Diabetic retinopathy classification for supervised machine learning algorithms

Background Artificial intelligence and automated technology were first reported more than 70 years ago and nowadays provide unprecedented diagnostic accuracy, screening capacity, risk stratification, and workflow optimization. Diabetic retinopathy is an important cause of preventable blindness worldwide, and artificial intelligence technology provides precocious diagnosis, monitoring, and guide treatment. High-quality exams are fundamental in supervised artificial intelligence algorithms, but the lack of ground truth standards in retinal exams datasets is a problem. Main body In this article, ETDRS, NHS, ICDR, SDGS diabetic retinopathy grading, and manual annotation are described and compared in publicly available datasets. The various DR labeling systems generate a fundamental problem for AI datasets. Possible solutions are standardization of DR classification and direct retinal-finding identifications. Conclusion Reliable labeling methods also need to be considered in datasets with more trustworthy labeling.

Enhancing Design for Additive Manufacturing Workflow: Optimization, Design and Simulation Tools

In the last few decades, complex light-weight designs have been successfully produced via additive manufacturing (AM), launching a new era in the thinking–design process. In addition, current software platforms provide design tools combined with multi-scale simulations to exploit all the technology benefits. However, the literature highlights that several stages must be considered in the design for additive manufacturing (DfAM) process, and therefore, performing holistic guided-design frameworks become crucial to efficiently manage the process. In this frame, this paper aims at providing the main optimization, design, and simulation tools to minimize the number of design evaluations generated through the different workflow assessments. Furthermore, DfAM phases are described focusing on the implementation of design optimization strategies as topology optimization, lattice infill optimization, and generative design in earlier phases to maximize AM capabilities. In conclusion, the current challenges for the implementation of the workflow are hence described.

Use of failure modes and effects analysis to mitigate potential risks prior to implementation of an intravenous compounding technology

Disclaimer In an effort to expedite the publication of articles related to the COVID-19 pandemic, AJHP is posting these manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. Purpose The purpose of this study was to identify potential failure points in a new chemotherapy preparation technology and to implement changes that prevent or minimize the consequences of those failures before they occur using the failure modes and effects analysis (FMEA) approach. Methods An FMEA was conducted by a team of medication safety pharmacists, oncology pharmacists and technicians, leadership from informatics, investigational drug, and medication safety services, and representatives from the technology vendor. Failure modes were scored using both Risk Priority Number (RPN) and Risk Hazard Index (RHI) scores. Results The chemotherapy preparation workflow was defined in a 41-step process with 16 failure modes. The RPN and RHI scores were identical for each failure mode because all failure modes were considered detectable. Five failure modes, all attributable to user error, were deemed to pose the highest risk. Mitigation strategies and system changes were identified for 2 failure modes, with subsequent system modifications resulting in reduced risk. Conclusion The FMEA was a useful tool for risk mitigation and workflow optimization prior to implementation of an intravenous compounding technology. The process of conducting this study served as a collaborative and proactive approach to reducing the potential for medication errors upon adoption of new technology into the chemotherapy preparation process.

Multi-objective workflow optimization strategy (MOWOS) for cloud computing

Workflow scheduling involves mapping large tasks onto cloud resources to improve scheduling efficiency. This has attracted the interest of many researchers, who devoted their time and resources to improve the performance of scheduling in cloud computing. However, scientific workflows are big data applications, hence the executions are expensive and time consuming. In order to address this issue, we have extended our previous work ”Cost Optimised Heuristic Algorithm (COHA)” and presented a novel workflow scheduling algorithm named Multi-Objective Workflow Optimization Strategy (MOWOS) to jointly reduce execution cost and execution makespan. MOWOS employs tasks splitting mechanism to split large tasks into sub-tasks to reduce their scheduling length. Moreover, two new algorithms called MaxVM selection and MinVM selection are presented in MOWOS for task allocations. The design purpose of MOWOS is to enable all tasks to successfully meet their deadlines at a reduced time and budget. We have carefully tested the performance of MOWOS with a list of workflow inputs. The simulation results have demonstrated that MOWOS can effectively perform VM allocation and deployment, and well handle incoming streaming tasks with a random arriving rate. The performance of the proposed algorithm increases significantly in large and extra-large workflow tasks than in small and medium workflow tasks when compared to the state-of-art work. It can greatly reduce cost by 8%, minimize makespan by 10% and improve resource utilization by 53%, while also allowing all tasks to meet their deadlines.

Sustainable Manufacturing: Application of Optimization to Textile Manufacturing Plants

The main goal of manufacturing industry is to produce the end products on time with good quality and keep the resource wastage low. However, manufacturing industry face several challenges such as bottle necks in the workflow, unsynchronized production, and sudden increase in product demands. In this paper, we are proposing a management platform for textile manufacturing plants with following modules: (1) sewing workflow optimization (2) quality assurance workflow optimization and (3) finishing workflow optimizations. We have used Genetic Programming (GP) approach, to optimize the workflows, considering different factors that affect each workflow. Our results show that, using our proposed platform, the manufacturing workflows can be optimized and reduce the bottle necks in the workflows and resource wastage in the manufacturing plant.

Improving efficiency through workflow optimization in a pharmacist-run diabetes clinic

Purpose To evaluate the impact of hiring nonclinical support staff on pharmacist productivity and diabetes control outcomes in internal medicine clinics of an integrated healthcare system. Methods A retrospective, longitudinal cohort study was conducted. Patients were included if they were contacted by telephone for a diabetes consultation with a clinical pharmacist from July 1, 2015, through June 30, 2017. Nonclinical support staff were hired in July 2016 to schedule patient appointments with the clinical pharmacists. The primary outcome was the average rate of completed telephone encounters per month before and after hiring of nonclinical support staff. The secondary outcome was the mean change in glycated hemoglobin (HbA1c) level in patients who had a laboratory assay completed within 90 days of clinical pharmacist outreach. The tertiary outcome was the call completion rate for scheduled appointments vs unscheduled calls. Results In total, 6,709 patients were included; their average age was 55 years. After the intervention, the mean (SD) rate of completed telephone encounters increased from 61% (3.8%) to 77% (3.5%) (P < 0.001). Small improvements were noted in glycemic control, as measured by the mean (SD) percentage of patients with an HbA1c concentration of <8%, which increased from 31% (5.2%) to 42% (3.0%) (P < 0.001), and the mean (SD) change in average HbA1c concentration, which increased from 8.9% (0.2%) to 8.5% (0.1%) (P < 0.001). Throughout the study, scheduled calls were more likely to be completed than unscheduled calls (mean [SD] completion rate, 66% [9.0%] vs 74% [6.0%]; P < 0.001). Conclusion Hiring nonclinical support staff led to greater efficiency among the clinical pharmacist team, yielding a higher volume of telephone interactions, a modest overall decrease in HbA1c values, and an increased likelihood of reaching patients by phone.