Three-Dimensional Scanning Applied for Flexible and In Situ Calibration of Galvanometric Scanner Systems.

Galvanometric laser scanner (GLS) systems are widely used for materials processing due to their high precision, processing velocity, and repeatability. However, GLS systems generally suffer from scan field distortions due to joint and task space relationship errors. The problem is further pronounced in robotic applications, where the GLS systems are manipulated in space, as unknown errors in the relative pose of the GLS can be introduced. This paper presents an in situ, data-driven methodology for calibrating GLS systems using 3D scanning, emphasising the flexibility, generalisation, and automated industrial integration. Three-dimensional scanning serves two primary purposes: (1) determining the relative pose between the GLS system and the calibration plate to minimise calibration errors and (2) supplying an image processing algorithm with dense and accurate data to measure the scan field distortion based on the positional deviations of marked fiducials. The measured deviations are used to train a low-complexity Radial Basis Function (RBF) network to predict and correct the distorted scan field. The proposed method shows promising results and significantly reduces the scan field distortion without the use of specialised calibration tools and with limited knowledge of the optical design of the GLS system.

Learning Insurance Benefit Rules from Policy Texts with Small Labeled Data.

To protect vital health program funds from being paid out on services that are wasteful and inconsistent with medical practices, government healthcare insurance programs need to validate the integrity of claims submitted by providers for reimbursement. However, due the complexity of healthcare billing policies and the lack of coded rules, maintaining "integrity" is a labor-intensive task, often narrow-scope and expensive. We propose an approach that combines deep learning and an ontology to support the extraction of actionable knowledge on benefit rules from regulatory healthcare policy text. We demonstrate its feasibility even in the presence of small ground truth labeled data provided by policy investigators. Leveraging deep learning and rich ontological information enables the system to learn from human corrections and capture better benefit rules from policy text, beyond just using a deterministic approach based on pre-defined textual and semantic pattterns.

Discovering Associations between Social Determinants and Health Outcomes: Merging Knowledge Graphs from Literature and Electronic Health Data.

Social Determinants of Health (SDoH) are an increasingly important part of the broader research and public health efforts in understanding individuals' physical and mental well-being. Despite this, non-clinical factors affecting health are poorly recorded in electronic health databases and techniques to study how SDoH might relate to population outcomes are lacking. This paper proposes an approach to systematically identify and quantify associations between SDoH and health-related outcomes in a specific cohort of people by (1) leveraging published evidence from literature to build a knowledge graph of health and social factor associations and (2) analysing a large dataset of claims and medical records where those associations may be found. This work demonstrates how the proposed approach could be used to generate hypotheses and inform further research on SDoH in a data-driven manner.

Ontology-Guided Policy Information Extraction for Healthcare Fraud Detection.

Financial losses in Medicaid, from Fraud, Waste and Abuse (FWA), in the United States are estimated to be in the tens of billions of dollars each year. This results in escalating costs as well as limiting the funding available to worthy recipients of healthcare. The Centers for Medicare & Medicaid Services mandate thorough auditing, in which policy investigators manually research and interpret the policy to validate the integrity of claims submitted by providers for reimbursement, a very time-consuming process. We propose a system that aims to interpret unstructured policy text to semi-automatically audit provider claims. Guided by a domain ontology, our system extracts entities and relations to build benefit rules that can be executed on top of claims to identify improper payments, and often in turn payment policy or claims adjudication system vulnerabilities. We validate the automatic knowledge extraction from policies based on ground truth created by domain experts. Lastly, we discuss how the system can co-reason with human investigators in order to increase thoroughness and consistency in the review of claims and policy, to identify providers that systematically violate policies and to help in prioritising investigations.

Effect of X-ray radiation exposure on lyophilized recombinant activated factor VII (formulated for storage at room temperature).

