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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Entropy removal of medical diagnostics

Shannon entropy is a core concept in machine learning and information theory, particularly in decision tree modeling. To date, no studies have extensively and quantitatively applied Shannon entropy in a systematic way to quantify the entropy of clinical situations using diagnostic variables (true and false positives and negatives, respectively). Decision tree representations of medical decision-making tools can be generated using diagnostic variables found in literature and entropy removal can be calculated for these tools. This concept of clinical entropy removal has significant potential for further use to bring forth healthcare innovation, such as quantifying the impact of clinical guidelines and value of care and applications to Emergency Medicine scenarios where diagnostic accuracy in a limited time window is paramount. This analysis was done for 623 diagnostic tools and provided unique insights into their utility. For studies that provided detailed data on medical decision-making algorithms, bootstrapped datasets were generated from source data to perform comprehensive machine learning analysis on these algorithms and their constituent steps, which revealed a novel and thorough evaluation of medical diagnostic algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Using personal monitoring data to derive organ doses for medical radiation workers in the Million Person Study—considerations regarding NCRP Commentary no. 30

The study of low dose and low-dose rate exposure is of central importance in understanding the possible range of health effects from prolonged exposures to radiation. The One Million Person Study of Radiation Workers and Veterans (MPS) of low-dose health effects was designed to evaluate radiation risks among healthy American workers and veterans. The MPS is evaluating low-dose and dose-rate effects, intakes of radioactive elements, cancer and non-cancer outcomes, as well as differences in risks between women and men. Medical radiation workers make up a large group of individuals occupationally exposed to low doses of radiation from external x-ray/gamma exposures. For the MPS, about 100 000 United States medical radiation workers have been selected for study. The approach to the complex dosimetry circumstances for such workers over three to four decades of occupation were initially and broadly described in National Council on Radiation Protection and Measurements (NCRP) Report No. 178. NCRP Commentary No. 30 provides more detail and describes an optimum approach for using personal monitoring data to estimate lung and other organ doses applicable to the cohort and provides specific precautions/considerations applicable to the dosimetry of medical radiation worker organ doses for use in epidemiologic studies. The use of protective aprons creates dosimetric complexity. It is recommended that dose values from dosimeters worn over a protective apron be reduced by a factor of 20 for estimating mean organ doses to tissues located in the torso and that 15% of the marrow should be assumed to remain unshielded for exposure scenarios when aprons are worn. Conversion coefficients relating personal dose equivalent, H p (10) in mSv, to mean absorbed doses to organs and tissues, D T in mGy, for females and males for six exposure scenarios have been determined and presented for use in the MPS. This Memorandum summarises several key points in NCRP Commentary No. 30.

Environmental Sciences & Ecology↗
DOE OSTI2024

Assessing the reliability of medical resource demand models in the context of COVID-19

Abstract Background Numerous medical resource demand models have been created as tools for governments or hospitals, aiming to predict the need for crucial resources like ventilators, hospital beds, personal protective equipment (PPE), and diagnostic kits during crises such as the COVID-19 pandemic. However, the reliability of these demand models remains uncertain. Methods Demand models typically consist of two main components: hospital use epidemiological models that predict hospitalizations or daily admissions, and a demand calculator that translates the outputs of the epidemiological model into predictions for resource usage. We conducted separate analyses to evaluate each of these components. In the first analysis, we validated various hospital use epidemiological models using a recent validation framework designed for epidemiological models. This allowed us to quantify the accuracy of the models in predicting critical aspects such as the date and magnitude of local COVID-19 peaks, among other factors. In the second analysis, we evaluated a range of demand calculators for ventilators, medical gowns, and COVID-19 test kits. To achieve this, we decoupled these demand calculators from the underlying epidemiological models and provided ground truth data for their inputs. This approach enabled a direct comparison of the demand calculators, comparing them against each other and actual usage data when available. The code is available athttps://doi.org/10.5281/zenodo.13712387. Results Performance varied greatly across the epidemiological models, with greater variability in COVID-19 hospital use predictions than for COVID-19 deaths as analyzed previously. Some models did not have any peaks. Among those that did, the models under-estimated date of peak approximately as often as they over-estimated, but were more likely to under-estimate magnitude of peak, with typical relative errors around 50%. Regarding demand calculator predictions, there was significant variability, including five-fold differences in predictions for gown models. Validation against actual or surrogate usage data illustrated the potential value of demand models while demonstrating their limitations. Conclusions The emerging field of demand modeling holds promise in averting medical resource shortages during future public health emergencies. However, achieving this potential necessitates focused efforts on standardization, transparency, and rigorous model validation before placing reliance on demand models in critical public health decision-making.

