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DOE OSTI2024

Question-answering system extracts information on injection drug use from clinical notes

Background. Injection drug use (IDU) can increase mortality and morbidity. Therefore, identifying IDU early and initiating harm reduction interventions can benefit individuals at risk. However, extracting IDU behaviors from patients’ electronic health records (EHR) is difficult because there is no other structured data available, such as International Classification of Disease (ICD) codes, and IDU is most often documented in unstructured free-text clinical notes. Although natural language processing can efficiently extract this information from unstructured data, there are no validated tools. Methods. Here, to address this gap in clinical information, we design a question-answering (QA) framework to extract information on IDU from clinical notes for use in clinical operations. Our framework involves two main steps: (1) generating a gold-standard QA dataset and (2) developing and testing the QA model. We use 2323 clinical notes of 1145 patients curated from the US Department of Veterans Affairs (VA) Corporate Data Warehouse to construct the gold-standard dataset for developing and evaluating the QA model. We also demonstrate the QA model’s ability to extract IDU-related information from temporally out-of-distribution data. Results. Here, we show that for a strict match between gold-standard and predicted answers, the QA model achieves a 51.65% F1 score. For a relaxed match between the gold-standard and predicted answers, the QA model obtains a 78.03% F1 score, along with 85.38% Precision and 79.02% Recall scores. Moreover, the QA model demonstrates consistent performance when subjected to temporally out-of-distribution data. Conclusions. Our study introduces a QA framework designed to extract IDU information from clinical notes, aiming to enhance the accurate and efficient detection of people who inject drugs, extract relevant information, and ultimately facilitate informed patient care.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Decoding substance use disorder severity from clinical notes using a large language model

Substance use disorder (SUD) poses a major concern due to its detrimental effects on health and society. SUD identification and treatment depend on a variety of factors such as severity, co-determinants (e.g., withdrawal symptoms), and social determinants of health. Existing diagnostic coding systems used by insurance providers, like the International Classification of Diseases (ICD-10), lack granularity for certain diagnoses, but American clinicians will add this granularity (as that found within the Diagnostic and Statistical Manual of Mental Disorders classification or DSM-5) as supplemental unstructured text in clinical notes. Traditional natural language processing (NLP) methods face limitations in accurately parsing such diverse clinical language. Large language models (LLMs) offer promise in overcoming these challenges by adapting to diverse language patterns. This study investigates the application of LLMs for extracting severity-related information for various SUD diagnoses from clinical notes. We propose a workflow employing zero-shot learning of LLMs with carefully crafted prompts and post-processing techniques. Through experimentation with Flan-T5, an open-source LLM, we demonstrate its superior recall compared to the rule-based approach. Focusing on 11 categories of SUD diagnoses, we show the effectiveness of LLMs in extracting severity information, contributing to improved risk assessment and treatment planning for SUD patients.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

The role of bulge formation in the homogenization of stellar populations at Z ∼ 2 as revealed by internal color dispersion in candels

We use data from the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey to study how the spatial variation in the stellar populations of galaxies relates to the formation of galaxies at 1.5 2) show reduced ICD values, implying a greater homogeneity of their stellar populations. This indicates that if a galaxy is to have a quiescent bulge along with a star-forming disk, typical of Hubble sequence galaxies, this is most common for stellar masses 10 < log (M/M{sub ⊙}) < 11 and when the bulge component remains relatively small (n<2).

