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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Uncertainty Considerations for Ballistic Limit Equations

The overall risk for any spacecraft system is typically determined using a Probabilistic Risk Assessment (PRA). A PRA attempts to determine the overall risk associated with a particular mission by factoring in all known risks (and their corresponding uncertainties, if known) to the spacecraft during its mission. The threat to mission and human life posed by the mircro-meteoroid & orbital debris (MMOD) environment is one of the risks. NASA uses the BUMPER II program to provide point estimate predictions of MMOD risk for the Space Shuttle and the International Space Station. However, BUMPER II does not provide uncertainty bounds or confidence intervals for its predictions. With so many uncertainties believed to be present in the models used within BUMPER II, providing uncertainty bounds with BUMPER II results would appear to be essential to properly evaluating its predictions of MMOD risk. The uncertainties in BUMPER II come primarily from three areas: damage prediction/ballistic limit equations, environment models, and failure criteria definitions. In order to quantify the overall uncertainty bounds on MMOD risk predictions, the uncertainties in these three areas must be identified. In this paper, possible approaches through which uncertainty bounds can be developed for the various damage prediction and ballistic limit equations encoded within the shuttle and station versions of BUMPER II are presented and discussed. We begin the paper with a review of the current approaches used by NASA to perform a PRA for the Space Shuttle and the International Space Station, followed by a review of the results of a recent sensitivity analysis performed by NASA using the shuttle version of the BUMPER II code. Following a discussion of the various equations that are encoded in BUMPER II, we propose several possible approaches for establishing uncertainty bounds for the equations within BUMPER II. We conclude with an evaluation of these approaches and present a recommendation regarding which of them would be the most appropriate to follow.

Schonberg, W. P.↗
NASA NTRS2006

Radiation-Induced Leukemia at Doses Relevant to Radiation Therapy: Modeling Mechanisms and Estimating Risks

Because many cancer patients are diagnosed earlier and live longer than in the past, second cancers induced by radiation therapy have become a clinically significant issue. An earlier biologically based model that was designed to estimate risks of high-dose radiation induced solid cancers included initiation of stem cells to a premalignant state, inactivation of stem cells at high radiation doses, and proliferation of stem cells during cellular repopulation after inactivation. This earlier model predicted the risks of solid tumors induced by radiation therapy but overestimated the corresponding leukemia risks. Methods: To extend the model to radiation-induced leukemias, we analyzed in addition to cellular initiation, inactivation, and proliferation a repopulation mechanism specific to the hematopoietic system: long-range migration through the blood stream of hematopoietic stem cells (HSCs) from distant locations. Parameters for the model were derived from HSC biologic data in the literature and from leukemia risks among atomic bomb survivors v^ ho were subjected to much lower radiation doses. Results: Proliferating HSCs that migrate from sites distant from the high-dose region include few preleukemic HSCs, thus decreasing the high-dose leukemia risk. The extended model for leukemia provides risk estimates that are consistent with epidemiologic data for leukemia risk associated with radiation therapy over a wide dose range. For example, when applied to an earlier case-control study of 110000 women undergoing radiotherapy for uterine cancer, the model predicted an excess relative risk (ERR) of 1.9 for leukemia among women who received a large inhomogeneous fractionated external beam dose to the bone marrow (mean = 14.9 Gy), consistent with the measured ERR (2.0, 95% confidence interval [CI] = 0.2 to 6.4; from 3.6 cases expected and 11 cases observed). As a corresponding example for brachytherapy, the predicted ERR of 0.80 among women who received an inhomogeneous low-dose-rate dose to the bone marrow (mean = 2.5 Gy) was consistent with the measured ERR (0.62, 95% Cl =-0.2 to 1.9). Conclusions: An extended, biologically based model for leukemia that includes HSC initiation, inactivation, proliferation, and, uniquely for leukemia, long-range HSC migration predicts, %Kith reasonable accuracy, risks for radiationinduced leukemia associated with exposure to therapeutic doses of radiation.

Shuryak, Igor↗
NASA NTRS1992

Method for improved prediction of bone fracture risk using bone mineral density in structural analysis

A non-invasive in-vivo method of analyzing a bone for fracture risk includes obtaining data from the bone such as by computed tomography or projection imaging which data represents a measure of bone material characteristics such as bone mineral density. The distribution of the bone material characteristics is used to generate a finite element method (FEM) mesh from which load capability of the bone can be determined. In determining load capability, the bone is mathematically compressed, and stress, strain force, force/area versus bone material characteristics are determined.

