Knowledge discovery from posts in online health communities using unified medical language system

Patient-reported posts in Online Health Communities (OHCs) contain various valuable information that can help establish knowledge-based online support for online patients. However, utilizing these reports to improve online patient services in the absence of appropriate medical and healthcare expert knowledge is difficult. Thus, we propose a comprehensive knowledge discovery method that is based on the Unified Medical Language System for the analysis of narrative posts in OHCs. First, we propose a domain-knowledge support framework for OHCs to provide a basis for post analysis. Second, we develop a Knowledge-Involved Topic Modeling (KI-TM) method to extract and expand explicit knowledge within the text. We propose four metrics, namely, explicit knowledge rate, latent knowledge rate, knowledge correlation rate, and perplexity, for the evaluation of the KI-TM method. Our experimental results indicate that our proposed method outperforms existing methods in terms of providing knowledge support. Our method enhances knowledge support for online patients and can help develop intelligent OHCs in the future

Next generation sequencing in early diagnosis of pneumocystis jirovecii pneumonia after chemotherapy: a case report

The incidence of Pneumocystis pneumonia is increasing in immunosuppressive patients. How to diagnose and treat Pneumocystis pneumonia in the early stage has become an important issue for clinicians. The development of Next-generation Sequencing (NGS) provides technical support for the diagnosis of Pneumocystis pneumonia. Case report: A 14-year-old male patient was diagnosed with T lymphoblastoma and treated with chemotherapy. After chemotherapy, the patient developed bone marrow suppression and was complicated with severe pneumonia. He was given endotracheal intubation and ventilator assisted respiration. Samples of patients' alveolar lavage fluid were obtained, and Next-generation Sequencing (NGS) was used for diagnosis, confirming the pathogen as Pneumocystis jiroveci, which was treated by TMP/SMX. The patient's condition gradually improved, and was finally removed from ventilator and endotracheal tube. Pneumocystis jiroveci is a common opportunistic pathogen in immunosuppressive patients, and Next-generation Sequencing (NGS) can be used for rapid diagnosis of Pneumocystis pneumonia, thus improving the clinical therapeutic effect.

Process monitoring based on orthogonal locality preserving projection with maximum likelihood estimation

By integrating two powerful methods of density reduction and intrinsic dimensionality estimation, a new data-driven method, referred to as OLPP-MLE (orthogonal locality preserving projection-maximum likelihood estimation), is introduced for process monitoring. OLPP is utilized for dimensionality reduction, which provides better locality preserving power than locality preserving projection. Then, the MLE is adopted to estimate intrinsic dimensionality of OLPP. Within the proposed OLPP-MLE, two new static measures for fault detection TOLPP2 and SPEOLPP are defined. In order to reduce algorithm complexity and ignore data distribution, kernel density estimation is employed to compute thresholds for fault diagnosis. The effectiveness of the proposed method is demonstrated by three case studies

Reinforcement Calculation of Circular and Annular Cross Section

Circular and annular cross-section is one of the more common cross-section forms, such as water towers, piers, etc. But reinforcement calculation of these crosssections is dual, the nonlinearity in width change and the nonlinear stress-strain relationship of concrete and reinforcement, which brings inconvenience to the calculation. In this paper, the corresponding calculation formulas are analytically derived and practical design charts (nomograms) are obtained. Based on stress-stain curve of concrete and reinforcement, five strain distribution regions were constructed. Via strains stress can be determined and then via the stress internal force can be computed, without application of the traditional equivalent rectangular stress block in concrete compression zone. The obtained dimensionless design charts can be applied to all load cases, such as uniform tension, tension with a small and large eccentricity, pure bending, compression with a large and small eccentricity and uniform compression. The calculation by using the design charts is simple and quick

A Knowledge-Constrained Role-Based Access Control model for protecting patient privacy in hospital information systems

Current access control mechanisms of the hospital information system can hardly identify the real access intention of system users. A relaxed access control increases the risk of compromise of patient privacy. To reduce unnecessary access of patient information by hospital staff, this paper proposes a Knowledge-Constrained Role-Based Access Control (KCRBAC)model in which a variety of medical domain knowledge is considered in access control. Based on the proposed Purpose Tree and knowledge-involved algorithms, the model can dynamically define the boundary of access to the patient information according to the context, which helps protect patient privacy by controlling access. Compared with the Role-Based Access Control model, KC-RBAC can effectively protectpatient information according to the results of the experiments

A Survey on Physical Adversarial Attack in Computer Vision

Over the past decade, deep learning has revolutionized conventional tasks that rely on hand-craft feature extraction with its strong feature learning capability, leading to substantial enhancements in traditional tasks. However, deep neural networks (DNNs) have been demonstrated to be vulnerable to adversarial examples crafted by malicious tiny noise, which is imperceptible to human observers but can make DNNs output the wrong result. Existing adversarial attacks can be categorized into digital and physical adversarial attacks. The former is designed to pursue strong attack performance in lab environments while hardly remaining effective when applied to the physical world. In contrast, the latter focus on developing physical deployable attacks, thus exhibiting more robustness in complex physical environmental conditions. Recently, with the increasing deployment of the DNN-based system in the real world, strengthening the robustness of these systems is an emergency, while exploring physical adversarial attacks exhaustively is the precondition. To this end, this paper reviews the evolution of physical adversarial attacks against DNN-based computer vision tasks, expecting to provide beneficial information for developing stronger physical adversarial attacks. Specifically, we first proposed a taxonomy to categorize the current physical adversarial attacks and grouped them. Then, we discuss the existing physical attacks and focus on the technique for improving the robustness of physical attacks under complex physical environmental conditions. Finally, we discuss the issues of the current physical adversarial attacks to be solved and give promising directions