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2018

Coarse-to-Fine Multi-Task Training of Convolutional Neural Networks for Automated Information Extraction from Cancer Pathology Reports

Information extraction and coding of free-text pathology reports is an important activity for cancer registries to support national cancer surveillance. Cancer registrars must process high volumes of pathology reports on an annual basis. In this study, we investigated an automated approach using a coarse-to-fine training of convolutional neural networks (CNNs) for extracting the primary site, histological grade and laterality from unstructured cancer pathology text reports. Our proposed training scheme consists of two stages. In the first stage, the multi-task learning (MTL) with hard parameter sharing approach is used to train a multi-task MT-CNN model for all the tasks. Then, the TM-CNN parameters are used to initialize a CNN model for each task to be fine trained individually using its corresponding dataset. The performance of our proposed model was compared against a state-of-the-art CNN and the commonly used SVM classifier. We observed that the proposed model consistently outperformed the base line models, especially for the less prevalent classes. Specifically, the proposed training approach achieved a micro-F score of 0.7749 over 12 ICD- O-3 topography codes which is a significant improvement as compared with state-of-the-art CNN (0.7101) and the SVM (0.6019) classifiers. Also, the results demonstrate the potential of the proposed method for handling class imbalance within each task. It significantly improves macro-F score by 24% and 12% of the primary site and histology grade tasks, respectively.

Alawad, Mohammed↗

DOE OSTI2017

Deep Learning for Automated Extraction of Primary Sites from Cancer Pathology Reports

Pathology reports are a primary source of information for cancer registries which process high volumes of free-text reports annually. Information extraction and coding is a manual, labor-intensive process. Here in this study we investigated deep learning and a convolutional neural network (CNN), for extracting ICDO- 3 topographic codes from a corpus of breast and lung cancer pathology reports. We performed two experiments, using a CNN and a more conventional term frequency vector approach, to assess the effects of class prevalence and inter-class transfer learning. The experiments were based on a set of 942 pathology reports with human expert annotations as the gold standard. CNN performance was compared against a more conventional term frequency vector space approach. We observed that the deep learning models consistently outperformed the conventional approaches in the class prevalence experiment, resulting in micro and macro-F score increases of up to 0.132 and 0.226 respectively when class labels were well populated. Specifically, the best performing CNN achieved a micro-F score of 0.722 over 12 ICD-O-3 topography codes. Transfer learning provided a consistent but modest performance boost for the deep learning methods but trends were contingent on CNN method and cancer site. Finally, these encouraging results demonstrate the potential of deep learning for automated abstraction of pathology reports.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2014

SU-E-T-51: Bayesian Network Models for Radiotherapy Error Detection

Purpose: To develop a probabilistic model of radiotherapy plans using Bayesian networks that will detect potential errors in radiation delivery. Methods: Semi-structured interviews with medical physicists and other domain experts were employed to generate a set of layered nodes and arcs forming a Bayesian Network (BN) which encapsulates relevant radiotherapy concepts and their associated interdependencies. Concepts in the final network were limited to those whose parameters are represented in the institutional database at a level significant enough to develop mathematical distributions. The concept-relation knowledge base was constructed using the Web Ontology Language (OWL) and translated into Hugin Expert Bayes Network files via the the RHugin package in the R statistical programming language. A subset of de-identified data derived from a Mosaiq relational database representing 1937 unique prescription cases was processed and pre-screened for errors and then used by the Hugin implementation of the Estimation-Maximization (EM) algorithm for machine learning all parameter distributions. Individual networks were generated for each of several commonly treated anatomic regions identified by ICD-9 neoplasm categories including lung, brain, lymphoma, and female breast. Results: The resulting Bayesian networks represent a large part of the probabilistic knowledge inherent in treatment planning. By populating the networks entirely with data captured from a clinical oncology information management system over the course of several years of normal practice, we were able to create accurate probability tables with no additional time spent by experts or clinicians. These probabilistic descriptions of the treatment planning allow one to check if a treatment plan is within the normal scope of practice, given some initial set of clinical evidence and thereby detect for potential outliers to be flagged for further investigation. Conclusion: The networks developed here support the use of probabilistic models into clinical chart checking for improved detection of potential errors in RT plans.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