Medical Informatics↗
DOE OSTI2020

Domestic Supply Chain of Medical Consumables Needed During COVID-19 Pandemic

This report is an assessment of the availability, demand, and production capability of medical consumables needed to combat the COVID-19 pandemic. We focus here on the ability of the supply chain to increase production to meet demand during peak pandemic conditions. In addition, we report on domestic production efforts and supply chain issues, as well as the propensity of other countries to limit export of needed medical supplies to the United States. In brief, the domestic supply chain and production capability vary greatly depending on the consumable. Some consumables, such as N95 masks or face shields, can likely be nearly fully supplied by domestic production, while other consumables, such as gloves or surgical masks, have little domestic production and thus will depend on imports to meet demand. The assessment pulls primarily from published news sources and company press releases. As such, the demand and production numbers are not precisely known, and some degree of uncertainty exists in them. The total production capability is also often difficult to ascertain from these reports especially as other industries have begun to supplement existing supply chains. In addition, local conditions (including company financial decisions, local public health issues, and political aspects) can vary throughout the pandemic which will affect future production. The report also pulls from estimates of demand and domestic production of some consumables compiled by the White House COVID-19 Supply Chain Taskforce (SCTF) presented by Rear Admiral John Polowczyk before a June 9, 2020 hearing of the Senate Homeland Security and Governmental Affairs Committee. These numbers, where applicable, are likely to be more accurate than those found in public news sources since they are more directly tied to private companies’ actual orders and actual production estimates, as opposed to publicly released estimates. Finally, we note that other issues with the supply chain, including the production of raw materials, increase demand due to industries that have not previously used PPE now using it, and increased domestic production from non-traditional suppliers, make complete picture of a rapidly changing supply chain difficult to obtain. This report attempts to produce an accurate image of the supply and estimated demand, as understood by the authors who are not experts in the medical supply chain.

42 ENGINEERING↗
DOE OSTI2022

Implantable photoelectronic charging (I-PEC) for medical implants

Medical implants with functionalities such as sensing, health monitoring, stimulation, diagnosis, and physiological treatment are rapidly growing. With the increasing functional sophistication and addition of modules such as data transmission, on-chip processing, and data storage, energy demand of the implantable system is also growing. Using implantable energy harvester either to recharge or ultimately replace hazardous battery is essential to provide a long-term sustainable solution. Furthermore, energy harvesting techniques using piezoelectric, thermoelectric, radio frequency power transmission, biofuel, and photoelectronic (or sometimes termed as “photovoltaic” in terms of solar light harvesting, i.e., PV) conversion, have been attempted for the implantable, but these methods are currently limited by insufficient power output, large footprint, and low efficiency. Nevertheless, the planar PV with potential of lighter weight, higher energy density, and higher efficiency, provides promising power solution for in-body medical implants. In this short review, we will discuss the potential opportunities and challenges associated with PV's for medical implants, covering materials, to devices, and to system level requirements.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Federated benchmarking of medical artificial intelligence with MedPerf

Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain. MedPerf focuses on enabling federated evaluation of AI models, by securely distributing them to different facilities, such as healthcare organizations. This process of bringing the model to the data empowers each facility to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status and real-world deployment, our roadmap and, importantly, the use of MedPerf with multiple international institutions within cloud-based technology and on-premises scenarios. Finally, we welcome new contributions by researchers and organizations to further strengthen MedPerf as an open benchmarking platform.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Deep medical image analysis with representation learning and neuromorphic computing

Deep learning is increasingly used in medical imaging, improving many steps of the processing chain, from acquisition to segmentation and anomaly detection to outcome prediction. Yet significant challenges remain: (i) image-based diagnosis depends on the spatial relationships between local patterns, something convolution and pooling often do not capture adequately; (ii) data augmentation, the de facto method for learning three-dimensional pose invariance, requires exponentially many points to achieve robust improvement; (iii) labelled medical images are much less abundant than unlabelled ones, especially for heterogeneous pathological cases; and (iv) scanning technologies such as magnetic resonance imaging can be slow and costly, generally without online learning abilities to focus on regions of clinical interest. To address these challenges, novel algorithmic and hardware approaches are needed for deep learning to reach its full potential in medical imaging.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

