Person:

Beam, Andrew

Background: Discovering causal genetic variants from large genetic association studies poses many difficult challenges. Assessing which genetic markers are involved in determining trait status is a computationally demanding task, especially in the presence of gene-gene interactions. Results: A non-parametric Bayesian approach in the form of a Bayesian neural network is proposed for use in analyzing genetic association studies. Demonstrations on synthetic and real data reveal they are able to efficiently and accurately determine which variants are involved in determining case-control status. By using graphics processing units (GPUs) the time needed to build these models is decreased by several orders of magnitude. In comparison with commonly used approaches for detecting interactions, Bayesian neural networks perform very well across a broad spectrum of possible genetic relationships. Conclusions: The proposed framework is shown to be a powerful method for detecting causal SNPs while being computationally efficient enough to handle large datasets. Electronic supplementary material The online version of this article (doi:10.1186/s12859-014-0368-0) contains supplementary material, which is available to authorized users.

Insomnia remains under-diagnosed and poorly treated despite its high economic and social costs. Though previous work has examined how patient characteristics affect sleep medication prescriptions, the role of physician characteristics that influence this clinical decision remains unclear. We sought to understand patient and physician factors that influence sleep medication prescribing patterns by analyzing Electronic Medical Records (EMRs) including the narrative clinical notes as well as codified data. Zolpidem and trazodone were the most widely prescribed initial sleep medication in a cohort of 1,105 patients. Some providers showed a historical preference for one medication, which was highly predictive of their future prescribing behavior. Using a predictive model (AUC = 0.77), physician preference largely determined which medication a patient received (OR = 3.13; p = 3 × 10−37). In addition to the dominant effect of empirically determined physician preference, discussion of depression in a patient’s note was found to have a statistically significant association with receiving a prescription for trazodone (OR = 1.38, p = 0.04). EMR data can yield insights into physician prescribing behavior based on real-world physician-patient interactions.

Infants with autism spectrum disorder (ASD) were recently found to have prolonged auditory brainstem response (ABR); however, at older ages, findings are contradictory. We compared ABR differences between participants with ASD and controls with respect to age using a meta‐analysis. Data sources included MEDLINE, EMBASE, Web of Science, Google Scholar, HOLLIS, and ScienceDirect from their inception to June 2016. The 25 studies that were included had a total of 1349 participants (727 participants with ASD and 622 controls) and an age range of 0–40 years. Prolongation of the absolute latency of wave V in ASD had a significant negative correlation with age (R2 = 0.23; P = 0.01). The 22 studies below age 18 years showed a significantly prolonged wave V in ASD (Standard Mean Difference = 0.6 [95% CI, 0.5–0.8]; P < 0.001). The 3 studies above 18 years of age showed a significantly shorter wave V in ASD (SMD = −0.6 [95% CI, −1.0 to −0.2]; P = 0.004). Prolonged ABR was consistent in infants and children with ASD, suggesting it can serve as an ASD biomarker at infancy. As the ABR is routinely used to screen infants for hearing impairment, the opportunity for replication studies is extensive. Autism Res 2018, 11: 355–363. © 2017 The Authors Autism Research published by International Society for Autism Research and Wiley Periodicals, Inc. Lay Summary Our analysis of previous studies showed that infants and children with autism spectrum disorder (ASD) have a slower brain response to sound, while adults have a faster brain response to sound. This suggests that slower brain response in infants may predict ASD risk. Brain response to sound is routinely tested on newborns to screen hearing impairment, which has created large data sets to afford replication of these results.

We developed an insomnia classification algorithm by interrogating an electronic medical records (EMR) database of 314,292 patients. The patients received care at Massachusetts General Hospital (MGH), Brigham and Women’s Hospital (BWH), or both, between 1992 and 2010. Our algorithm combined structured variables (such as International Classification of Diseases 9th Revision [ICD-9] codes, prescriptions, laboratory observations) and unstructured variables (such as text mentions of sleep and psychiatric disorders in clinical narrative notes). The highest classification performance of our algorithm was achieved when it included a combination of structured variables (billing codes for insomnia, common psychiatric conditions, and joint disorders) and unstructured variables (sleep disorders and psychiatric disorders). Our algorithm had superior performance in identifying insomnia patients compared to billing codes alone (area under the receiver operating characteristic curve [AUROC] = 0.83 vs. 0.55 with 95% confidence intervals [CI] of 0.76–0.90 and 0.51–0.58, respectively). When applied to the 314,292-patient population, our algorithm classified 36,810 of the patients with insomnia, of which less than 17% had a billing code for insomnia. In conclusion, an insomnia classification algorithm that incorporates clinical notes is superior to one based solely on billing codes. Compared to traditional methods, our study demonstrates that a classification algorithm that incorporates physician notes can more accurately, comprehensively, and quickly identify large cohorts of insomnia patients.