Dataset Information

Automated Algorithm for Detection of Transient Adenosine Release.

ABSTRACT: Spontaneous adenosine release events have been discovered in the brain that last only a few seconds. The identification of these adenosine events from fast-scan cyclic voltammetry (FSCV) data is difficult due to the random nature of adenosine release. In this study, we develop an algorithm that automatically identifies and characterizes adenosine transient features, including event time, concentration, and duration. Automating the data analysis reduces analysis time from 10 to 18 h to about 40 min per experiment. The algorithm identifies adenosine based on its two oxidation peaks, the time delay between them, and their current vs time peak ratios. In order to validate the program, four data sets from three independent researchers were analyzed by the algorithm and then compared to manual identification by an analyst. The algorithm resulted in 10 ± 4% false negatives and 9 ± 3% false positives. The specificity of the algorithm was verified by comparing calibration data for adenosine triphosphate (ATP), histamine, hydrogen peroxide, and pH changes and these analytes were not identified as adenosine. Stimulated histamine release in vivo was also not identified as adenosine. The code is modular in design and could be easily adjusted to detect features of spontaneous dopamine or other neurochemical transients in FSCV data.

SUBMITTER: Borman RP

PROVIDER: S-EPMC5312768 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Automated Algorithm for Detection of Transient Adenosine Release.

Borman Ryan P RP Wang Ying Y Nguyen Michael D MD Ganesana Mallikarjunarao M Lee Scott T ST Venton B Jill BJ

ACS chemical neuroscience 20161208 2

Spontaneous adenosine release events have been discovered in the brain that last only a few seconds. The identification of these adenosine events from fast-scan cyclic voltammetry (FSCV) data is difficult due to the random nature of adenosine release. In this study, we develop an algorithm that automatically identifies and characterizes adenosine transient features, including event time, concentration, and duration. Automating the data analysis reduces analysis time from 10 to 18 h to about 40 m ...[more]

PMID: 28196418

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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.

Dataset Information

Automated Algorithm for Detection of Transient Adenosine Release.

Publications

Automated Algorithm for Detection of Transient Adenosine Release.

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