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Deriving personalized insights from popular wearable trackers requires complex numerical reasoning that challenges standard LLMs, necessitating tool-based approaches like code generation.
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MULTIVERSE: Mining Collective Data Science Knowledge from Code on the Web to Suggest Alternative Analysis Approaches
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Sf-lg: Space-filling line graphs for visualizing interrelated time-series data on smartwatches
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Effectiveness of wearable activity trackers to increase physical activity and improve health: a systematic review of systematic reviews and meta-analyses
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Large language models encode clinical knowledge
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A large language model for electronic health records
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Natural Language to Code Generation in Interactive Data Science Notebooks, December 2022
Pengcheng Yin, Wen-Ding Li, Kefan Xiao, Abhishek Rao, Yeming Wen, Kensen Shi, Joshua Howland, Paige Bailey, Michele Catasta, Henryk Michalewski, Alex Polozov, and Charles Sutton · 2022
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Kevin Zhang, Neha Patki, and Kalyan Veeramachaneni · 2022
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Sparks of artificial general intelligence: Early experiments with gpt-4, 2023
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, and Yi Zhang · 2023
Data science opportunities of large language models for neuroscience and biomedicine
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Towards a personal health large language model
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From classification to clinical insights: Towards analyzing and reasoning about mobile and behavioral health data with large language models
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Capabilities of gemini models in medicine
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