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The existing discrete-time neural dynamics methods for solving the multilinear tensor equation (MTE) with $\mathcal {M}$-tensor are all derived from the continuous-time one and depend on the Euler difference formula, which cannot be applied to essentially discrete problems and have low solution accuracy. Moreover, these methods all focus on static problems rather than time-varying ones, and thus may have unsatisfactory performance in applications with time-varying parameters. Additionally, most of these methods fail to handle the MTE with $\mathcal {M}$-tensor under noisy conditions. To remedy these issues, a high-order robust discrete-time neural dynamics (HRDND) method with a directly discrete approach is proposed for solving the time-varying MTE (TMTE) with $\mathcal {M}$-tensor in this article. Theoretical analyses on convergence and robustness are provided to prove that the proposed HRDND method is feasible and effective. Finally, simulative experiments on four time-varying numerical examples and an application derived from the Bellman equation solved by the proposed HRDND method and other four methods are given, whose results illustrate the superiority of the proposed HRDND method.

Periapical dental diseases (PDD) are one of the most prevalent dental diseases leading to tooth loss and poor quality of life. Differential diagnosis of periapical diseases is critical because the treatment plan depends on the diagnosis. The current diagnosis approach of PDD uses an eyeballing method mainly depending on clinicians' expertise, any lack of which may lead to an inaccurate diagnosis. We developed an advanced image processing tool that can help clinicians for more accurately differential PDD diagnoses that leads to the correct treatment approach. Sixty periapical radiographs were processed by the tool and the differential diagnostic output was compared with manually annotated gold standard dataset. Our tool performed well with a 95% sensitivity, 89% specificity, and 93% accuracy in providing a differential diagnosis. Demonstrating such promising results, further studies should test the accuracy of this tool on a larger dataset get more definitive results for widespread deployment and use.

The COVID-19 pandemic has heightened the necessity for pervasive data and system interoperability to manage healthcare information and knowledge. There is an urgent need to better understand the role of interoperability in improving the societal responses to the pandemic. This paper explores data and system interoperability, a very specific area that could contribute to fighting COVID-19. Specifically, the authors propose a unified health information system framework to connect data, systems, and devices to increase interoperability and manage healthcare information and knowledge. A blockchain-based solution is also provided as a recommendation for improving the data and system interoperability in healthcare.

Patient-centered appointment access is of critical importance at community health centers (CHCs) and its optimal implementation entails the use of advanced data analytics. This study seeks to optimize patient-centered appointment scheduling through data mining of Electronic Health Record/Practice Management (EHR/PM) systems. Data was collected from different EHR/PM systems in use at three CHCs across the state of Indiana and integrated into a multidimensional data warehouse. Data mining was performed using decision tree modeling, logistic regression, and visual analytics combined with n-gram modeling to derive critical influential factors that guide implementation of patient-centered open-access scheduling. The analysis showed that appointment adherence was significantly correlated with the time dimension of scheduling, with lead time for an appointment being the most significant predictor. Other variables in the time dimension such as time of the day and season were important predictors as were variables tied to patient demographic and clinical characteristics. Operationalizing the findings for selection of open-access hours led to a 16% drop in missed appointment rates at the interventional health center. The study uncovered the variability in factors affecting patient appointment adherence and associated open-access interventions in different health care settings. It also shed light on the reasons for same-day appointment through n-gram-based text mining. Optimizing open-access scheduling methods require ongoing monitoring and mining of large-scale appointment data to uncover significant appointment variables that impact schedule utilization. The study also highlights the need for greater “in-CHC” data analytic capabilities to re-design care delivery processes for improving access and efficiency.

