The development of effective machine learning methodologies for enhancing the
efficiency and accuracy of clinical systems is crucial. Despite significant
research efforts, managing a plethora of diversified clinical tasks and
adapting to emerging new tasks remain significant challenges. This paper
presents a novel paradigm that employs a pre-trained large language model as a
universal clinical multi-task decoder. This approach leverages the flexibility
and diversity of language expressions to handle task topic variations and
associated arguments. The introduction of a new task simply requires the
addition of a new instruction template. We validate this framework across
hundreds of tasks, demonstrating its robustness in facilitating multi-task
predictions, performing on par with traditional multi-task learning and
single-task learning approaches. Moreover, it shows exceptional adaptability to
new tasks, with impressive zero-shot performance in some instances and superior
data efficiency in few-shot scenarios. This novel approach offers a unified
solution to manage a wide array of new and emerging tasks in clinical
applications.

该论文介绍了一种利用预训练的大型语言模型作为通用临床多任务解码器的新范例，用于管理和适应多样化的临床任务及新任务的挑战，并验证了该框架在数百个任务上的鲁棒性，表现出与传统多任务学习和单任务学习方法相当的性能，对新任务具有出色的零样本表现和很强的数据效率，为临床应用中的各种新任务提供了统一的解决方案。

大型语言模型作为通用临床多任务解码器

Large Language Model as a Universal Clinical Multi-task Decoder

In the relentless efforts in enhancing medical diagnostics, the integration
of state-of-the-art machine learning methodologies has emerged as a promising
research area. In molecular biology, there has been an explosion of data
generated from multi-omics sequencing. The advent sequencing equipment can
provide large number of complicated measurements per one experiment. Therefore,
traditional statistical methods face challenging tasks when dealing with such
high dimensional data. However, most of the information contained in these
datasets is redundant or unrelated and can be effectively reduced to
significantly fewer variables without losing much information. Dimensionality
reduction techniques are mathematical procedures that allow for this reduction;
they have largely been developed through statistics and machine learning
disciplines. The other challenge in medical datasets is having an imbalanced
number of samples in the classes, which leads to biased results in machine
learning models. This study, focused on tackling these challenges in a neural
network that incorporates autoencoder to extract latent space of the features,
and Generative Adversarial Networks (GAN) to generate synthetic samples. Latent
space is the reduced dimensional space that captures the meaningful features of
the original data. Our model starts with feature selection to select the
discriminative features before feeding them to the neural network. Then, the
model predicts the outcome of cancer for different datasets. The proposed model
outperformed other existing models by scoring accuracy of 95.09% for bladder
cancer dataset and 88.82% for the breast cancer dataset.

该研究使用神经网络结合自编码器提取特征的潜在空间，并利用生成对抗网络 GAN 生成合成样本，以解决医学数据集中的样本不平衡和高维数据降维的挑战，并在膀胱癌数据集和乳腺癌数据集上分别取得了 95.09% 和 88.82% 的准确度。

使用自编码器和生成对抗网络的方法处理多组学数据的不平衡类别和分类

An Autoencoder and Generative Adversarial Networks Approach for  Multi-Omics Data Imbalanced Class Handling and Classification

Technical debt refers to the consequences of sub-optimal decisions made
during software development that prioritize short-term benefits over long-term
maintainability. Self-Admitted Technical Debt (SATD) is a specific form of
technical debt, explicitly documented by developers within software artifacts
such as source code comments and commit messages. As SATD can hinder software
development and maintenance, it is crucial to address and prioritize it
effectively. However, current methodologies lack the ability to automatically
estimate the repayment effort of SATD based on its textual descriptions. To
address this limitation, we propose a novel approach for automatically
estimating SATD repayment effort, utilizing a comprehensive dataset comprising
341,740 SATD items from 2,568,728 commits across 1,060 Apache repositories. Our
findings show that different types of SATD require varying levels of repayment
effort, with code/design, requirement, and test debt demanding greater effort
compared to non-SATD items, while documentation debt requires less. We
introduce and evaluate machine learning methodologies, particularly BERT and
TextCNN, which outperforms classic machine learning methods and the naive
baseline in estimating repayment effort. Additionally, we summarize keywords
associated with varying levels of repayment effort that occur during SATD
repayment. Our contributions aim to enhance the prioritization of SATD
repayment effort and resource allocation efficiency, ultimately benefiting
software development and maintainability.

自我承认技术债务（SATD）是一种特定形式的技术债务，由开发人员在软件工件中明确记录，如源代码注释和提交消息。本研究旨在提出一种新方法，利用大规模数据集自动估计 SATD 的还款工作，通过评估不同类型的债务及其还款的程度，并应用机器学习技术来提高还款工作的优先级和资源分配效率，以最终促进软件开发和可维护性。