Matching patients to clinical trials is a key unsolved challenge in bringing
new drugs to market. Today, identifying patients who meet a trial's eligibility
criteria is highly manual, taking up to 1 hour per patient. Automated screening
is challenging, however, as it requires understanding unstructured clinical
text. Large language models (LLMs) offer a promising solution. In this work, we
explore their application to trial matching. First, we design an LLM-based
system which, given a patient's medical history as unstructured clinical text,
evaluates whether that patient meets a set of inclusion criteria (also
specified as free text). Our zero-shot system achieves state-of-the-art scores
on the n2c2 2018 cohort selection benchmark. Second, we improve the data and
cost efficiency of our method by identifying a prompting strategy which matches
patients an order of magnitude faster and more cheaply than the status quo, and
develop a two-stage retrieval pipeline that reduces the number of tokens
processed by up to a third while retaining high performance. Third, we evaluate
the interpretability of our system by having clinicians evaluate the natural
language justifications generated by the LLM for each eligibility decision, and
show that it can output coherent explanations for 97% of its correct decisions
and 75% of its incorrect ones. Our results establish the feasibility of using
LLMs to accelerate clinical trial operations.

利用大型语言模型加速临床试验操作，设计了一个基于零 - shot 方法的系统来评估患者是否符合试验纳入标准，并通过优化数据和成本效率以及提高解释性来改进该方法。

零射机临床试验患者匹配与 LLMs

Zero-Shot Clinical Trial Patient Matching with LLMs

Parkinson's disease (PD) is a neuro-degenerative disorder that affects
movement, speech, and coordination. Timely diagnosis and treatment can improve
the quality of life for PD patients. However, access to clinical diagnosis is
limited in low and middle income countries (LMICs). Therefore, development of
automated screening tools for PD can have a huge social impact, particularly in
the public health sector. In this paper, we present PULSAR, a novel method to
screen for PD from webcam-recorded videos of the finger-tapping task from the
Movement Disorder Society - Unified Parkinson's Disease Rating Scale
(MDS-UPDRS). PULSAR is trained and evaluated on data collected from 382
participants (183 self-reported as PD patients). We used an adaptive graph
convolutional neural network to dynamically learn the spatio temporal graph
edges specific to the finger-tapping task. We enhanced this idea with a multi
stream adaptive convolution model to learn features from different modalities
of data critical to detect PD, such as relative location of the finger joints,
velocity and acceleration of tapping. As the labels of the videos are
self-reported, there could be cases of undiagnosed PD in the non-PD labeled
samples. We leveraged the idea of Positive Unlabeled (PU) Learning that does
not need labeled negative data. Our experiments show clear benefit of modeling
the problem in this way. PULSAR achieved 80.95% accuracy in validation set and
a mean accuracy of 71.29% (2.49% standard deviation) in independent test,
despite being trained with limited amount of data. This is specially promising
as labeled data is scarce in health care sector. We hope PULSAR will make PD
screening more accessible to everyone. The proposed techniques could be
extended for assessment of other movement disorders, such as ataxia, and
Huntington's disease.

PULSAR 是一种新型方法，通过以视频的形式记录手指敲击任务以筛查帕金森病 (PD)，并通过自适应图卷积神经网络和多流自适应卷积模型学习数据的关键特征，实现了 80.95% 的准确率和 71.29% 的平均准确率，该方法有望增加 PD 筛查的普及性。

基于图的多流自适应卷积正负样本学习用于帕金森病识别

PULSAR: Graph based Positive Unlabeled Learning with Multi Stream  Adaptive Convolutions for Parkinson's Disease Recognition

The core activity of a Private Equity (PE) firm is to invest into companies
in order to provide the investors with profit, usually within 4-7 years. To
invest into a company or not is typically done manually by looking at various
performance indicators of the company and then making a decision often based on
instinct. This process is rather unmanageable given the large number of
companies to potentially invest. Moreover, as more data about company
performance indicators becomes available and the number of different indicators
one may want to consider increases, manual crawling and assessment of
investment opportunities becomes inefficient and ultimately impossible. To
address these issues, this paper proposes a framework for automated data-driven
screening of investment opportunities and thus the recommendation of businesses
to invest in. The framework draws on data from several sources to assess the
financial and managerial position of a company, and then uses an explainable
artificial intelligence (XAI) engine to suggest investment recommendations. The
robustness of the model is validated using different AI algorithms, class
imbalance-handling methods, and features extracted from the available data
sources.

本文提出一个基于数据驱动的框架来自动筛选投资机会，并且推荐可以投资的公司。该框架利用多个数据来源来对公司的财务和管理状况进行评估，并使用可解释性人工智能（XAI）引擎提供投资建议。此模型的健壮性通过不同的人工智能算法，类别不平衡处理方法和从可用数据来源提取的特征进行验证。