SU-E-T-169: Characterization of Pacemaker/ICD Dose in SAVI HDR Brachytherapy

Purpose: It is important to estimate dose to pacemaker (PM)/Implantable Cardioverter Defibrillator (ICD) before undertaking Accelerated Partial Breast Treatment using High Dose Rate (HDR) brachytherapy. Kim et al. have reported HDR PM/ICD dose using a single-source balloon applicator. To the authors knowledge, there have so far not been any published PM/ICD dosimetry literature for the Strut Adjusted Volume Implant (SAVI, Cianna Medical, Aliso Viejo, CA). This study aims to fill this gap by generating a dose look up table (LUT) to predict maximum dose to the PM/ICD in SAVI HDR brachytherapy. Methods: CT scans for 3D dosimetric planning were acquired for four SAVI applicators (6−1-mini, 6−1, 8−1 and 10−1) expanded to their maximum diameter in air. The CT datasets were imported into the Elekta Oncentra TPS for planning and each applicator was digitized in a multiplanar reconstruction window. A dose of 340 cGy was prescribed to the surface of a 1 cm expansion of the SAVI applicator cavity. Cartesian coordinates of the digitized applicator were determined in the treatment leading to the generation of a dose distribution and corresponding distance-dose prediction look up table (LUT) for distances from 2 to 15 cm (6-mini) and 2 to 20 cm (10–1).The deviation between the LUT doses and the dose to the cardiac device in a clinical case was evaluated. Results: Distance-dose look up table were compared to clinical SAVI plan and the discrepancy between the max dose predicted by the LUT and the clinical plan was found to be in the range (−0.44%, 0.74%) of the prescription dose. Conclusion: The distance-dose look up tables for SAVI applicators can be used to estimate the maximum dose to the ICD/PM, with a potential usefulness for quick assessment of dose to the cardiac device prior to applicator placement.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-F-T-452: Influence of Dose Calculation Algorithm and Heterogeneity Correction On Risk Categorization of Patients with Cardiac Implanted Electronic Devices Undergoing Radiotherapy

Purpose: There is a lack of studies with significant cohort data about patients using pacemaker (PM), implanted cardioverter defibrillator (ICD) or cardiac resynchronization therapy (CRT) device undergoing radiotherapy. There is no literature comparing the cumulative doses delivered to those cardiac implanted electronic devices (CIED) calculated by different algorithms neither studies comparing doses with heterogeneity correction or not. The aim of this study was to evaluate the influence of the algorithms Pencil Beam Convolution (PBC), Analytical Anisotropic Algorithm (AAA) and Acuros XB (AXB) as well as heterogeneity correction on risk categorization of patients. Methods: A retrospective analysis of 19 3DCRT or IMRT plans of 17 patients was conducted, calculating the dose delivered to CIED using three different calculation algorithms. Doses were evaluated with and without heterogeneity correction for comparison. Risk categorization of the patients was based on their CIED dependency and cumulative dose in the devices. Results: Total estimated doses at CIED calculated by AAA or AXB were higher than those calculated by PBC in 56% of the cases. In average, the doses at CIED calculated by AAA and AXB were higher than those calculated by PBC (29% and 4% higher, respectively). The maximum difference of doses calculated by each algorithm was about 1 Gy, either using heterogeneity correction or not. Values of maximum dose calculated with heterogeneity correction showed that dose at CIED was at least equal or higher in 84% of the cases with PBC, 77% with AAA and 67% with AXB than dose obtained with no heterogeneity correction. Conclusion: The dose calculation algorithm and heterogeneity correction did not change the risk categorization. Since higher estimated doses delivered to CIED do not compromise treatment precautions to be taken, it’s recommend that the most sophisticated algorithm available should be used to predict dose at the CIED using heterogeneity correction.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

Age density patterns in patients medical conditions: A clustering approach

This paper presents a data analysis framework to uncover relationships between health conditions, age and sex for a large population of patients. We study a massive heterogeneous sample of 1.7 million patients in Brazil, containing 47 million of health records with detailed medical conditions for visits to medical facilities for a period of 17 months. The findings suggest that medical conditions can be grouped into clusters that share very distinctive densities in the ages of the patients. For each cluster, we further present the ICD-10 chapters within it. Finally, we relate the findings to comorbidity networks, uncovering the relation of the discovered clusters of age densities to comorbidity networks literature.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

