APPLYING MACHINE LEARNING ALGORITHMS TO PATIENT DATA IS HELPING STANFORD RESEARCHERS BETTER DIAGNOSE AND TREAT LUNG DISEASE.

将机器学习算法应用于患者数据正在帮助斯坦福大学的研究人员更好地诊断和治疗肺部疾病。

Parts of medicine can be trial and error-if one drug doesn’t work, try another; if a diagnosis isn’t leading to a cure, maybe the diagnosis is wrong. But eliminating that trial and error, through more informed diagnostic tests, saves time for both clinicians and patients. In the division of pulmonary, allergy and critical care medicine, machine learning algorithms are now guiding those more personalized treatment decisions.

某些药物可能会反复试验-如果一种药物无效，请尝试另一种药物。 如果诊断不能治愈，则可能是错误的诊断。 但是，通过更明智的诊断测试来消除这种尝试和错误，可以为临床医生和患者节省时间。 在肺，过敏和重症监护医学领域，机器学习算法现在正在指导那些更个性化的治疗决策。

“We’re at a critical juncture in pulmonary medicine, where innovative analysis approaches are needed to handle the large number of patient samples and clinical variables we are collecting for research,” says Andrew Sweatt, MD, a clinical assistant professor of pulmonary, allergy, and critical care medicine. “Machine learning is a promising tool that can help us with most of this high-throughput data.”

“我们正处于肺部医学的关键时刻，需要创新的分析方法来处理我们正在收集的大量患者样品和临床变量，以进行研究，”肺部过敏临床助理教授安德鲁·斯威特 ( Andrew Sweatt)说和重症监护药物。 “机器学习是一种很有前途的工具，可以帮助我们处理大部分此类高通量数据。”

In machine learning, a computer program sifts through data-whether it’s information on the levels of different molecules in a blood sample or scans of the lungs-and finds otherwise hidden patterns. Often, such programs can do a better job than the human eye at spotting structure in the data, finding correlations between data and patient outcomes, or pinpointing groups of variables that set some patients apart.

在机器学习中，计算机程序会筛选数据(无论是有关血液样本中不同分子水平的信息还是对肺部扫描的信息)，并找到其他隐藏的模式。 通常，这样的程序在发现数据结构，发现数据与患者结果之间的相关性或查明使某些患者与众不同的变量组方面比人眼做得更好。

“We’re not trying to replace doctors, but with machine learning, there’s a huge potential for augmenting clinical decisions by physicians,” says Husham Sharifi, MD, instructor of pulmonary, allergy, and critical care medicine.

“我们不是要取代医生，而是通过机器学习，有很大的潜力可以扩大医师的临床决策，”肺部，过敏和重症监护医学的讲师Husham Sharifi说。

Many patients with pulmonary arterial hypertension (PAH) have other underlying diseases-scleroderma, lupus, cirrhosis, congenital heart disease, or HIV, to name a few. Others have been exposed to drugs or toxins, such as methamphetamine. And in roughly a third to half of patients, the rare lung disease appears without any explanation. In all cases, though, the underlying disease is the same: The small arteries that carry blood through the lungs narrow over time due to structural changes. This progression leads to high blood pressure in the lungs and places strain on the heart.

许多肺动脉高压(PAH)患者还有其他潜在疾病-硬皮病，狼疮，肝硬化，先天性心脏病或HIV，仅举几例。 其他人则接触过毒品或毒素，如甲基苯丙胺。 在大约三分之一至一半的患者中，这种罕见的肺部疾病似乎没有任何解释。 但是，在所有情况下，潜在疾病都是相同的：由于结构变化，随着时间的流逝，携带血液通过肺部的小动脉会逐渐变窄。 这种进展会导致肺部高压，并使心脏承受压力。

“It’s a very aggressive disease, and there’s a lot of room to improve patient outcomes,” says Sweatt.

