ADH-Enhancer: an attention-based deep hybrid framework for enhancer identification and strength prediction.

Enhancers play an important role in the process of gene expression regulation. In DNA sequence abundance or absence of enhancers and irregularities in the strength of enhancers affects gene expression process that leads to the initiation and propagation of diverse types of genetic diseases such as hemophilia, bladder cancer, diabetes and congenital disorders. Enhancer identification and strength prediction through experimental approaches is expensive, time-consuming and error-prone. To accelerate and expedite the research related to enhancers identification and strength prediction, around 19 computational frameworks have been proposed. These frameworks used machine and deep learning methods that take raw DNA sequences and predict enhancer's presence and strength. However, these frameworks still lack in performance and are not useful in real time analysis. This paper presents a novel deep learning framework that uses language modeling strategies for transforming DNA sequences into statistical feature space. It applies transfer learning by training a language model in an unsupervised fashion by predicting a group of nucleotides also known as k-mers based on the context of existing k-mers in a sequence. At the classification stage, it presents a novel classifier that reaps the benefits of two different architectures: convolutional neural network and attention mechanism. The proposed framework is evaluated over the enhancer identification benchmark dataset where it outperforms the existing best-performing framework by 5%, and 9% in terms of accuracy and MCC. Similarly, when evaluated over the enhancer strength prediction benchmark dataset, it outperforms the existing best-performing framework by 4%, and 7% in terms of accuracy and MCC.

GHS-NET a generic hybridized shallow neural network for multi-label biomedical text classification.

Exponential growth of biomedical literature and clinical data demands more robust yet precise computational methodologies to extract useful insights from biomedical literature and to perform accurate assignment of disease-specific codes. Such approaches can largely enhance the effectiveness of diverse biomedicine and bioinformatics applications. State-of-the-art computational biomedical text classification methodologies either solely leverage discrimintaive features extracted through convolution operations performed by deep convolutional neural network or contextual information extracted by recurrent neural network. However, none of the methodology takes advantage of both convolutional and recurrent neural networks. Further, existing methodologies lack to produce decent performance for the classification of different genre biomedical text such as biomedical literature or clinical notes. We, for the very first time, present a generic deep learning based hybrid multi-label classification methodology namely GHS-NET which can be utilized to accurately classify biomedical text of diverse genre. GHS-NET makes use of convolutional neural network to extract most discriminative features and bi-directional Long Short-Term Memory to acquire contextual information. GHS-NET effectiveness is evaluated for extreme multi-label biomedical literature classification and assignment of ICD-9 codes to clinical notes. For the task of extreme multi-label biomedical literature classification, performance comparison of GHS-Net and state-of-the-art deep learning based methodology reveals that GHS-Net marks the increment of 1%, 6%, and 1% for hallmarks of cancer dataset, 10%, 16%, and 11% for chemical exposure dataset in terms of precision, recall, and F1-score. For the task of clinical notes classification, GHS-Net outperforms previous best deep learning based methodology over Medical Information Mart for Intensive Care dataset (MIMIC-III) by the significant margin of 6%, 8% in terms of recall and F1-score. GHS-NET is available as a web service at1 and potentially can be used to accurately classify multi-variate disease and chemical exposure specific text.

Improving patient safety culture in Saudi Arabia (2012-2015): trending, improvement and benchmarking.

BACKGROUND: Measuring patient safety culture can provide insight into areas for improvement and help monitor changes over time. This study details the findings of a re-assessment of patient safety culture in a multi-site Medical City in Riyadh, Kingdom of Saudi Arabia (KSA). Results were compared to an earlier assessment conducted in 2012 and benchmarked with regional and international studies. Such assessments can provide hospital leadership with insight on how their hospital is performing on patient safety culture composites as a result of quality improvement plans. This paper also explored the association between patient safety culture predictors and patient safety grade, perception of patient safety, frequency of events reported and number of events reported. METHODS: We utilized a customized version of the patient safety culture survey developed by the Agency for Healthcare Research and Quality. The Medical City is a tertiary care teaching facility composed of two sites (total capacity of 904 beds). Data was analyzed using SPSS 24 at a significance level of 0.05. A t-Test was used to compare results from the 2012 survey to that conducted in 2015. Two adopted Generalized Estimating Equations in addition to two linear models were used to assess the association between composites and patient safety culture outcomes. Results were also benchmarked against similar initiatives in Lebanon, Palestine and USA. RESULTS: Areas of strength in 2015 included Teamwork within units, and Organizational Learning-Continuous Improvement; areas requiring improvement included Non-Punitive Response to Error, and Staffing. Comparing results to the 2012 survey revealed improvement on some areas but non-punitive response to error and Staffing remained the lowest scoring composites in 2015. Regression highlighted significant association between managerial support, organizational learning and feedback and improved survey outcomes. Comparison to international benchmarks revealed that the hospital is performing at or better than benchmark on several composites. CONCLUSION: The Medical City has made significant progress on several of the patient safety culture composites despite still having areas requiring additional improvement. Patient safety culture outcomes are evidently linked to better performance on specific composites. While results are comparable with regional and international benchmarks, findings confirm that regular assessment can allow hospitals to better understand and visualize changes in their performance and identify additional areas for improvement.