Cann, Christopher E.↗
DOE OSTI2022

Progress Towards a Predictive Eagle Behavior and Risk Modeling Framework: Overview and Recent Validation Efforts

This presentation summarizes progress to date of the U.S. Department of Energy project, "Development of a computational framework for modeling golden eagles (Aquila chrysaetos) near wind farms," which focuses on stochastic behavioral modeling of soaring raptors across landscape, facility, and turbine spatiotemporal scales. This publicly available, open-source modeling framework includes behavioral models based on three different underlying principles: energy minimization at landscape scale, behavioral heuristics at landscape-facility scale, and data-driven behaviors at the facility-micro-scale. We will briefly overview the key advancements in the behavioral modeling state of the art, which leverages multiple high-resolution telemetry data sources combined with high-fidelity atmospheric flow modeling insights. We then present preliminary results from a validation study in Altamont, California. This new study involves a novel application of the Stochastic Soaring Raptor Simulator (SSRS), in a new geographic locale, to understand facility scale eagle movement patterns over time scales representative of a wind project's lifetime. For this desktop analysis (that does not depend on any high-performance computing resources), SSRS simultaneously considers a variety of wind conditions and eagle approach vectors toward a project site of interest. This work demonstrates the integration of publicly available landscape-scale atmospheric datasets, our recently improved engineering updraft models (see presentation from Thedin et al.), and our energy minimization behavioral models within the SSRS framework. While we only present results from a single behavioral model, the integration of these three modeling components forms the foundation for our more sophisticated behavioral models (see presentations from Brandes et al., Sandhu et al.) that are under active development. Results are presented in the form of presence maps, which may be applied to estimate risk to wildlife, augment ground survey data, inform wind-plant operations, or incorporated into wind-plant designs.

agent-based modeling↗
NASA NTRS2016

Pitfalls and Precautions When Using Predicted Failure Data for Quantitative Analysis of Safety Risk for Human Rated Launch Vehicles

Launch vehicle reliability analysis is largely dependent upon using predicted failure rates from data sources such as MIL-HDBK-217F. Reliability prediction methodologies based on component data do not take into account risks attributable to manufacturing, assembly, and process controls. These sources often dominate component level reliability or risk of failure probability. While consequences of failure is often understood in assessing risk, using predicted values in a risk model to estimate the probability of occurrence will likely underestimate the risk. Managers and decision makers often use the probability of occurrence in determining whether to accept the risk or require a design modification. Due to the absence of system level test and operational data inherent in aerospace applications, the actual risk threshold for acceptance may not be appropriately characterized for decision making purposes. This paper will establish a method and approach to identify the pitfalls and precautions of accepting risk based solely upon predicted failure data. This approach will provide a set of guidelines that may be useful to arrive at a more realistic quantification of risk prior to acceptance by a program.

Hatfield, Glen S.↗
NASA NTRSDate not supplied

Performance Risk Model Validation with Operationally Relevant Tasks

Human Research Program aims to develop methods to support astronauts’ health and productivity during spaceflight. The Crew Health and Performance Probabilistic Risk Assessment (CHP-PRA) team uses powerful computational methods to predict mission risk in both domains: medical and performance. Here, we show how CHP-PRA uses the Performance Risk Model (PRisM) to quantify the performance risk and show an application of the model on operationally relevant tasks. There are various metrics adopted across performance researchers that PRisM can accommodate. For data analysis, interpretation, and integration, we use a method of unifying data from multiple sources by converting each to a single metric. We consult subject matter experts prior to integrating the converted data into PRisM. The method we use is inspired by the Cooper-Harper rating scale [1]. Using this unified metric, we can easily combine data from various tests and lab groups. We explain our conversion method in detail and show how it pertains to the process of testing and validation of PRisM on operational tasks. We conducted an initial validation in collaboration with the Behavioral Health and Performance (BHP) lab. We test PRisM using data on their operationally relevant task ROBoT-r, a track-and-capture task for grappling incoming resupply vehicles [2]. Several other labs at NASA Johnson Space Center worked together to design 7 Functional Task Tests (FTTs) in pursuit of simulating the tasks required after landing on a planetary surface and after return to Earth [3]. Here we use the results from both ROBoT-r and the 7 FTTs and compare their experiment data to PRisM’s computational output to demonstrate how PRisM can support operations by predicting crew performance on future missions.

