Genetic targets for autism spectrum disorder identified by MU team
February 22, 2018
COLUMBIA, Mo. – Autism is a spectrum of closely related symptoms involving behavioral, social and cognitive deficits. Early detection of autism in children is key to producing the best outcomes; however, searching for the genetic causes of autism is complicated by various symptoms found within the spectrum. Now, a multi-disciplinary team of researchers at the […]
An RNAmazing research breakthrough
December 19, 2017
Professor of Bioengineering and the Dalton Cardiovascular Research Center Li-Qun (Andrew) Gu and Shi-Jie Chen, joint Professor of Physics, Biochemistry and the MU Informatics Institute and their team recently published “Nanopore electric snapshots of an RNA tertiary folding pathway,” in the prestigious journal Nature Communications.
Dr. Gregory Alexander recently awarded AHRQ grant.
October 9, 2017
Dr. Gregory Alexander, from The Sinclair School of Nursing, was recently awarded a $1,995,522.00 AHRQ grant. This grant will support an interdisciplinary research team who are already contributing to clinical research in long-term care settings. The PI is a doctorally-prepared RN and fellow in the American Academy of Nursing with over two decades of work […]
Leadership Auditorium, 2501 Student Center
Real-time prediction of unplanned 30-day hospital readmissions
Hospital readmissions are frequent and costly. It has been estimated that unplanned readmissions account for $17.4 billion in Medicare expenditures annually. Since the fiscal year 2013, the Hospital Readmissions Reduction Program (HRRP) has been established to financially penalize hospitals with excessive readmissions after initial admissions for particular conditions and procedures. In recent years, numerous hospital readmission predictive models have been reported and most of them rely on attributes that are only available near or post-discharge of the current encounter, such as the length of stay, discharge disposition, diagnosis codes. By incorporating these attributes, it is impossible to perform real-time readmission prediction during an inpatient encounter. However, early prediction of readmission can help deliver timely interventions to reduce the readmission risk. In this work, a machine learning predictive model has been built based on the Health Facts data. Because this model focuses more on patients’ medical history and requires less information from the current encounter, it allows real-time readmission prediction. I will discuss more details during the seminar.
Leadership Auditorium, 2501 Student Center
Medical Calculators: Prevalence, and Barriers to Use
Medical calculators synthesize measurable evidence and help introduce new medical guidelines and standards. Some medical calculators can fulfill the role of CDS for Meaningful Use purposes. However, there are barriers for clinicians to use medical calculators in practice. Objectives of this study were to determine whether lack of EHR integration would be a barrier to use of medical calculators and understand factors that may limit use and perceived usefulness of calculators
A survey about medical calculators as they relate to clinical efficiency, perceived usefulness, and barriers to effective use was conducted at a medium-sized academic medical center. 819 physicians were invited to participate in an online survey with a 13% response rate. Results were statistically analyzed to highlight factors related to use or non-use of medical calculators.
We found a negative correlation between use of medical calculators and years of experience (p<0.001), with decreasing calculator use as experience goes up. Barriers to using medical calculators by non-users and users of medical calculators show that necessity and integration are significantly different with p<0.001 and p=0.037, respectively. 46.7% of non-users reported necessity as a barrier compared to 7.7% of users. Integration was reported as a barrier for 43.6% of users, but only 13.3% of non-users. 61% of users indicated that calculators made them more efficient, and 70% reported that unavailability of normally used calculators make them less efficient. 60% of users indicated that they are somewhat or very likely to use newly published medical calculators.
The results highlight that medical calculators are important for care delivery by both users and non-users. For non-users, they are seen as having a potentially positive impact on patient care, but unnecessary as part of clinical practice. For medical calculator users, calculators are an important part of regular workflow for efficiency improvement. Clinicians with fewer years of experience show an eagerness to consume newly published calculators, making these kinds of CDS a potentially useful way to disseminate new medical evidence. The survey results suggest that when medical calculators can be automated and integrated into the EHR as part of everyday workflow then efficiency and adoption may increase.
222 Naka Hall
Application of Deep Learning in Predicting Phenotypes
Genomic selection (GS) can use single-nucleotide polymorphism (SNPs) markers to predict breeding values (BV) for enhancing quantitative traits in breeding populations. GS has been proved to increase breeding efficiency in both plant and animal breeding. However, existing statistical and machine-learning methods require imputation to missing values in genotypes, which leads to poor generalization and computation inefficiency. Here, we propose a deep-learning model using convolutional neural networks (CNN) to predict the Genomic Estimated Breeding Value (GEBV) and also to investigate contributions of genomic SNPs to GEBV using a saliency map approach.Comparing with traditional statistical models including rr-BLUP, Bayesian ridge regression, Bayesian LASSO and Bayes A on a Glycine Max (soybean) Nested Association Mapping (NAM) dataset and a simulation dataset, our model can better handle the missing values in genotypes and is more efficient in accurately predicting breeding values. Our model also has a great potential in interpreting phenotype-genotype associations over the entire genome. The model is available at https://github.com/kateyliu/DL_gwas.