High-throughput phenotyping, the automated mapping of patient signs and
symptoms to standardized ontology concepts, is essential to gaining value from
electronic health records (EHR) in the support of precision medicine. Despite
technological advances, high-throughput phenotyping remains a challenge. This
study compares three computational approaches to high-throughput phenotyping: a
Large Language Model (LLM) incorporating generative AI, a Natural Language
Processing (NLP) approach utilizing deep learning for span categorization, and
a hybrid approach combining word vectors with machine learning. The approach
that implemented GPT-4 (a Large Language Model) demonstrated superior
performance, suggesting that Large Language Models are poised to be the
preferred method for high-throughput phenotyping of physician notes.

本研究比较了高通量表型化的三种计算方法：基于生成式人工智能的大型语言模型（LLM），利用深度学习进行跨度分类的自然语言处理（NLP）方法，以及将词向量与机器学习相结合的混合方法。采用 GPT-4（一种大型语言模型）的方法表现出卓越性能，表明大型语言模型可能成为医师笔记的高通量表型化的首选方法。

一种大型语言模型优于其他计算方法对医生笔记的高通量表型

A Large Language Model Outperforms Other Computational Approaches to the  High-Throughput Phenotyping of Physician Notes

High-throughput phenotyping (HTP) of seeds, also known as seed phenotyping,
is the comprehensive assessment of complex seed traits such as growth,
development, tolerance, resistance, ecology, yield, and the measurement of
parameters that form more complex traits. One of the key aspects of seed
phenotyping is cereal yield estimation that the seed production industry relies
upon to conduct their business. While mechanized seed kernel counters are
available in the market currently, they are often priced high and sometimes
outside the range of small scale seed production firms' affordability. The
development of object tracking neural network models such as You Only Look Once
(YOLO) enables computer scientists to design algorithms that can estimate
cereal yield inexpensively. The key bottleneck with neural network models is
that they require a plethora of labelled training data before they can be put
to task. We demonstrate that the use of synthetic imagery serves as a feasible
substitute to train neural networks for object tracking that includes the tasks
of object classification and detection. Furthermore, we propose a seed kernel
counter that uses a low-cost mechanical hopper, trained YOLOv8 neural network
model, and object tracking algorithms on StrongSORT and ByteTrack to estimate
cereal yield from videos. The experiment yields a seed kernel count with an
accuracy of 95.2\% and 93.2\% for Soy and Wheat respectively using the
StrongSORT algorithm, and an accuray of 96.8\% and 92.4\% for Soy and Wheat
respectively using the ByteTrack algorithm.

使用合成图像训练神经网络模型和物体跟踪算法，开发了一种低成本的种仁计数器，能够准确估计玉米和小麦的产量。