performance modeling↗
NASA NTRS2019

Scaling Evaluation of Ice-Crystal Icing on a Modern Turbofan Engine in PSL Using the COMDES-MELT Code

This paper presents preliminary ice-crystal icing (ICI) altitude scaling evaluation results of a Honeywell Uncertified Research Engine (HURE) that was tested in the NASA Glenn Research Center Propulsion Systems Laboratory (PSL) during January of 2018. This engine geometry features a hidden core design to keep the core less exposed. The engine was fitted with internal video cameras to observe various ice buildup processes at multiple selected locations within the engine core flow path covering the fan stator, the splitter-lip/shroud/strut, and the high pressure compressor (HPC) variable inlet guide vane (IGV) regions. The potential ice accretion risk was pre-determined to occur by using NASA’s in-house 1D Engine Icing Risk assessment code, COMDES-MELT. The code was successful in predicting the risk of ice accretion in adiabatic regions like the fan-stator of the HURE at specific engine operating points. However at several operating points during the test, liquid water was observed running along the shroud toward the variable IGV of the HPC regions with an air temperature well below freezing, thus no particle melting could have occurred due to heating from the air alone. It was reasoned that other sources of heat were present in that region. To account for these heat sources the inlet total temperature was adjusted to give a wet bulb temperature of 24 F below the standard minimum wet bulb temperature of 492 R to allow ice to accrete in the splitter-lip/shroud/strut region, which was determined from a reference case where hard ice was observed in that region. With that adjustment the COMDES-MELT code was successful in providing operating points where there was a risk of ice accretion during the test campaign. In addition to calculating possible conditions at different selected lower altitudes, simulations were run to determine potential inlet conditions that could lead to ice-crystal accretion along the prescribed stations where the cameras were available. From there, scaled test conditions were determined by best matching the following three icing related parameters of the reference condition: (1) the local air total wet bulb temperature, (2) the local ice crystal cloud melt ratio and (3) the engine fan face ice/water to air mass flux ratio of the ice crystal cloud. Instantaneous images taken from the time-lapsed movies of ice buildup were used along with the relevant thermodynamic data of air, water vapor and local icing condition to help evaluate how closely the proposed altitude scaling method could be used in ground based test facility to duplicate selected reference ICI features observed at specific location inside this engine at different scale altitudes. Discussions on observed limitation for engine icing scaling application from this test campaign and needed improvement are provided. A scaling test procedure to help identify potential ICI risk conditions and possible ice accretion locations of a new turbofan engine is evaluated in PSL.

Engine Testing in PSL↗
NASA NTRSDate not supplied

Analysis and Prediction of VFR Vs IFR Traffic Behavior to Support Uncrewed Aircraft Flight Operations at Regional Airports

Uncrewed Aircraft flight operations at a regional airport will be affected by the uncertainty in the Visual Flight Rules traffic around. This paper analyzes Visual Flight Rules traffic behavior, compares it with Instrument Flight Rules traffic and develops traffic prediction methods to support uncrewed aircraft flight operations. The spatio-temporal distribution of traffic operating under Visual Flight Rules and Instrument Flight Rules was analyzed from one month of historical track data around Fort Worth Alliance airport as a representative regional airport. The traffic behavior was visualized as occupancy maps generated at different altitudes. The Instrument Flight Rules traffic was concentrated in fewer regions of the airspace along structured routes where the risk of interacting with one or more flights reached close to fifty percent in some areas. Visual Flight Rules traffic was spread over more regions, mostly segregated from the Instrument Flight Rules regions, and the risk of interaction was lower reaching up to twenty-five percent on average over the month in some regions. The interaction risk was predicted using predictive occupancy maps over multiple time horizons and conditioned on time and partial observation of traffic in the vicinity. The month-to-month predictability of Visual Flight Rules risk was lower than that of the Instrument Flight Rules traffic, consistently over all the conditions analyzed. However, the prediction and its accuracy were demonstrated to be sensitive to the conditions used. The predictive models generated can be used to support both strategic planning and in-flight decision-making by uncrewed aircraft during flight operations while maintaining an acceptable risk of interacting with other traffic.