A Phenome-Wide Association Study of genes associated with COVID-19 severity reveals shared genetics with complex diseases in the Million Veteran Program

The study aims to determine the shared genetic architecture between COVID-19 severity with existing medical conditions using electronic health record (EHR) data. We conducted a Phenome-Wide Association Study (PheWAS) of genetic variants associated with critical illness (n = 35) or hospitalization (n = 42) due to severe COVID-19 using genome-wide association summary data from the Host Genetics Initiative. PheWAS analysis was performed using genotype-phenotype data from the Veterans Affairs Million Veteran Program (MVP). Phenotypes were defined by International Classification of Diseases (ICD) codes mapped to clinically relevant groups using published PheWAS methods. Among 658,582 Veterans, variants associated with severe COVID-19 were tested for association across 1,559 phenotypes. Variants at the ABO locus (rs495828, rs505922) associated with the largest number of phenotypes (n rs495828 = 53 and n rs505922 = 59); strongest association with venous embolism, odds ratio (OR rs495828 1.33 (p = 1.32 x 10 –199 ), and thrombosis OR rs505922 1.33, p = 2.2 x10 -265 . Among 67 respiratory conditions tested, 11 had significant associations including MUC5B locus (rs35705950) with increased risk of idiopathic fibrosing alveolitis OR 2.83, p = 4.12 × 10 –191 ; CRHR1 (rs61667602) associated with reduced risk of pulmonary fibrosis, OR 0.84, p = 2.26× 10 –12 . The TYK2 locus (rs11085727) associated with reduced risk for autoimmune conditions, e.g., psoriasis OR 0.88, p = 6.48 x10 -23 , lupus OR 0.84, p = 3.97 x 10 –06 . PheWAS stratified by ancestry demonstrated differences in genotype-phenotype associations. LMNA (rs581342) associated with neutropenia OR 1.29 p = 4.1 x 10 –13 among Veterans of African and Hispanic ancestry but not European. Overall, we observed a shared genetic architecture between COVID-19 severity and conditions related to underlying risk factors for severe and poor COVID-19 outcomes. Differing associations between genotype-phenotype across ancestries may inform heterogenous outcomes observed with COVID-19. Divergent associations between risk for severe COVID-19 with autoimmune inflammatory conditions both respiratory and non-respiratory highlights the shared pathways and fine balance of immune host response and autoimmunity and caution required when considering treatment targets.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2013

Inadequate Exercise as a Risk Factor for Sepsis Mortality

Objective: Test whether inadequate exercise is related to sepsis mortality. Research Design and Methods: Mortality surveillance of an epidemiological cohort of 155,484 National Walkers’ and Runners’ Health Study participants residing in the United States. Deaths were monitored for an average of 11.6-years using the National Death index through December 31, 2008. Cox proportional hazard analyses were used to compare sepsis mortality (ICD-10 A40-41) to inadequate exercise (,1.07 METh/d run or walked) as measured on their baseline questionnaires. Deaths occurring within one year of the baseline survey were excluded. Results: Sepsis was the underlying cause in 54 deaths (sepsisunderlying) and a contributing cause in 184 deaths (sepsiscontributing), or 238 total sepsis-related deaths (sepsistotal). Inadequate exercise was associated with 2.24-fold increased risk for sepsisunderlying (95%CI: 1.21 to 4.07-fold, P = 0.01), 2.11-fold increased risk for sepsiscontributing (95%CI: 1.51- to 2.92-fold, P,1024 ), and 2.13-fold increased risk for sepsistotal (95%CI: 1.59- to 2.84-fold, P,1026 ) when adjusted for age, sex, race, and cohort. The risk increase did not differ significantly between runners and walkers, by sex, or by age. Sepsistotal risk was greater in diabetics (P = 1025 ), cancer survivors (P = 0.0001), and heart attack survivors (P = 0.003) and increased with waist circumference (P = 0.0004). The sepsistotal risk associated with inadequate exercise persisted when further adjusted for diabetes, prior cancer, prior heart attack and waist circumference, and when excluding deaths with cancer, or cardiovascular, respiratory, or genitourinary disease as the underlying cause. Inadequate exercise also increased sepsistotal risk in 2163 baseline diabetics (4.78-fold, 95%CI: 2.1- to 13.8-fold, P = 0.0001) when adjusted, which was significantly greater (P = 0.03) than the adjusted risk increase in non-diabetics (1.80-fold, 95%CI: 1.30- to 2.46-fold, P = 0.0006). Conclusion: Inadequate exercise is a risk factor for sepsis mortality, particular in diabetics.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