“这是一种非常具有侵略性的疾病，还有很大的空间可以改善患者的预后，” Sweatt说。

Without treatment, nearly half of all patients die within five years of their diagnosis. Over the past decade, several drugs have been approved to treat PAH. The treatments don’t consistently work in all patients, however, although they all have the same mechanism-to relax and open blood vessels.

未经治疗，将近一半的患者在诊断后五年内死亡。 在过去的十年中，已经批准了几种药物来治疗PAH。 尽管并非所有患者都具有相同的放松和打开血管的机制，但这些治疗方法并非始终适用于所有患者。

A large body of research has suggested that there’s a component of PAH that’s mediated by the immune system, and new drugs are in development to target this inflammation. Sweatt wanted to know whether some patients would be better helped by these new drugs. Until now, PAH has been grouped into subtypes based on the patient’s underlying predisposition, and all subtypes have been treated the same.

大量研究表明，PAH的一部分是由免疫系统介导的，目前正在开发针对这种炎症的新药。 斯威特想知道是否有些患者可以通过这些新药得到更好的帮助。 到目前为止，PAH已根据患者的潜在易感性分为亚型，并且所有亚型均已接受相同治疗。

Sweatt and his colleagues collected blood samples from 385 PAH patients and measured levels of 48 immune proteins and signaling molecules. Then they let a machine-learning program parse the data set.

Sweatt和他的同事从385名PAH患者中收集了血液样本，并测量了48种免疫蛋白和信号分子的水平。 然后，他们让机器学习程序解析数据集。

“My goal was to remain agnostic by avoiding common pre-conceived notions about the disease, and instead let the molecular data alone tell the story,” says Sweatt.

Sweatt说：“我的目标是通过避免对疾病的普遍先入为主的观念来保持不可知论，而仅让分子数据来讲述故事。”

It worked- the program revealed four previously unknown subtypes of PAH based on the immune profiles of the patients. One-third of the patients studied had minimal inflammation, suggesting that drugs targeting the immune system may not be helpful for them. The three other groups were each distinguished by their unique inflammatory signatures in the blood.

它起作用了，该程序根据患者的免疫特征揭示了四种先前未知的PAH亚型。 被研究的患者中有三分之一的炎症很小，表明针对免疫系统的药物可能对他们没有帮助。 其他三个组分别以其在血液中的独特炎症特征而区分。

Importantly, the clinical disease severity and risk of death also differed among the four subgroups.

重要的是，四个亚组之间的临床疾病严重程度和死亡风险也有所不同。

“What really stood out is that these immune phenotypes were completely independent of the cause of PAH,” says Sweatt. In other words, patients who had underlying immune diseases like lupus or scleroderma were just as likely to be in each subcategory of PAH as patients with no underlying disease. “It means we really detected a hidden system for classifying patients that is highly relevant to underlying disease biology and clinical outcomes,” he says.

“真正突出的是这些免疫表型完全独立于PAH的病因，” Sweatt说。 换句话说，患有潜在性免疫疾病(如狼疮或硬皮病)的患者与没有潜在疾病的患者一样，属于PAH的每个子类别。 他说：“这意味着我们确实检测到了一个用于对患者进行分类的隐藏系统，该系统与潜在的疾病生物学和临床结果高度相关。”

The data suggest that different types of immune drugs may work against PAH for different patients, but more work is needed to determine whether the new immune subtypes can help guide treatment. Sweatt’s research has been recognized as an innovative first step toward precision medicine in PAH. Building on this foundational work, Sweatt also has additional machine learning-based studies planned to better understand the biological underpinnings and therapy ramifications of each immune subtype.

