DK responses. Compare three increasingly-inclusive analytic approaches for estimating health disparities using a single item: (a) omitting SE and DK respondents; (b) classifying SE as sexual minority and omitting DK; and (c) a new approach classifying only SE and DK respondents with >50% predicted probabilities of being sexual minorities as sexual minority. Methods: We used the sociodemographic information and follow-up questions for SE and DK respondents in the 2013-2014 NHIS to generate predicted probabilities of identifying as a sexual minority adult. Results: 94% of the 144 SE respondents and 20% of the 310 DK respondents were predicted to identify as a sexual minority adult, with higher probabilities for younger, wealthier, non-Hispanic white, and urban-dwelling respondents. Using a more specific definition of sexual minority orientation improved the precision of health and healthcare disparity estimates. Conclusions: Predicted probabilities of sexual minority orientation may be used in this and other surveys potentially to improve representation and categorization of those who identify as a sexual minority adult

Care integration within and outside health system boundaries

Objective: Examine care integration—efforts to unify disparate parts of health care organizations to generate synergy across activities occurring within and between them—to understand whether and at which organizational level health systems impact care quality and staff experience. Data Sources: Surveys administered to one practice manager (56/59) and up to 26 staff (828/1360) in 59 practice sites within 24 physician organizations within 17 health systems in four states (2017-2019). Study Design: We developed manager and staff surveys to collect data on organizational, social, and clinical process integration, at four organizational levels: practice site, physician organization, health system, and outside health systems. We analyzed data using descriptive statistics and regression. Principal Findings: Managers and staff perceived opportunity for improvement across most types of care integration and organizational levels. Managers/staff perceived little variation in care integration across health systems. They perceived better care integration within practice sites than within physician organizations, health systems, and outside health systems—up to 38 percentage points (pp) lower (P <.001) outside health systems compared to within practice sites. Of nine clinical process integration measures, one standard deviation (SD) (7.2-pp) increase in use of evidence-based care related to 6.4-pp and 8.9-pp increases in perceived quality of care by practice sites and health systems, respectively, and a 4.5-pp increase in staff job satisfaction; one SD (9.7-pp) increase in integration of social services and community resources related to a 7.0-pp increase in perceived quality of care by health systems; one SD (6.9-pp) increase in patient engagement related to a 6.4-pp increase in job satisfaction and a 4.6-pp decrease in burnout; and one SD (10.6-pp) increase in integration of diabetic eye examinations related to a 5.5-pp increase in job satisfaction (all P <.05). Conclusions: Measures of clinical process integration related to higher staff ratings of quality and experience. Action is needed to improve care integration within and outside health systems