VFR↗
NASA NTRSDate not supplied

Analysis and Prediction of VFR Vs IFR Traffic Behavior to Support Uncrewed Aircraft Flight Operations at Regional Airports

Uncrewed Aircraft flight operations at a regional airport will be affected by the uncertainty in the Visual Flight Rules traffic around. This paper analyzes Visual Flight Rules traffic behavior, compares it with Instrument Flight Rules traffic and develops traffic prediction methods to support uncrewed aircraft flight operations. The spatio-temporal distribution of traffic operating under Visual Flight Rules and Instrument Flight Rules was analyzed from one month of historical track data around Fort Worth Alliance airport as a representative regional airport. The traffic behavior was visualized as occupancy maps generated at different altitudes. The Instrument Flight Rules traffic was concentrated in fewer regions of the airspace along structured routes where the risk of interacting with one or more flights reached close to fifty percent in some areas. Visual Flight Rules traffic was spread over more regions, mostly segregated from the Instrument Flight Rules regions, and the risk of interaction was lower reaching up to twenty-five percent on average over the month in some regions. The interaction risk was predicted using predictive occupancy maps over multiple time horizons and conditioned on time and partial observation of traffic in the vicinity. The month-to-month predictability of Visual Flight Rules risk was lower than that of the Instrument Flight Rules traffic, consistently over all the conditions analyzed. However, the prediction and its accuracy were demonstrated to be sensitive to the conditions used. The predictive models generated can be used to support both strategic planning and in-flight decision-making by uncrewed aircraft during flight operations while maintaining an acceptable risk of interacting with other traffic.

VFR↗
NASA NTRS2017

Modeling Oxygen Prebreathe Protocols for Exploration Extravehicular Activities Using Variable Pressure Suits

Exploration missions are expected to use variable pressure extravehicular activity (EVA) spacesuits as well as a spacecraft "exploration atmosphere" of 56.5 kPa (8.2 psia), 34% O2, both of which provide the possibility of reducing the oxygen prebreathe times necessary to reduce decompression sickness (DCS) risk. Previous modeling work predicted 8.4% DCS risk for an EVA beginning at the exploration atmosphere, followed by 15 minutes of in-suit O2 prebreathe, and 6 hours of EVA at 29.6 kPa (4.3 psia). In this study we model notional prebreathe protocols for a variable pressure suit where the exploration atmosphere is unavailable.

Abercromby, Andrew F. J.↗
NASA NTRS2000

End-To-End Risk Assesment: From Genes and Protein to Acceptable Radiation Risks for Mars Exploration

The human exploration of Mars will impose unavoidable health risks from galactic cosmic rays (GCR) and possibly solar particle events (SPE). It is the goal of NASA's Space Radiation Health Program to develop the capability to predict health risks with significant accuracy to ensure that risks are well below acceptable levels and to allow for mitigation approaches to be effective at reasonable costs. End-to-End risk assessment is the approach being followed to understand proton and heavy ion damage at the molecular, cellular, and tissue levels in order to predict the probability of the major health risk including cancer, neurological disorders, hereditary effects, cataracts, and acute radiation sickness and to develop countermeasures for mitigating risks.

Cucinotta, Francis A.↗
NASA NTRSDate not supplied

Prediction of Calcium Oxalate Stone Risk with Interventions

Introduction Mitigations intended to reduce bone mineral loss in-flight also influence the level of renal stone risk to astronauts. Extending a recent study completed by our group [1], we investigate the likely influence of combined fluid intake and Ca excretion mitigation strategies. Methods A computational biochemistry model representing CaOx crystal precipitation, growth, and agglomeration [2] is combined with a probabilistic analysis to predict the in-flight CaOx renal stone incidence risk ratio (IRR) relative to pre-flight values using 1517 astronaut 24-hour urine chemistries. The output of this simulation is used to assess the combined impact of controlling fluid intake and Ca excretion on maintaining IRR < 1.2 for > 95% of the population, a threshold determined from initial analysis of the model output compared to referent populations [1]. Results and Conclusions Figure 1 illustrates the simulated in-flight astronaut population (n=40000) with CaOx IRR > 1.2. The red lines indicate the known clinical risk boundary of each factor, and the white box is considered the area of high combined clinical risk. The simulation and identified risk threshold conform well to the expected clinical risk boundaries. The results, assuming insensible water losses equal or exceed 0.7 L/day, imply mitigations achieve risks similar to pre-flight levels when either Ca < 150 mg/day or fluid volume intake > 3.2 L/day individually, or combined Ca < 200 and fluid volume intake > 2.5 L/day. We continue to investigate similar compounded effects and look to apprise engineering trades by informing relative risk changes during mission design. References [1] D. A. Goodenow-Messman, S. A. Gokoglu, M. Kassemi, and J. Myers, Jerry G., “Characterization of Astronaut CaOx Renal Stone Incidence Rates to Quantify In-flight and Post-flight Relative Risk,” npj Microgravity, in review, 2021. [2] M. Kassemi and D. Thompson, “Prediction of renal crystalline size distributions in space using a PBE analytic model. 1. Effect of microgravity-induced biochemical alterations,” Am. J. Physiol. Physiol., vol. 311, no. 3, pp. F520–F530, Sep. 2016, doi: 10.1152/ajprenal.00401.2015.