High-Throughput Computing: Case Study of Medical Image Processing Applications

HPC is designed for large-scale simulations using monolithic codes of tightly coupled processes highly optimized to deliver decreased time to solution. Medical image processing is not a traditional field of HPC. Similar to AI applications, medical image processing parses large datasets, typically multiple times, to support a variety of studies for classification, diagnosis or monitoring purposes. The convergence of AI, HPC and Big Data encouraged more fields using image processing to transition to HPC. However, not all applications benefit from the same optimizations. In this paper we focus on high throughput medical image processing applications that analyze a huge dataset of small MRI images and that require HPC systems to decrease the time of parsing the entire dataset and not individual MRIs. We show in this research the performance of running SLANT, an image processing application for a whole brain segmentation, on large-scale systems and highlight performance limitations. We present optimizations prioritizing throughput that exhibit a 3.5x speed-up on the Summit Supercomputer that can be used as a baseline for building a high-throughput execution framework for other HPC systems.

Predescu, Maria↗
DOE OSTI2020

Updated Medical Waste Management Plans and Emergency Action Plans for the Lawrence Livermore National Laboratory Main Site

Attached are the updated Medical Waste Management Plans and Emergency Action Plans for Lawrence Livermore National Laboratory’s (LLNL) Biosafety Level 2 (BSL-2) facilities and Biosafety Level 3 (BSL-3) facility. A redline version of each document has also been provided. Minor changes such as facility personnel, contact information, and titles for those that support onsite Biosafety Labs at LLNL have been made to the document. It should be noted that the most recent versions of the Medical Waste Large Quantity Generator Registration Application Form and Medical Waste Management Plan were incorporated and sourced from the Alameda County Department of Environmental Health website.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Radiological Handling and Containment Considerations in Support of an American Medical Isotope Producer

This work will discuss evaluation and design of new and modified facility level radiological containment, transfer, and handling systems in support of the United States’ American Medical Isotope Production Act. In the United States alone, Molybdenum-99 (Mo-99) is used as a precursor to locally generate its decay product, technetium-99m (Tc-99m), for use in approximately 40,000 medical procedures every day to detect cancer and heart disease. Currently, however, Mo-99 is primarily produced overseas and typically requires highly enriched uranium (HEU) which is classified as weapon -usable. This poses a serious security risk to the United States and, in 2012, Congress passed the American Medical Isotope ACT which aimed to cease all Mo-99 production that requires HEU and support American based companies who have found safer production methods. Through this effort, a system was designed and developed to access irradiated material in processing hot cells for ease of material introduction and removal in the confined area. The proposed solution uses a double-door design that allows for easy access to material while also providing shielding through a mechanically manipulated carousel. A system was also designed to transport irradiated materials from target reactors to a target receipt box using a rail guide system, pneumatic motor, and lead screw drive.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

The effect of anxiety on sleep disorders in medical students: a moderated mediation model

The relationship between anxiety and sleep disorders is a key research topic in the academic community. However, evidence on the mechanism through which anxiety influences sleep disorders remains limited. The purpose of this study was to investigate the roles of flourishing and neuroticism in the mechanism through which anxiety influences sleep disorders in medical students. We constructed a moderated mediation model and tested the mediating role of flourishing and the moderating role of neuroticism in medical college students. The results showed that: (1) anxiety was significantly and positively related to sleep disorders and significantly and negatively related to flourishing; flourishing was significantly and negatively related to sleep disorders; neuroticism was significantly and positively related to sleep disorders; (2) flourishing had a mediation effect on the relationship between anxiety and sleep disorders; (3) neuroticism moderated the process through which flourishing mediated the effect of anxiety on sleep disorders. Our research expands the literature on the mechanism underlying the effects of anxiety on sleep disorders and provides insights into the potential prevention and intervention of sleep and emotional problems in medical students.

