In this paper we focus on three major task: 1) discussing our methods: Our method captures a portion of the data in DCD flowsheets, kidney perfusion data, and Flowsheet data captured peri-organ recovery surgery. 2) demonstrating the result: We built a comprehensive, analyzable database from 2022 OPTN data. This dataset is by far larger than any previously available even in this preliminary phase; and 3) proving that our methods can be extended to all the past OPTN data and future data. The scope of our study is all Organ Procurement and Transplantation Network (OPTN) data of the USA organ donors since 2008. The data was not analyzable in a large scale in the past because it was captured in PDF documents known as ``Attachments'', whereby every donor's information was recorded into dozens of PDF documents in heterogeneous formats. To make the data analyzable, one needs to convert the content inside these PDFs to an analyzable data format, such as a standard SQL database. In this paper we will focus on 2022 OPTN data, which consists of $\approx 400,000$ PDF documents spanning millions of pages. The entire OPTN data covers 15 years (2008--20022). This paper assumes that readers are familiar with the content of the OPTN data.

翻译：本文聚焦于三大主要任务：1）讨论我们的方法：该方法捕获了DCD流程表、肾脏灌注数据以及器官复苏手术期间收集的流程表数据中的部分信息；2）展示结果：我们基于2022年OPTN数据构建了一个全面且可分析的数据库，该数据集即使在初步阶段也远超以往任何可用数据；3）证明我们的方法可扩展至所有过去的OPTN数据及未来数据。研究范围涵盖自2008年以来美国器官捐献者的全部器官获取与移植网络（OPTN）数据。过去这些数据无法大规模分析，因其以名为“附件”的PDF文档形式捕获，每位捐献者的信息记录于数十种异构格式的PDF文档中。要使数据可分析，需将这些PDF中的内容转换为可分析的数据格式，例如标准SQL数据库。本文重点研究2022年OPTN数据，该数据集包含约40万份PDF文档，涉及数百万页。完整的OPTN数据涵盖15年（2008-2022年）。本文假设读者熟悉OPTN数据的内容。