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Power Up: Combining Behavior Monitoring Software with Business Intelligence Tools to Enhance Proactive Animal Welfare Reporting.

ABSTRACT: Animal welfare is a dynamic process, and its evaluation must be similarly dynamic. The development of ongoing behavior monitoring programs in zoos and aquariums is a valuable tool for identifying meaningful changes in behavior and allows proactive animal management. However, analyzing observational behavior data in an ongoing manner introduces unique challenges compared with traditional hypothesis-driven studies of behavior over fixed time periods. Here, I introduce business intelligence software as a potential solution. Business intelligence software combines the ability to integrate multiple data streams with advanced analytics and robust data visualizations. As an example, I provide an overview of the Microsoft Power BI platform, a leading option in business intelligence software that is freely available. With Power BI, users can apply data cleaning and shaping in a stepwise fashion, then build dashboards using a library of visualizations through a drag-and-drop interface. I share two examples of data dashboards built with Power BI using data from the ZooMonitor behavior recording app: a quarterly behavior summary and an enrichment evaluation summary. I hope this introduction to business intelligence software and Microsoft Power BI empowers researchers and managers working in zoos and aquariums with new tools to enhance their evidence-based decision-making processes.

SUBMITTER: Wark JD

PROVIDER: S-EPMC9264768 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Purpose:Next-generation sequencing technologies are actively applied in clinical oncology. Bioinformatics pipeline analysis is an integral part of this process; however, humans cannot yet realize the full potential of the highly complex pipeline output. As a result, the decision to include a variant in the final report during routine clinical sign-out remains challenging. Methods:We used an artificial intelligence approach to capture the collective clinical sign-out experience of six board-certified molecular pathologists to build and validate a decision support tool for variant reporting. We extracted all reviewed and reported variants from our clinical database and tested several machine learning models. We used 10-fold cross-validation for our variant call prediction model, which derives a contiguous prediction score from 0 to 1 (no to yes) for clinical reporting. Results:For each of the 19,594 initial training variants, our pipeline generates approximately 500 features, which results in a matrix of > 9 million data points. From a comparison of naive Bayes, decision trees, random forests, and logistic regression models, we selected models that allow human interpretability of the prediction score. The logistic regression model demonstrated 1% false negativity and 2% false positivity. The final models' Youden indices were 0.87 and 0.77 for screening and confirmatory cutoffs, respectively. Retraining on a new assay and performance assessment in 16,123 independent variants validated our approach (Youden index, 0.93). We also derived individual pathologist-centric models (virtual consensus conference function), and a visual drill-down functionality allows assessment of how underlying features contributed to a particular score or decision branch for clinical implementation. Conclusion:Our decision support tool for variant reporting is a practically relevant artificial intelligence approach to harness the next-generation sequencing bioinformatics pipeline output when the complexity of data interpretation exceeds human capabilities.

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Power Up: Combining Behavior Monitoring Software with Business Intelligence Tools to Enhance Proactive Animal Welfare Reporting.

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