Body Mass Index at Accession and Incident Cardiometabolic Risk Factors in US Army Soldiers, 2001–2011

Individuals entering US Army service are generally young and healthy, but many are overweight, which may impact cardiometabolic risk despite physical activity and fitness requirements. This analysis examines the association between Soldiers’ BMI at accession and incident cardiometabolic risk factors (CRF) using longitudinal data from 731,014 Soldiers (17.0% female; age: 21.6 [3.9] years; BMI: 24.7 [3.8] kg/m 2 ) who were assessed at Army accession, 2001–2011. CRF were defined as incident diagnoses through 2011, by ICD-9 code, of metabolic syndrome, glucose/insulin disorder, hypertension, dyslipidemia, or overweight/obesity (in those not initially overweight/obese). Multivariable-adjusted proportional hazards models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) between BMI categories at accession and CRF. Initially underweight (BMI<18.5 kg/m 2 ) were 2.4% of Soldiers, 53.5% were normal weight (18.5-<25), 34.2% were overweight (25-<30), and 10.0% were obese (≥30). Mean age range at CRF diagnosis was 24–29 years old, with generally low CRF incidence: 228 with metabolic syndrome, 3,880 with a glucose/insulin disorder, 26,373 with hypertension, and 13,404 with dyslipidemia. Of the Soldiers who were not overweight or obese at accession, 5,361 were eventually diagnosed as overweight or obese. Relative to Soldiers who were normal weight at accession, those who were overweight or obese, respectively, had significantly higher risk of developing each CRF after multivariable adjustment (HR [95% CI]: metabolic syndrome: 4.13 [2.87–5.94], 13.36 [9.00–19.83]; glucose/insulin disorder: 1.39 [1.30–1.50], 2.76 [2.52–3.04]; hypertension: 1.85 [1.80–1.90], 3.31 [3.20–3.42]; dyslipidemia: 1.81 [1.75–1.89], 3.19 [3.04–3.35]). Risk of hypertension, dyslipidemia, and overweight/obesity in initially underweight Soldiers was 40%, 31%, and 79% lower, respectively, versus normal-weight Soldiers. BMI in early adulthood has important implications for cardiometabolic health, even within young, physically active populations.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Improving five-year survival prediction via multitask learning across HPV-related cancers