Debra A Goodenow↗
NASA NTRSDate not supplied

Quantifying radiation quality for space relevant radiation types: Fitting excess risk models to outbred mice data

Accurately quantifying the differences in radiation quality between space and terrestrial environments is important for predicting health risks for astronauts.Recently, Edmundson et al. 2020[1] provided valuable new results from out-bred mice linking tumor induction and genetic background aer exposure to low and high-LET radiation. The goal of the current study is to more rigorously estimate a relative biological effectiveness (RBE) factor by leveraging the solid tumor data from Edmundson et al. 2020. Excess relative risk (ERR) models and excess absolute risk (EAR) models were fit using Poisson regression similar to the models that the Radiation Effects Research Foundation uses to fit atomic bomb survivor data. Linear ERR and EAR slopes were simulated usingBayesian analyses, and RBE values were calculated from the ratio of the heavy ion linear slope to the gamma linear slope using the full posterior distribution.

Lori J. Chappell↗
NASA NTRS2008

Overview of the NASA Subsonic Rotary Wing Aeronautics Research Program in Rotorcraft Crashworthiness

This paper provides an overview of rotorcraft crashworthiness research being conducted at NASA Langley Research Center under sponsorship of the Subsonic Rotary Wing (SRW) Aeronautics Program. The research is focused in two areas: development of an externally deployable energy attenuating concept and improved prediction of rotorcraft crashworthiness. The deployable energy absorber (DEA) is a composite honeycomb structure, with a unique flexible hinge design that allows the honeycomb to be packaged and remain flat until needed for deployment. The capabilities of the DEA have been demonstrated through component crush tests and vertical drop tests of a retrofitted fuselage section onto different surfaces or terrain. The research on improved prediction of rotorcraft crashworthiness is focused in several areas including simulating occupant responses and injury risk assessment, predicting multi-terrain impact, and utilizing probabilistic analysis methods. A final task is to perform a system-integrated simulation of a full-scale helicopter crash test onto a rigid surface. A brief description of each research task is provided along with a summary of recent accomplishments.

Jackson, Karen E.↗
NASA NTRS2009

Overview of the NASA Subsonic Rotary Wing Aeronautics Research Program in Rotorcraft Crashworthiness

This paper provides an overview of rotorcraft crashworthiness research being conducted at NASA Langley Research Center under sponsorship of the Subsonic Rotary Wing (SRW) Aeronautics Program. The research is focused in two areas: development of an externally deployable energy attenuating concept and improved prediction of rotorcraft crashworthiness. The deployable energy absorber (DEA) is a composite honeycomb structure, with a unique flexible hinge design that allows the honeycomb to be packaged and remain flat until needed for deployment. The capabilities of the DEA have been demonstrated through component crush tests and vertical drop tests of a retrofitted fuselage section onto different surfaces or terrain. The research on improved prediction of rotorcraft crashworthiness is focused in several areas including simulating occupant responses and injury risk assessment, predicting multi-terrain impact, and utilizing probabilistic analysis methods. A final task is to perform a system-integrated simulation of a full-scale helicopter crash test onto a rigid surface. A brief description of each research task is provided along with a summary of recent accomplishments.