Yu, Chuang↗
DOE OSTI2023

Evaluation of two extraction chromatography resins for scandium and titanium separation for medical isotope production

Scandium-47 ( 47 Sc) can be used in nuclear medicine as a therapeutic-diagnostic, or "theragnostic," radioactive medical isotope for cancer detection and treatment. The 47 Sc isotope can be produced through the photonuclear reaction 48 Ti(γ,p) 47 Sc by irradiating enriched 48 Ti target material. The enriched target material necessary for production is costly; 48 TiO 2 costs ~ $\$$1550/g, and targets can be > 50 g ($\$$77,500) to produce medically relevant amounts of 47 Sc. In order to keep costs low, a highly efficient separation of scandium from bulk titanium is desired, along with efficient methods for recycling the target material. Here this research is focused on evaluating efficient methods for the separation of scandium from bulk quantities of titanium using commercially available diglycolamide-based and hydroxamate-based extraction chromatography resins (DGA resin and ZR resin, respectively). The sorption of 47 Sc and Ti on these resins were investigated at varying concentrations of HNO 3 , HCl, H 2 SO 4 , and HF to explore how they might be used in a large-scale production/processing setting.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

Association Between Dispatch Complaint and Critical Prehospital Time Intervals in Suspected Stroke 911 Activations in the National Emergency Medical Services Information System, 2012–2016

Emergency Medical Services can help improve stroke outcomes by recognizing stroke symptoms, establishing response priority for 911 calls, and minimizing prehospital delays. This study examines 911 stroke events and evaluates associations between events dispatched as stroke and critical EMS time intervals. Data from the National Emergency Medical Services Information System, 2012 to 2016, were analyzed. Activations from 911 calls with a primary or secondary provider impression of stroke were included for adult patients transported to a hospital destination. Three prehospital time intervals were evaluated: (1) response time (RT) ≤8 min, (2) on-scene time (OST) ≤15 min, and (3) transport time (TT) ≤12 min. Associations between stroke dispatch complaint and prehospital time intervals were assessed using multivariate regression to estimate adjusted risk ratios (ARR) and 95% confidence intervals (CIs). Approximately 37% of stroke dispatch complaints were identified by EMS as a suspected stroke. Compared to stroke events without a stroke dispatch complaint, median OST was shorter for events with a stroke dispatch (16 min vs. 14 min, respectively). In adjusted analyses, events dispatched as stroke were more likely to meet the EMS time benchmark for OST ≤15 min (OST, 1.20 [1.20–1.21]), but not RT or TT (RT, [1.00–1.01]; TT, 0.95 [0.94–0.95]). Furthermore, our results indicate that dispatcher recognition of stroke symptoms reduces the time spent on-scene by EMS personnel. These findings can inform future EMS stroke education and quality improvement efforts to emphasize dispatcher recognition of stroke signs and symptoms, as EMS dispatchers play a crucial role in optimizing the prehospital response.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Development and validation of a 30-day mortality index based on pre-existing medical administrative data from 13,323 COVID-19 patients: The Veterans Health Administration COVID-19 (VACO) Index

Background Available COVID-19 mortality indices are limited to acute inpatient data. Using nationwide medical administrative data available prior to SARS-CoV-2 infection from the US Veterans Health Administration (VA), we developed the VA COVID-19 (VACO) 30-day mortality index and validated the index in two independent, prospective samples. Methods and findings We reviewed SARS-CoV-2 testing results within the VA between February 8 and August 18, 2020. The sample was split into a development cohort (test positive between March 2 and April 15, 2020), an early validation cohort (test positive between April 16 and May 18, 2020), and a late validation cohort (test positive between May 19 and July 19, 2020). Our logistic regression model in the development cohort considered demographics (age, sex, race/ethnicity), and pre-existing medical conditions and the Charlson Comorbidity Index (CCI) derived from ICD-10 diagnosis codes. Weights were fixed to create the VACO Index that was then validated by comparing area under receiver operating characteristic curves (AUC) in the early and late validation cohorts and among important validation cohort subgroups defined by sex, race/ethnicity, and geographic region. We also evaluated calibration curves and the range of predictions generated within age categories. 13,323 individuals tested positive for SARS-CoV-2 (median age: 63 years; 91% male; 42% non-Hispanic Black). We observed 480/3,681 (13%) deaths in development, 253/2,151 (12%) deaths in the early validation cohort, and 403/7,491 (5%) deaths in the late validation cohort. Age, multimorbidity described with CCI, and a history of myocardial infarction or peripheral vascular disease were independently associated with mortality–no other individual comorbid diagnosis provided additional information. The VACO Index discriminated mortality in development (AUC = 0.79, 95% CI: 0.77–0.81), and in early (AUC = 0.81 95% CI: 0.78–0.83) and late (AUC = 0.84, 95% CI: 0.78–0.86) validation. The VACO Index allows personalized estimates of 30-day mortality after COVID-19 infection. For example, among those aged 60–64 years, overall mortality was estimated at 9% (95% CI: 6–11%). The Index further discriminated risk in this age stratum from 4% (95% CI: 3–7%) to 21% (95% CI: 12–31%), depending on sex and comorbid disease. Conclusion Prior to infection, demographics and comorbid conditions can discriminate COVID-19 mortality risk overall and within age strata. The VACO Index reproducibly identified individuals at substantial risk of COVID-19 mortality who might consider continuing social distancing, despite relaxed state and local guidelines.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Xenon Abatement Simulations to Support the KAERI Medical Isotope Facility