Oncology is a highly siloed field of research in which sub-disciplinary specialization has limited the amount of information shared between researchers of distinct cancer types. This can be attributed to legitimate differences in the physiology and carcinogenesis of cancers affecting distinct anatomical sites. However, underlying processes that are shared across seemingly disparate cancers probably affect prognosis. The objective of the current study is to investigate whether multitask learning improves 5-year survival cancer patient survival prediction by leveraging information across anatomically distinct HPV related cancers. Furthermore, data were obtained from the Surveillance, Epidemiology, and End Results (SEER) program database. The study cohort consisted of 29,768 primary cancer cases diagnosed in the United States between 2004 and 2015. Ten different cancer diagnoses were selected, all with a known association with HPV risk. In the analysis, the cancer diagnoses were categorized into three distinct topography groups of varying specificity. The most specific topography grouping consisted of 10 original cancer diagnoses differentiated by the first two digits of the ICD-O-3 topography code. The second topography grouping consisted of cancer diagnoses categorized into six distinct organ groups. Finally, the third topography grouping consisted of just two groups, head-neck cancers and ano-genital cancers. The tasks were to predict 5-year survival for patients within the different topography groups using 14 predictive features which were selected among descriptive variables available in the SEER database. The information from the predictive features was shared between tasks in three different ways, resulting in three distinct predictive models: 1) Information was not shared between patients assigned to different tasks (single task learning); 2) Information was shared between all patients, regardless of task (pooled model); 3) Only relevant information was shared between patients grouped to different tasks (multitask learning). Prediction performance was evaluated with Brier scores. All three models were evaluated against one another on each of the three distinct topography-defined tasks. The results showed that multitask classifiers achieved relative improvement for the majority of the scenarios studied compared to single task learning and pooled baseline methods. In this study, we have demonstrated that sharing information among anatomically distinct cancer types can lead to improved predictive survival models.

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 OSTI2018

ARIES Blend Lot Book Data Analysis

The Advanced Recovery Integrated Extraction System (ARIES) Program being executed at Los Alamos TA-55 produces unpolished plutonium oxide powder from pit disassembly in batches of material referred to as a blend lot. Each blend lot is sampled and analyzed by Los Alamos group C-AAC (Chemistry Division, Actinide Analytical Chemistry Group) for radioactive and nonradioactive constituents contained in the material. A blend lot, which nominally consists of 12 kg(Pu), is then packaged into three DOE-STD-3013 containers that are analyzed by nondestructive analysis (NDA) to determine the fraction and total amount of plutonium mass relative to impure plutonium oxide. The analytical and NDA data are presented in a Blend Lot Book (BLB). The data are used to confirm that a blend lot grouping of three cans meets the requirements for off-site shipment specified in the formal interface control document (ICD). The analytical and NDA data from 49 blend lots were reviewed to provide summary-level information on the constituents present in the blend lots. The results provide an overall perspective in historical amounts for constituent averages over all blend lots and similar information in the variation between constituent levels. Note that to properly compare the can contents, it would be most appropriate to decay all of cans’ constituents to a particular date. Previously, this analysis only examined statistical data as determined at the time of blend lot chemical analysis. The chemical analysis dates span the period from June 2011 to May 2015 (4 years), which is 28% of one Pu-241 half-life. Therefore, the approximate nature of the aggregate information must be properly understood in this light. However, to address this issue, a new table is included in this revision that decays the 48 blend lots to a single date.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Seascape Interface Control Document (V.1)

This paper serves as the Interface Control Document (ICD) for the Seascape automated test harness developed at Sandia National Laboratories. The primary purposes of the Seascape system are: (1) provide a place for accruing large, curated, labeled data sets useful for developing and evaluating detection and classification algorithms (including, but not limited to, supervised machine learning applications) (2) provide an automated structure for specifying, running and generating reports on algorithm performance. Seascape uses GitLab, Nexus, Solr, and Banana, open source codes, together with code written in the Python language, to automatically provision and configure computational nodes, queue up jobs to accomplish algorithms test runs against the stored data sets, gather the results and generate reports which are then stored in the Nexus artifact server.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Seascape Interface Control Document (V. 2)

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Seascape Interface Control Document

This paper serves as the Interface Control Document (ICD) for the Seascape automated test harness developed at Sandia National Laboratories. The primary purposes of the Seascape system are: (1) provide a place for accruing large, curated, labeled data sets useful for developing and evaluating detection and classification algorithms (including, but not limited to, supervised machine learning applications) (2) provide an automated structure for specifying, running and generating reports on algorithm performance. Seascape uses GitLab, Nexus, Solr, and Banana, open source software, together with code written in the Python language, to automatically provision and configure computational nodes, queue up jobs to accomplish algorithms test runs against the stored data sets, gather the results and generate reports which are then stored in the Nexus artifact server.

97 MATHEMATICS AND COMPUTING↗