Nymannlott5617

Given the ubiquitous nature of information systems in modern health care, interest in the pursuit of formal training in clinical informatics is increasing. This interest is not restricted to generalists-informatics training is increasingly being sought by future subspecialists. The traditional structure of Accreditation Council on Graduate Medical Education subspecialty training requires completion of both clinical and clinical informatics fellowship programs, and understandably lacks appeal due to the time commitment required. One approach to encourage clinical informatics training is to integrate it with clinical fellowships in order to confer dual-board eligibility. In this perspective, we describe 3 successful petitions for combined training in clinical informatics in order to support other programs and the American Board of Preventive Medicine in establishing pathways for training subspecialists in clinical informatics. © The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email journals.permissions@oup.com.OBJECTIVES To independently assess quality of care among patients who died in hospital and whose next-of-kin submitted a letter of complaint and make comparisons with matched controls. To identify whether use of a treatment escalation limitation plan (TELP) during the terminal illness was a relevant background factor. DESIGN The study was an investigator-blinded retrospective case-note review of 42 complaints cases and 72 controls matched for age, sex, ward location and time of death. SETTING The acute medical and surgical wards of three District General Hospitals administered by NHS Lanarkshire, Scotland. PARTICIPANTS None. INTERVENTION None. OUTCOME MEASURES Quality of care clinical 'problems', non-beneficial interventions (NBIs) and harms were evaluated using the Structured Judgment Review Method. Complaints were categorized using the Healthcare Complaints Analysis Tool. RESULTS The event frequencies and rate ratios for clinical 'problems', NBIs and harms were consistently higher in complaint cases compared to controls. The difference was only significant for NBIs (P = 0.05). TELPs were used less frequently in complaint cases compared to controls (23.8 versus 47.2%, P = 0.013). The relationship between TELP use and the three key clinical outcomes was nonsignificant. CONCLUSIONS Care delivered to patients at end-of-life whose next-of-kin submitted a complaint was poorer overall than among control patients when assessed independently by blinded reviewers. Regular use of a TELP in acute clinical settings has the potential to influence complaints relating to end-of-life care, but this requires further prospective study. © The Author(s) 2020. Published by Oxford University Press in association with the International Society for Quality in Health Care. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.Predicting the interactions between drugs and targets plays an important role in the process of new drug discovery, drug repurposing (also known as drug repositioning). There is a need to develop novel and efficient prediction approaches in order to avoid the costly and laborious process of determining drug-target interactions (DTIs) based on experiments alone. These computational prediction approaches should be capable of identifying the potential DTIs in a timely manner. Matrix factorization methods have been proven to be the most reliable group of methods. Here, we first propose a matrix factorization-based method termed 'Coupled Matrix-Matrix Completion' (CMMC). Next, in order to utilize more comprehensive information provided in different databases and incorporate multiple types of scores for drug-drug similarities and target-target relationship, we then extend CMMC to 'Coupled Tensor-Matrix Completion' (CTMC) by considering drug-drug and target-target similarity/interaction tensors. Results Evaluation on two benchmark datasets, DrugBank and TTD, shows that CTMC outperforms the matrix-factorization-based methods GRMF, $L_2,1$-GRMF, NRLMF and NRLMF$\beta $. Based on the evaluation, CMMC and CTMC outperform the above three methods in term of area under the curve, F1 score, sensitivity and specificity in a considerably shorter run time. © The Author(s) 2020. Published by Oxford University Press.Accumulating evidence has shown that microRNAs (miRNAs) play crucial roles in different biological processes, and their mutations and dysregulations have been proved to contribute to tumorigenesis. https://www.selleckchem.com/ In silico identification of disease-associated miRNAs is a cost-effective strategy to discover those most promising biomarkers for disease diagnosis and treatment. The increasing available omics data sources provide unprecedented opportunities to decipher the underlying relationships between miRNAs and diseases by computational models. However, most existing methods are biased towards a single representation of miRNAs or diseases and are also not capable of discovering unobserved associations for new miRNAs or diseases without association information. In this study, we present a novel computational method with adaptive multi-source multi-view latent feature learning (M2LFL) to infer potential disease-associated miRNAs. First, we adopt multiple data sources to obtain similarity profiles and capture different latent features according to the geometric characteristic of miRNA and disease spaces. Then, the multi-modal latent features are projected to a common subspace to discover unobserved miRNA-disease associations in both miRNA and disease views, and an adaptive joint graph regularization term is developed to preserve the intrinsic manifold structures of multiple similarity profiles. Meanwhile, the Lp,q-norms are imposed into the projection matrices to ensure the sparsity and improve interpretability. The experimental results confirm the superior performance of our proposed method in screening reliable candidate disease miRNAs, which suggests that M2LFL could be an efficient tool to discover diagnostic biomarkers for guiding laborious clinical trials. © The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.