The availability of both structured and unstructured databases, such as
electronic health data, social media data, patent data, and surveys that are
often updated in real time, among others, has grown rapidly over the past
decade. With this expansion, the statistical and methodological questions
around data integration, or rather merging multiple data sources, has also
grown. Specifically, the science of the ``data cleaning pipeline'' contains
four stages that allow an analyst to perform downstream tasks, predictive
analyses, or statistical analyses on ``cleaned data.'' This article provides a
review of this emerging field, introducing technical terminology and commonly
used methods.

通过介绍技术术语和常用方法，综述了数据集成和数据清洗流程中的四个阶段，探讨了在现有的结构化和非结构化数据库中进行预测分析或统计分析所涉及的统计和方法学问题。

数据清洗流程初探

A Primer on the Data Cleaning Pipeline

Liver infection is a common disease, which poses a great threat to human
health, but there is still able to identify an optimal technique that can be
used on large-level screening. This paper deals with ML algorithms using
different data sets and predictive analyses. Therefore, machine ML can be
utilized in different diseases for integrating a piece of pattern for
visualization. This paper deals with various machine learning algorithms on
different liver illness datasets to evaluate the analytical performance using
different types of parameters and optimization techniques. The selected
classification algorithms analyze the difference in results and find out the
most excellent categorization models for liver disease. Machine learning
optimization is the procedure of modifying hyperparameters in arrange to employ
one of the optimization approaches to minimise the cost function. To set the
hyperparameter, include a number of Phosphotase,Direct Billirubin, Protiens,
Albumin and Albumin Globulin. Since it describes the difference linking the
predictable parameter's true importance and the model's prediction, it is
crucial to minimise the cost function.

ML algorithms and machine learning optimization are used to evaluate different liver illness datasets and identify the most excellent categorization models by modifying hyperparameters such as Phosphotase, Direct Billirubin, Protiens, Albumin, and Albumin Globulin, essential for minimizing the cost function and visualizing the pattern.

利用机器学习进行肝部感染预测分析，以评估神经网络的分析性能和优化技术

Liver Infection Prediction Analysis using Machine Learning to Evaluate  Analytical Performance in Neural Networks by Optimization Techniques

Unstructured data, especially text, continues to grow rapidly in various
domains. In particular, in the financial sphere, there is a wealth of
accumulated unstructured financial data, such as the textual disclosure
documents that companies submit on a regular basis to regulatory agencies, such
as the Securities and Exchange Commission (SEC). These documents are typically
very long and tend to contain valuable soft information about a company's
performance. It is therefore of great interest to learn predictive models from
these long textual documents, especially for forecasting numerical key
performance indicators (KPIs). Whereas there has been a great progress in
pre-trained language models (LMs) that learn from tremendously large corpora of
textual data, they still struggle in terms of effective representations for
long documents. Our work fills this critical need, namely how to develop better
models to extract useful information from long textual documents and learn
effective features that can leverage the soft financial and risk information
for text regression (prediction) tasks. In this paper, we propose and implement
a deep learning framework that splits long documents into chunks and utilizes
pre-trained LMs to process and aggregate the chunks into vector
representations, followed by self-attention to extract valuable document-level
features. We evaluate our model on a collection of 10-K public disclosure
reports from US banks, and another dataset of reports submitted by US
companies. Overall, our framework outperforms strong baseline methods for
textual modeling as well as a baseline regression model using only numerical
data. Our work provides better insights into how utilizing pre-trained
domain-specific and fine-tuned long-input LMs in representing long documents
can improve the quality of representation of textual data, and therefore, help
in improving predictive analyses.

我们提出并实现了一个利用预训练语言模型处理长文本文档的深度学习框架，将长文档分成块，并利用自注意力机制提取有价值的文档级特征，该框架在美国银行的 10-K 公开披露报告和其他美国公司提交的报告数据集上表现出色，是文本建模和回归模型的更好选择，利用预训练的领域特定和微调的模型在表示文本数据方面的效果较好，从而有助于提高预测分析的质量。