To maintain and improve the verification regime that is outlined by the Preparatory Commission of the Comprehensive Nuclear Test-Ban Treaty there is a need to understand and reduce the radioxenon releases from medical isotope production facilities. In support of this objective Pacific Northwest National Laboratory (PNNL) was tasked with, modeling and evaluate the abatement process and delay bed designs for the medical isotope production facility under construction by the Korea Atomic Energy Research Institute (KAERI). This report includes this analysis and provides PNNL proposed modifications to the KAERI adsorption bed design.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2022

Analyzing Relationships Between Economic and Neighborhood-Related Social Determinants of Health and Intensive Care Unit Length of Stay for Critically Ill Children With Medical Complexity Presenting With Severe Sepsis

Objectives: Of the Social Determinants of Health (SDoH), we evaluated socioeconomic and neighborhood-related factors which may affect children with medical complexity (CMC) admitted to a Pediatric Intensive Care Unit (PICU) in Shelby County, Tennessee with severe sepsis and their association with PICU length of stay (LOS). We hypothesized that census tract-level socioeconomic and neighborhood factors were associated with prolonged PICU LOS in CMC admitted with severe sepsis in the underserved community. Methods: This single-center retrospective observational study included CMC living in Shelby County, Tennessee admitted to the ICU with severe sepsis over an 18-month period. Severe sepsis CMC patients were identified using an existing algorithm incorporated into the electronic medical record at a freestanding children's hospital. SDoH information was collected and analyzed using patient records and publicly available census-tract level data, with ICU length of stay as the primary outcome. Results: 83 encounters representing 73 patients were included in the analysis. The median PICU LOS was 9.04 days (IQR 3.99–20.35). The population was 53% male with a median age of 4.1 years (IQR 1.96–12.02). There were 57 Black/African American patients (68.7%) and 85.5% had public insurance. Based on census tract-level data, about half (49.4%) of the CMC severe sepsis population lived in census tracts classified as suffering from high social vulnerability. There were no statistically significant relationships between any socioeconomic and neighborhood level factors and PICU LOS. Conclusion: Pediatric CMC severe sepsis patients admitted to the PICU do not have prolonged lengths of ICU stay related to socioeconomic and neighborhood-level SDoH at our center. A larger sample with the use of individual-level screening would need to be evaluated for associations between social determinants of health and PICU outcomes of these patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Production Review of Accelerator-Based Medical Isotopes

The production of reactor-based medical isotopes is fragile, which has meant supply shortages from time to time. This paper reviews alternative production methods in the form of cyclotrons, linear accelerators and neutron generators. Finally, the status of the production of medical isotopes in China is described.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

The Evolution of Medical Countermeasures for Ebola Virus Disease: Lessons Learned and Next Steps

The Ebola virus disease outbreak that occurred in Western Africa from 2013–2016, and subsequent smaller but increasingly frequent outbreaks of Ebola virus disease in recent years, spurred an unprecedented effort to develop and deploy effective vaccines, therapeutics, and diagnostics. This effort led to the U.S. regulatory approval of a diagnostic test, two vaccines, and two therapeutics for Ebola virus disease indications. Moreover, the establishment of fieldable diagnostic tests improved the speed with which patients can be diagnosed and public health resources mobilized. The United States government has played and continues to play a key role in funding and coordinating these medical countermeasure efforts. Here, we describe the coordinated U.S. government response to develop medical countermeasures for Ebola virus disease and we identify lessons learned that may improve future efforts to develop and deploy effective countermeasures against other filoviruses, such as Sudan virus and Marburg virus.

59 BASIC BIOLOGICAL SCIENCES↗