Sparse Covers for Planar Graphs and Graphs that Exclude a Fixed Minor

We consider the construction of sparse covers for planar graphs and other graphs that exclude a fixed minor. We present an algorithm that gives a cover for the γ-neighborhood of each node. For planar graphs, the cover has radius less than 16γ and degree no more than 18. For every n node graph that excludes a minor of a fixed size, we present an algorithm that yields a cover with radius no more than 4γ and degree O(logn). This is a significant improvement over previous results for planar graphs and for graphs excluding a fixed minor; in order to obtain clusters with radius O(γ), it was required to have the degree polynomial in n. Our algorithms are based on a recursive application of a basic routine called shortest-path clustering, which seems to be a novel approach to the construction of sparse covers. Since sparse covers have many applications in distributed computing, including compact routing, distributed directories, synchronizers, and Universal TSP, our improved cover construction results in improved algorithms for all these problems, for the class of graphs that exclude a fixed minor

Medical Conditions Analysis for Electronic Healthcare Records

693KA8-22-C-00001This workbook "Medical Conditions Analysis for Electronic Healthcare Records" (DOI: https://doi.org/10.21949/1528558)supplements the MITRE document "Integrating Commercial Healthcare Datasets for Aeromedical Risk Analyses" (DOI: https://doi.org/10.21949/158556).This workbook addresses eight different medical conditions that can lead to pilot incapacitation. Each page focuses on one condition and lists information that could be found in a healthcare dataset to help predict the onset of that condition through the use of a machine learning algorithm. The information listed ranges from diagnoses, tests, and vitals that are collected in electronic health records. The MITRE team has included the relevant ICD\u20109, ICD\u201010, LOINC, and/or SNOMED codes to help readily identify the information in the data, as well as the normal ranges the data should fall between (where applicable). This document was produced with the objective of aiding machine learning algorithms in medical risk forecasting for these specific conditions

Integrating Commercial Healthcare Datasets for Aeromedical Risk Analyses

693KA8-22-C-00001The Federal Aviation Administration (FAA) Office of Aerospace Medicine requires comprehensive longitudinal healthcare datasets to augment internal data for the purpose of conducting safety risk assessments to update medical standards (i.e. data dirven, risk based decision making). The Federal Aviation Administration (FAA) tasked The MITRE Corporation\u2019s Center for Advanced Aviation System Development (MITRE CAASD), in its Innovation Partner role, to identify commercial healthcare datasets that hold potential value in forecasting medical risk and are suitable for integration into the Aeromedical Data Environment. MITRE CAASD performed a market survey of existing healthcare datasets available commercially or for public use. This market survey led to the identification of over 40 healthcare data sources, many of which contain numerous subordinate sets. An initial set of screening criteria ensured that candidate data sources were sufficiently suitable for modeling objectives; this screening reduced the set to three final candidate data sources. These three data sources were compared using a set of features relevant to risk modeling of aeromedically relevant outcomes by condition. This set of comparison features included their coverage of medical conditions of interest to the FAA, as well as factors impacting integration into the aeromedical data environment

Technological Feasibility Assessment of Conducting Aeromedical Certification Exams Using Telemedicine During Public Health Emergencies

693KA8 22 C 00001, 100976.10.102.1011.TM4During the Public Health Emergency (PHE) caused by the SARS-CoV-2 virus, airmen could not schedule medical exams with Aviation Medical Examiners (AMEs), necessitating the FAA to extend the duration of medical certification for several months. This situation was undesirable because it removed one of the safeguards for ensuring human reliability in aerospace operations. Accordingly, the Federal Aviation Administration's Office of Aerospace Medicine requested MITRE evaluate the feasibility of telemedicine for aeromedical certification exams in a future PHE, with the main objective of identifying validated technologies for elements of the exam. Based on interviews with senior AMEs, a telemedicine innovation challenge, and an assessment of marketplace best practices, mature telemedicine technology was identified to accomplish 19 and partially complete 10 elements of the FAA's aeromedical certification exam. Elements that can be completed via telemedicine include medical history, height, weight, ear, nose, throat, ocular motility, lungs, heart, vascular, skin, musculoskeletal extremities, spine, identifying body marks/scars/tattoos, neurologic, psychiatric, general systemic, hearing, blood pressure, pulse. Additionally, the electrocardiogram and urinalysis testing can be accomplished remotely. Elements that can be only partially assessed in the home via telemedicine include the retina, pupils, abdomen/viscera, genitourinary, vision (distant, near, intermediate), color vision, field of vision, and heterophoria. Elements that cannot be accomplished remotely include an exam of the anus and lymphatics