Jackson, Karen E.↗
DOE OSTI2020

Host phylogeny matters: Examining sources of variation in infection risk by blood parasites across a tropical montane bird community in India

Abstract Background Identifying patterns and drivers of infection risk among host communities is crucial to elucidate disease dynamics and predict infectious disease risk in wildlife populations. Blood parasites of the genera Plasmodium and Haemoproteus are a diverse group of vector-borne protozoan parasites that affect bird populations globally. Despite their widespread distribution and exceptional diversity, factors underlying haemosporidian infection risk in wild bird communities remain poorly understood. While some studies have examined variation in avian haemosporidian risk, researchers have primarily focused on host ecological traits without considering host phylogenetic relationships. In this study, we employ a phylogenetically informed approach to examine the association between host ecological traits and haemosporidian infection risk in endemic bird communities in the Western Ghats Sky Islands. Methods We used parasite sequence data based on partial mitochondrial cytochrome b gene, that was amplified from genomic DNA extracted from 1177 birds (28 species) across the Western Ghats to assess infection of birds with haemosporidian parasites. We employed a Bayesian phylogenetic mixed effect modelling approach to test whether haemosporidian infection risk was affected by seven species-specific and four individual-level ecological predictors. We also examined the effect of host phylogenetic relationships on the observed patterns of variation in haemosporidian infection risk by estimating phylogenetic signal. Results Our study shows that host ecological traits and host phylogeny differentially influence infection risk by Plasmodium (generalist parasite) and Haemoproteus (specialist parasite). For Plasmodium , we found that sociality, sexual dimorphism and foraging strata were important ecological predictors. For Haemoproteus , patterns of infection risk among host species were associated with sociality, species elevation and individual body condition. Interestingly, variance in infection risk explained by host phylogeny was higher for Haemoproteus parasites compared to Plasmodium . Conclusions Our study highlights that while host ecological traits promoting parasite exposure and host susceptibility are important determinants of infection risk, host phylogeny also contributes substantially to predicting patterns of haemosporidian infection risk in multi-host communities. Importantly, infection risk is driven by joint contributions of host ecology and host phylogeny and studying these effects together could increase our ability to better understand the drivers of infection risk and predict future disease threats. Graphical abstract

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2019

Space Program Advocacy Can Distort Project Management and Damage Systems Engineering

Over-optimistic project advocacy often causes exaggerated performance claims and underestimated costs and schedules. This can distort project management and damage systems engineering. NASA projects such as the space shuttle and Hubble are extreme examples. NASA's spectacular success in the Apollo moon landings seems to have produced overconfidence and carelessness, but also to have gained tolerance for unrealistic claims and forgiveness when they were proven wrong. Apollo risk analysis predicted many astronaut fatalities. This was believed but was potentially damaging to the Apollo program, so risk analysis was discontinued. The moon landings beat bad odds because Apollo obsessively reduced risk. Its success seemed to confirm that risk analysis was unreasonably pessimistic and that risk could be overcome by good engineering. This understanding caused risk to be increased during space shuttle engineering and led to an unnecessarily dangerous approach. The shuttle design placed a fragile spacecraft next to the fuel tanks and failed to provide crew escape or launch abort. These design decisions directly caused the Challenger and Columbia tragedies. After Challenger, risk analysis was re-established. The current rocket and capsule design does consider risk and the result strongly resembles Apollo. Apollo advocacy led NASA to abandon risk analysis and this was ultimate cause of the Shuttle tragedies. Excessive advocacy that distorts risk, cost, and schedule could be prevented in an ideal organization that used systems engineering to make rational and fair decisions. However, most real organizations accommodate human and group needs using informal methods often described as "the system." Humans have biases, use innate decision making heuristics, instinctively rely on "gut feel," and establish deviant groups through groupthink. Expecting organizations to become totally rational is impractical, but specific problems such as neglecting risk and underestimating cost and schedule can be directly challenged with some hope of success.

Jones, Harry W.↗
DOE OSTI2023

Using electronic health record metadata to predict housing instability amongst veterans

Housing instability is considered a significant life stressor and preemptive screening should be applied to identify those at risk for homelessness as early as possible so that they can be targeted for specialized care. We developed models to classify patient outcomes for an established VA Homelessness Screening Clinical Reminder (HSCR), which identifies housing instability, in the two months prior to its administration. Logistic Regression and Random Forest models were fit to classify responses using the last 18 months of document activity. We measure concentration of risk across stratifications of predicted probability and observe an enriched likelihood of finding confirmed false negative responses from veterans with diagnosed housing instability. Positive responses were 34 times more likely to be detected within the top 1 % of patients predicted at risk than from those randomly selected. There is a 1 in 4 chance of detecting false negatives within the top 1 % of predicted risk. Machine learning methods can classify between episodes of housing instability using a data-driven approach that does not rely on variables curated from domain experts. This method has the potential to improve clinicians’ ability to identify veterans who are experiencing housing instability but are not captured by HSCR.

60 APPLIED LIFE SCIENCES↗