Ribonucleic acid &#x0028 RNA &#x0029 hybridization is widely used in popular RNA simulation software in bioinformatics. However, limited by the exponential computational complexity of combinatorial problems, it is challenging to decide, within an acceptable time, whether a specific RNA hybridization is effective. We hereby introduce a machine learning based technique to address this problem. Sample machine learning &#x0028 ML &#x0029 models tested in the training phase include algorithms based on the boosted tree &#x0028 BT &#x0029 , random forest &#x0028 RF &#x0029 , decision tree &#x0028 DT &#x0029 and logistic regression &#x0028 LR &#x0029, and the corresponding models are obtained. Given the RNA molecular coding training and testing sets, the trained machine learning models are applied to predict the classification of RNA hybridization results. The experiment results show that the optimal predictive accuracies are 96.2&#x0025, 96.6&#x0025, 96.0&#x0025 and 69.8&#x0025 for the RF, BT, DT and LR-based approaches, respectively, under the strong constraint condition, compared with traditional representative methods. Furthermore, the average computation efficiency of the RF, BT, DT and LR-based approaches are 208 679, 269 756, 184 333 and 187 458 times higher than that of existing approach, respectively. Given an RNA design, the BT-based approach demonstrates high computational efficiency and better predictive accuracy in determining the biological effectiveness of molecular hybridization.

Idiopathic pneumonia syndrome (IPS) is a noninfectious acute lung injury, often fatal, following allogeneic hematopoietic cell transplantation (HCT). Similar to graft-versus-host disease (GVHD), IPS is mediated by type 1 cytopathic T cells accompanied with high levels of proinflammatory cytokines. We previously showed that elevated plasma soluble Stimulation-2 (sST2), which acts as a decoy receptor for IL-33, is a risk factor of death by GVHD (N. Engl. J. Med, 2013) or by IPS (Biol Blood Marrow Transplant, 2018). ST2 blockade of the excess of sST2 with a neutralizing antibody or small molecules released plasmatic IL-33, increasing its availability to cytoprotective T cells expressing the transmembrane molecule form of ST2, such as regulatory T cells (Tregs) reducing the type 1 proinflammatory T cell-response (Sci Transl Med, 2015; JCI Insight, 2019). The membrane-ST2 is also expressed on type 2 innate lymphoid cells (ILC2s), mostly present in lungs. Herein, we first confirmed in a cohort of 673 HCT patients that plasma sST2 measured 14 days following HCT is increased 10 fold in IPS patients (n=22) as compared to controls with no IPS/GVHD (n=271), and is 6 fold higher as compared to GVHD patients (n=380) (Figure 1A). Patients with IPS and high sST2 levels above the median of 200 ng/ml, were significantly more likely to die than patients with lower sST2 levels (Figure 1B).

Paczesny: Viracor Eurofins Clinical Diagnostic: Patents & Royalties.

Objectives:Using predictive modeling techniques, we developed and compared appointment no-show prediction models to better understand appointment adherence in underserved populations. Methods and Materials:We collected electronic health record (EHR) data and appointment data including patient, provider and clinical visit characteristics over a 3-year period. All patient data came from an urban system of community health centers (CHCs) with 10 facilities. We sought to identify critical variables through logistic regression, artificial neural network, and naïve Bayes classifier models to predict missed appointments. We used 10-fold cross-validation to assess the models’ ability to identify patients missing their appointments. Results:Following data preprocessing and cleaning, the final dataset included 73811 unique appointments with 12,392 missed appointments. Predictors of missed appointments versus attended appointments included lead time (time between scheduling and the appointment), patient prior missed appointments, cell phone ownership, tobacco use and the number of days since last appointment. Models had a relatively high area under the curve for all 3 models (e.g., 0.86 for naïve Bayes classifier). Discussion:Patient appointment adherence varies across clinics within a healthcare system. Data analytics results demonstrate the value of existing clinical and operational data to address important operational and management issues. Conclusion:EHR data including patient and scheduling information predicted the missed appointments of underserved populations in urban CHCs. Our application of predictive modeling techniques helped prioritize the design and implementation of interventions that may improve efficiency in community health centers for more timely access to care. CHCs would benefit from investing in the technical resources needed to make these data readily available as a means to inform important operational and policy questions.