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Automated Detection Algorithm for Mesoscale Heated Regions in TWINS Ion Temperature Maps

ABSTRACT: Abstract Earth's magnetotail plays a critical role in the dynamics of the magnetosphere, particularly during intervals of geomagnetic activity. To improve our understanding of the ion dynamics in this region, energetic neutral atom (ENA) imaging can provide global measurements to place in situ measurements in context and validate simulations. The NASA Two Wide‐angle Imaging Neutral‐atom Spectrometers mission provided near‐continuous observations using ENA imagers. ENA data can be used to calculate maps of equatorial ion temperatures that often show observations of regions of enhanced temperatures associated with phenomena in the magnetotail such as magnetic reconnection and narrow flow channels. We present an algorithm that can be used to search through a collection of these maps to identify intervals with such enhancements for further study. The algorithm results are validated against two sets of related phenomena: (a) a database of dipolarizing flux bundle (DFB) measurements from THEMIS and (b) a list of substorm onsets from SuperMAG. We demonstrate that the algorithm is very good at identifying intervals when there are DFB measurements or substorm onsets as long as there sufficient ENA data. We discuss some potential scientific studies that can result from use of the algorithm. We also show a preliminary application of the algorithm to simulation output to demonstrate the usefulness for other datasets, facilitate comparative studies, and introduce a new method for model validation. Key Points An algorithm has been developed to identify regions of increased ion temperatures in maps calculated from Two Wide‐angle Imaging Neutral‐atom Spectrometers energetic neutral atom data The algorithm is validated against a list of dipolarizing flux bundle measurements and a list of substorm onsets The algorithm will be useful for future case and statistical studies of mesoscale phenomena as well as model validation

SUBMITTER: Keesee A

PROVIDER: S-EPMC9788308 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Project description:BackgroundIn this study we implemented and developed state-of-the-art machine learning (ML) and natural language processing (NLP) technologies and built a computerized algorithm for medication reconciliation. Our specific aims are: (1) to develop a computerized algorithm for medication discrepancy detection between patients' discharge prescriptions (structured data) and medications documented in free-text clinical notes (unstructured data); and (2) to assess the performance of the algorithm on real-world medication reconciliation data.MethodsWe collected clinical notes and discharge prescription lists for all 271 patients enrolled in the Complex Care Medical Home Program at Cincinnati Children's Hospital Medical Center between 1/1/2010 and 12/31/2013. A double-annotated, gold-standard set of medication reconciliation data was created for this collection. We then developed a hybrid algorithm consisting of three processes: (1) a ML algorithm to identify medication entities from clinical notes, (2) a rule-based method to link medication names with their attributes, and (3) a NLP-based, hybrid approach to match medications with structured prescriptions in order to detect medication discrepancies. The performance was validated on the gold-standard medication reconciliation data, where precision (P), recall (R), F-value (F) and workload were assessed.ResultsThe hybrid algorithm achieved 95.0%/91.6%/93.3% of P/R/F on medication entity detection and 98.7%/99.4%/99.1% of P/R/F on attribute linkage. The medication matching achieved 92.4%/90.7%/91.5% (P/R/F) on identifying matched medications in the gold-standard and 88.6%/82.5%/85.5% (P/R/F) on discrepant medications. By combining all processes, the algorithm achieved 92.4%/90.7%/91.5% (P/R/F) and 71.5%/65.2%/68.2% (P/R/F) on identifying the matched and the discrepant medications, respectively. The error analysis on algorithm outputs identified challenges to be addressed in order to improve medication discrepancy detection.ConclusionBy leveraging ML and NLP technologies, an end-to-end, computerized algorithm achieves promising outcome in reconciling medications between clinical notes and discharge prescriptions.

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Automated Detection Algorithm for Mesoscale Heated Regions in TWINS Ion Temperature Maps

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