数据表明，不同类型的免疫药物可能对不同患者的PAH起作用，但是需要更多的工作来确定新的免疫亚型是否可以帮助指导治疗。 Sweatt的研究被认为是PAH朝着精准医学迈出的创新性第一步。 在此基础工作的基础上，Sweatt还计划进行其他基于机器学习的研究，以更好地了解每种免疫亚型的生物学基础和治疗结果。

Another challenge involves graft-versus-host disease of the lungs-also known as bronchiolitis obliterans syndrome (BOS). In that case, the challenge is not differentiating subtypes of patients, but diagnosing them in the first place. Graft-versus-host disease is a complication of a bone marrow or blood stem cell transplant in which the donated bone marrow or stem cells start attacking the body. But BOS can closely resemble other common complications of a transplant, including infections and inflammatory disorders.

另一个挑战涉及肺移植物抗宿主疾病，也称为闭塞性细支气管炎综合征(BOS)。 在那种情况下，挑战不是区分患者的亚型，而是首先诊断它们。 移植物抗宿主病是骨髓或血液干细胞移植的并发症，其中捐赠的骨髓或干细胞开始攻击人体。 但是BOS可以非常类似于移植的其他常见并发症，包括感染和炎症性疾病。

“All these types of lung disease are poorly defined,” says Joe Hsu, MD, an assistant professor of pulmonary, allergy, and critical care medicine. “The way we typically diagnose graft-versus-host disease is to look for everything else and, if we don’t find anything else, diagnose that.”

“所有这些类型的肺部疾病的定义都不明确，”肺部，过敏和重症监护医学助理教授乔许说。 “我们通常诊断移植物抗宿主病的方法是寻找其他一切，如果我们找不到其他东西，则进行诊断。”

Hsu and Sharifi wanted to do better at diagnosing BOS. They started collecting CT scans from patients with BOS as well as from transplant patients who had similar symptoms but did not have BOS. Then they used a machine learning approach-telling a computer program which cases were which and letting it learn how to differentiate them.

Hsu和Sharifi希望在诊断BOS方面做得更好。 他们开始从BOS患者以及症状相似但没有BOS的移植患者中收集CT扫描。 然后，他们使用机器学习方法，讲述了一个计算机程序，确定了哪些情况，并让其学习如何区分它们。

The machine, it turned out, became so good at telling BOS apart from other lung diseases that it was even slightly better than thoracic radiologists, who regularly read CT scans of the chest. The program learned to differentiate normal lung, mild BOS, severe BOS, and alternative diagnoses.

事实证明，这台机器与其他肺部疾病相比，能很好地告诉BOS，甚至比定期读取胸部CT扫描的胸腔放射科医生要好得多。 该程序学会了区分正常的肺，轻度的BOS，严重的BOS和其他诊断。

“It was seeing things that the eye couldn’t necessarily pick up on and improving the diagnosis quite a bit,” says Hsu.

Hsu说：“看到的东西不一定能引起眼睛的注意并改善了诊断。”

Since each diagnosis is treated differently, fast and easy diagnosis is critical. Hsu and Sharifi say in the future, similar programs might be able to differentiate other diagnoses as well, such as chronic obstructive pulmonary disease (COPD). Pulmonology, Sharifi points out, is full of numerical and imaging data that can be leveraged with machine learning.

由于每种诊断的处理方式都不相同，因此快速而简便的诊断至关重要。 Hsu和Sharifi表示，将来类似的程序也可能能够区分其他诊断，例如慢性阻塞性肺疾病(COPD)。 Sharifi指出，肺病学充满了可以通过机器学习加以利用的数值和影像数据。

“For a lot of other aspects of medicine, it’s a bigger challenge to integrate artificial intelligence because clinical notes can be so messy and unstructured,” he says. “But this is a good example of where algorithmic and computational analysis can be used hand in hand with a doctor’s advanced training and experience.”

他说：“对于医学的许多其他方面来说，集成人工智能是一个更大的挑战，因为临床笔记可能是如此混乱且无序。” “但是，这是一个很好的例子，可以在医生的高级培训和经验的共同作用下使用算法和计算分析。”

Originally published at https://medicine.stanford.edu.

最初发布在 https://medicine.stanford.edu 。