OBJECTIVE: To investigate effects of air transport and X-ray radiation exposure through airport security of a new room temperature-stable rFVIIa formulation (NovoSeven/NovoSeven RT, Novo Nordisk A/S, Bagsvaerd, Denmark) lyophilized using in vitro study methodology, thus evaluating possible effects of exposure through airport security and airplane travel. RESEARCH DESIGN AND METHODS: The effect of X-ray radiation exposure of rFVIIa and the solvent histidine at two different doses (400 microSv and 2000 microSv) was examined immediately after exposure, and post-exposure after storage at 30 degrees C for 1 month. References samples, not exposed to X-ray radiation, were used for comparison. MAIN OUTCOME MEASURE: Stability of rFVIIa after X-ray radiation exposure. RESULTS: All product parameters analyzed were within the acceptance criteria as well as within shelf life specification limits for the selected parameters for each product. CONCLUSION: The product rFVIIa and solvent histidine are therefore not expected to be affected as a consequence of airplane traveling and X-ray exposure during airport security check using hand luggage scanners.

In vitro stability of lyophilized and reconstituted recombinant activated factor VII formulated for storage at room temperature.

BACKGROUND: Recombinant activated factor VII (rFVIIa) is indicated to treat bleeding episodes or prevent bleeding related to surgery in patients with hemophilia A or B who have antibodies to coagulation factors VIII or IX. The first-generation rFVIIa formulation is stable when stored under refrigeration. A new formulation has been developed for storage at room temperature, which may improve patients' access to treatment during bleeding episodes. OBJECTIVE: These in vitro experiments were conducted to evaluate the stability of the new formulation of rFVIIa, both lyophilized and reconstituted, under the expected storage conditions, as well as at higher temperatures. METHODS: The stability of the new rFVIIa formulation when stored under various conditions before and after reconstitution was evaluated in terms of retained activity (clotting assay), rFVIIa content (high-performance liquid chromatography [HPLC]), and rFVIIa degradation products (HPLC), including aggregates (dimer/oligomer). Activity was analyzed within specific limits representing the allowable minimum/maximum for each test parameter at the end of the product's shelf-life, as adopted by the European Medicines Agency and the US Food and Drug Administration. Before reconstitution, vials from 9 lots of the new rFVIIa formulation, 3 of each size (1, 2, and 5 mg/vial), were stored at refrigerated temperature (5 degrees C) and at room temperature (25 degrees C) for 24 months, and at 30 degrees C for 12 months. To simulate short-term exposure to temperatures higher than recommended, samples were stored at 40 degrees C for 6 months, followed by storage at 25 degrees C for 12 months. To simulate the home setting, in which the product may be alternately stored in and out of the refrigerator, samples were stored at 30 degrees C for 8 hours and then at 5 degrees C for 16 hours, repeated daily for 5 days. To analyze the effect of storage at extremely elevated temperatures, samples were exposed to temperatures of 50 degrees C, 60 degrees C, and 70 degrees C for 12 hours. After reconstitution, samples were maintained at 25 degrees C for up to 6 hours or at 5 degrees C for up to 24 hours. RESULTS: The specific activity and rFVIIa content of the new lyophilized formulation remained stable after storage for 24 months at 5 degrees C and 25 degrees C, and for 12 months at 30 degrees C; after 5 days of daily alternation between storage at 5 degrees C and 30 degrees C; and after storage for 6 months at 40 degrees C followed by 12 months at 25 degrees C. When stored at 50 degrees C and 60 degrees C for 12 hours, activity remained constant, whereas rFVIIa aggregates increased within the specified limits; after storage at 70 degrees C for 12 hours, rFVIIa activity decreased in parallel with the formation of aggregates, which exceeded the specified limit for the 5-mg product. After reconstitution, samples of all vial sizes of the new rFVIIa formulation retained their activity when stored at 25 degrees C for 6 hours and at 5 degrees C for 24 hours. CONCLUSIONS: In these in vitro experiments, the new lyophilized formulation of rFVIIa was stable when stored for 24 months at 25 degrees C, 12 months at 30 degrees C, 6 months at 40 degrees C, and 12 hours at 50 degrees C and 60 degrees C without compromise to its activity or rFVIIa content. The reconstituted product retained activity when stored at 25 degrees C for 6 hours and at 5 degrees C for 24 hours.