Pulmonary Hypertension (PH) is a severe disease characterized by an elevated
pulmonary artery pressure. The gold standard for PH diagnosis is measurement of
mean Pulmonary Artery Pressure (mPAP) during an invasive Right Heart
Catheterization. In this paper, we investigate noninvasive approach to PH
detection utilizing Magnetic Resonance Imaging, Computer Models and Machine
Learning. We show using the ablation study, that physics-informed feature
engineering based on models of blood circulation increases the performance of
Gradient Boosting Decision Trees-based algorithms for classification of PH and
regression of values of mPAP. We compare results of regression (with
thresholding of estimated mPAP) and classification and demonstrate that metrics
achieved in both experiments are comparable. The predicted mPAP values are more
informative to the physicians than the probability of PH returned by
classification models. They provide the intuitive explanation of the outcome of
the machine learning model (clinicians are accustomed to the mPAP metric,
contrary to the PH probability).

肺动脉高压是一种严重疾病，其特征为肺动脉压力升高。本文研究了使用磁共振成像、计算机模型和机器学习进行非侵入性肺动脉高压检测的方法。我们通过消融研究表明，基于血液循环模型的物理启发特征工程提高了梯度提升决策树算法在肺动脉高压分类和 mPAP 值回归方面的性能。我们比较了回归（通过阈值估计 mPAP）和分类的结果，并证明了两个实验的指标是可比较的。与分类模型返回的肺动脉高压概率相比，预测的 mPAP 值对于医师来说更具信息价值。它们提供了机器学习模型结果的直观解释（临床医生习惯使用 mPAP 指标，而非肺动脉高压概率）。

利用 MRI、计算机模型和机器学习非侵入性估计肺动脉平均压力

Noninvasive Estimation of Mean Pulmonary Artery Pressure Using MRI,  Computer Models, and Machine Learning

Identifying disease phenotypes from electronic health records (EHRs) is
critical for numerous secondary uses. Manually encoding physician knowledge
into rules is particularly challenging for rare diseases due to inadequate EHR
coding, necessitating review of clinical notes. Large language models (LLMs)
offer promise in text understanding but may not efficiently handle real-world
clinical documentation. We propose a zero-shot LLM-based method enriched by
retrieval-augmented generation and MapReduce, which pre-identifies
disease-related text snippets to be used in parallel as queries for the LLM to
establish diagnosis. We show that this method as applied to pulmonary
hypertension (PH), a rare disease characterized by elevated arterial pressures
in the lungs, significantly outperforms physician logic rules ($F_1$ score of
0.62 vs. 0.75). This method has the potential to enhance rare disease cohort
identification, expanding the scope of robust clinical research and care gap
identification.

通过使用零射击的基于大型语言模型的方法，结合检索增强生成和 MapReduce 技术，我们在肺动脉高压领域展示了一种有效识别疾病的方法，该方法在诊断中明显优于医生逻辑规则（F1 分数为 0.62 比 0.75），从而有望增强罕见疾病群体的识别和推动临床研究以及关注空缺的发现。

基于检索增强生成的大型语言模型在零样本疾病表型分类中的应用

Large Language Models with Retrieval-Augmented Generation for Zero-Shot  Disease Phenotyping

Pulmonary hypertension (PH) is a condition of high blood pressure that
affects the arteries in the lungs and the right side of the heart (Mayo Clinic,
2017). A mean pulmonary artery pressure greater than 25 mmHg is defined as
Pulmonary hypertension. The estimated 5-year survival rate from the time of
diagnosis of pulmonary hypertension is only 57% without therapy and patients
with right heart failure only survive for approximately 1 year without
treatment (Benza et al., 2012). Given the indolent nature of the disease, early
detection of PH remains a challenge leading to delays in therapy.
Echocardiography is currently used as a screening tool for diagnosing PH.
However, electrocardiography (ECG), a more accessible, simple to use, and
cost-effective tool compared to echocardiography, is less studied and explored
for screening at-risk patients for PH. The goal of this project is to create a
neural network model which can process an ECG signal and detect the presence of
PH with a confidence probability. I created a dense neural network (DNN) model
that has an accuracy of 98% over the available training sample. For future
steps, the current model will be updated with a model suited for time-series
data. To balance the dataset with proper training samples, I will generate
additional data using data augmentation techniques. Through early and accurate
detection of conditions such as PH, we widen the spectrum of innovation in
detecting chronic life-threatening health conditions and reduce associated
mortality and morbidity.

这篇研究针对肺动脉高压这一疾病，使用人工神经网络技术，将心电图作为较简易的筛查工具，达到较高的检测准确度，以实现对该疾病的早期诊断和治疗，降低其相关死亡率和发病率。