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This study investigates the use of deep learning methods to improve the accuracy of a predictive model for dementia, and compares the performance to a traditional machine learning model. With sufficient accuracy the model can be deployed as a first round screening tool for clinical follow-up including neurological examination, neuropsychological testing, imaging and recruitment to clinical trials. Seven cohorts with two years of data, three to eight years prior to index date, and an incident cohort were created. Four trained models for each cohort, boosted trees, feed forward network, recurrent neural network and recurrent neural network with pre-trained weights, were constructed and their performance compared using validation and test data. The incident model had an AUC of 94.4% and F1 score of 54.1%. Eight years removed from index date the AUC and F1 scores were 80.7% and 25.6%, respectively. The results for the remaining cohorts were between these ranges. Deep learning models can result in significant improvement in performance but come at a cost in terms of run times and hardware requirements. The results of the model at index date indicate that this modeling can be effective at stratifying patients at risk of dementia. At this time, the inability to sustain this quality at longer lead times is more an issue of data availability and quality rather than one of algorithm choices., Competing Interests: The authors [VSN, CAH, YS, WHC, PAB] are employees of Optum. The study also received support from Global CEO Initiative, Biogen, Janssen Pharmaceutical, and Merck. Computers were donated to the study by Dell. However, this does not alter our adherence to PLOS ONE policies on sharing data and materials.

Propensity score (PS) methods have proliferated in recent years in observational studies in general and in observational comparative effectiveness research (CER) in particular. PS methods are an important set of tools for estimating treatment effects in observational studies, enabling adjustment for measured confounders in an easy-to-understand and transparent way. This article demonstrates how PS methods have been used to address specific CER questions from 2001 through to 2012 by identifying six impactful studies from this period. This article also discusses areas for improvement, including data infrastructure, and a unified set of guidelines in terms of PS implementation and reporting, which will boost confidence in evidence generated through observational CER using PS methods.

Objective: To extend recent conceptual and methodological advances in disparities research to include the incorporation of genomic information in analyses of racial/ethnic disparities in health care and health outcomes., Data Sources: Published literature on human genetic variation, the role of genetics in disease and response to treatment, and methodological developments in disparities research., Study Design: We present a conceptual framework for incorporating genomic information into the Institute of Medicine definition of racial/ethnic disparities in health care, identify key concepts used in disparities research that can be informed by genomics research, and illustrate the incorporation of genomic information into current methods using the example of HER-2 mutations guiding care for breast cancer., Principal Findings: Genomic information has not yet been incorporated into disparities research, though it has direct relevance to concepts of race/ethnicity, health status, appropriate care, and socioeconomic status. The HER-2 example demonstrates how available genetic information can be incorporated into current disparities methods to reduce selection bias and measurement error. Advances in health information infrastructure may soon make standardized genetic information more available to health services researchers., Conclusion: Genomic information can refine measurement of racial/ethnic disparities in health care and health outcomes and should be included wherever possible in disparities research., (© Health Research and Educational Trust.)