Flight Crew and Air Traffic Controller Interactions When Conducting Interval Management Utilizing Voice and Controller Pilot Data Link Communications

MITRE conducted this human-in-the-loop research project on Interval Management (IM) Controller Pilot Data Link Communications (CPDLC) to investigate the integration of two advanced Next Generation Air Transportation System (NextGen) capabilities across both the air and ground domains to uncover any complications that could arise from two key capabilities that were developed separately. The simulation study included three levels of IM clearance complexity and looked at aircraft equipped only with voice communication capability and those with both voice and CPDLC. An en route air traffic environment was simulated with 50 percent of aircraft equipped with the IM capability. Results: Most pilots and controllers in the experiment deemed the IM and CPDLC to be compatible, although the controllers seemed to have more difficulty with mixed IM equipped aircraft than with mixed CPDLC equipped aircraft. Concerns were noted for use of IM with voice communications, since the data entry requirement for the flight crew was increased when CPDLC autoload into the FMS was unavailable. Not surprisingly, this was particularly the case with the most complex IM clearances. Application: The results are intended to be used by the FAA as well as EUROCAE and RTCA when developing the technical standards for the interface between the IM and CPDLC equipment. FAA Aviation Safety (AVS) sponsors who develop the regulatory and guidance material for CPDLC and ADS-B are expected to use the results in the development of Advisory Circulars (ACs) and Technical Standard Orders (TSOs) based on the international standards material. Recommendations for consideration by these groups are provided in the Conclusions and Recommendations section of the report

Pilot Medical Monitoring: State of the Science Review on Identification of Pilot Incapacitation

693KA8-22-C-00001Organizations have begun to explore the use of human physiological monitoring technologies in critical safety systems to mitigate risk and adapt to evolving operator concepts. This report describes the initial capabilities needed to support safe flight operations in the case of an incapacitated pilot. We describe aspects of a pilot\u2019s physiological state which, in the absence of a second flight deck crewmember, would instead need to be monitored through sensing technology. We review the maturity of the science of sensing technologies for incapacitation detection. Six types of incapacitation were identified to review the state of the science for incapacitation detection technology: sudden cardiac death, sleep, epileptic seizure, stroke, hypoxia, and acute pain syndrome

Pilot Medical Certification Period Health State Forecasts

DTFAWA-10-C-00080The Federal Aviation Administration (FAA) Office of Aerospace Medicine supports research to use available healthcare data to inform policies regarding pilots' medical certifications. The MITRE Corporation's Center for Advanced System Development (MITRE CAASD) was asked to examine methods in advanced data analytics and machine learning (ML) to inform such risk-based decision-making. As an initial step in assessing the potential predictive value of commercially available healthcare data, the FAA provided MITRE CAASD the IBM MarketScan dataset\u2014a large set of commercial healthcare claims records. Using this dataset, we developed methods for identifying health status and changes in health status across conditions; for measuring changes in health status among enrollees with diabetes mellitus (DM); and for measuring the onset of new cases of DM, traumatic brain injury, sleep apnea, and chronic obstructive pulmonary disease. We developed a repeatable workflow and modeled these conditions using a wide range of ML methods. We conclude that ML-based predictive modeling of health conditions from IBM MarketScan data is feasible and informative. However, additional clinical information from commercially available electronic health records would likely improve accuracy and more closely align with future FAA needs

MITRE collection, AC 31, TX-0 and TX-2 Computer Memoranda, finding aid (MIT series 14)

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MITRE collection, AC 9, Group Leader's Meetings Minutes, finding aid (MIT series 8)

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MITRE collection, AC 19, Transistor Studies Memoranda and Notes, finding aid (MIT series 6)

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