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人工智能可能成为研究人员解决长期新冠谜题的唯一途径

人工智能可能成为研究人员解决长期新冠谜题的唯一途径

ERIN PRATER 2022-08-08
人工智能可以帮助发现出现慢性病症概率较高的患者,使他们能够得到治疗。

长期新冠可能是一个至少仅靠人类无法解决的复杂问题。

研究人员越来越多地依靠人工智能,帮助其对数以百万计长期新冠患者的电子病历进行归类,以更好地弄清楚这种存在数百种潜在症状的谜一样的病症。

在某些情况下,人工智能正在为患者提供帮助:在《柳叶刀-数字医疗》(The Lancet Digital Health)最近发表的一份研究论文中,研究人员训练三个机器学习模型,从之前感染新冠的患者当中识别出潜在长期新冠患者。模型和人类都能识别出绝大多数潜在长期新冠患者,这表明人工智能可以帮助发现出现慢性病症概率较高的患者,使他们能够得到治疗。

纽约威尔康奈尔医学院(Weill Cornell Medicine)医疗政策与研究助理教授王飞最近与他人共同发表了一篇研究论文,分析了长期新冠患者的诊断模式。

研究人员利用机器学习分析了数千名患者的电子病历,并从长期新冠患者中找到了四种模式。他表示:

• 重症患者患有血液和心脏疾病,许多患者可能在2020年春纽约市爆发首轮疫情时被感染。这个群体患既有病症的数量最多。

• 较轻症患者患有呼吸道疾病,并伴有睡眠问题。

• 患者新出现了肌肉骨骼疾病和神经精神疾病。

• 患者现在患有胃肠道疾病,包括腹痛。

王飞对《财富》杂志表示,长期新冠“非常复杂,因为它不只是一次病毒感染”,除了炎症和免疫系统疾病以外,还会对肺部以及人体所有器官系统造成影响,会引发“许多复杂的反应”。

研究人员越早对患者进行分类,查明患者的病因,比如器官损伤和失控性炎症反应等,就能尽早开发出针对性的疗法。王飞表示,有些患者在感染新冠之后出现的新疾病可能与新冠无关,但确实会混淆视听,所以由人工智能辅助从大量患者中找出长期新冠模式至关重要。

在开发出治疗方法之后,可以使用通过机器学习模型开发的患者清单,招募患者进行试验。招募患者的过程往往需要耗费大量成本和后勤服务。

人工智能还可以帮助研究人员进一步按照病毒变异株和亚变异株对患者进行分类,从而发现与不同感染期相关联的长期新冠的模式。

例如,王飞表示,在第一波疫情中“有许多人住院,许多患者被送进ICU,还有许多人使用了机械辅助通气。当时的死亡率也是最高的。”

这批患者的长期新冠是由新冠病毒导致的还是由重症监护后综合征导致的?后者是由创伤性的、令人虚弱的ICU治疗所导致的,其中可能包括插管治疗和长期卧床等。潜在症状包括长期肌无力、记忆问题和创伤后应激障碍等。

王飞希望人工智能能够尽快解开这个谜题。

最近一项算法辅助研究发现,长期新冠患者疫情之前使用类皮质激素的比率较高。许多重症新冠患者在医院中接受了类固醇治疗,尤其是使用呼吸机的患者。类固醇是否是导致长期新冠的原因,或者在其中发挥了一定作用?或者类固醇只是代表病情更严重的患者,这些患者由于基础病症或更严重的病毒感染进程,患长期新冠的风险更高。

王飞没有答案,也没有人能够确定。数以百万计患者感染病毒之后幸存了下来,却发现自己还要经历一场最为残酷的战斗,王飞希望利用人工智能进行研究,找到更多答案,尽快为他们找到更多治疗方法。

虽然许多人认为奥密克戎变异株似乎更加温和,但有数据表明,BA.2亚变异株引发长期新冠的风险高于BA.1。

王飞警告称:“如果你看过所有这些数据,你会在日常生活中非常小心,继续做好防护。因为这场疫情远没有结束。”(财富中文网)

翻译:刘进龙

审校:汪皓

长期新冠可能是一个至少仅靠人类无法解决的复杂问题。

研究人员越来越多地依靠人工智能,帮助其对数以百万计长期新冠患者的电子病历进行归类,以更好地弄清楚这种存在数百种潜在症状的谜一样的病症。

在某些情况下,人工智能正在为患者提供帮助:在《柳叶刀-数字医疗》(The Lancet Digital Health)最近发表的一份研究论文中,研究人员训练三个机器学习模型,从之前感染新冠的患者当中识别出潜在长期新冠患者。模型和人类都能识别出绝大多数潜在长期新冠患者,这表明人工智能可以帮助发现出现慢性病症概率较高的患者,使他们能够得到治疗。

纽约威尔康奈尔医学院(Weill Cornell Medicine)医疗政策与研究助理教授王飞最近与他人共同发表了一篇研究论文,分析了长期新冠患者的诊断模式。

研究人员利用机器学习分析了数千名患者的电子病历,并从长期新冠患者中找到了四种模式。他表示:

• 重症患者患有血液和心脏疾病,许多患者可能在2020年春纽约市爆发首轮疫情时被感染。这个群体患既有病症的数量最多。

• 较轻症患者患有呼吸道疾病,并伴有睡眠问题。

• 患者新出现了肌肉骨骼疾病和神经精神疾病。

• 患者现在患有胃肠道疾病,包括腹痛。

王飞对《财富》杂志表示,长期新冠“非常复杂,因为它不只是一次病毒感染”,除了炎症和免疫系统疾病以外,还会对肺部以及人体所有器官系统造成影响,会引发“许多复杂的反应”。

研究人员越早对患者进行分类,查明患者的病因,比如器官损伤和失控性炎症反应等,就能尽早开发出针对性的疗法。王飞表示,有些患者在感染新冠之后出现的新疾病可能与新冠无关,但确实会混淆视听,所以由人工智能辅助从大量患者中找出长期新冠模式至关重要。

在开发出治疗方法之后,可以使用通过机器学习模型开发的患者清单,招募患者进行试验。招募患者的过程往往需要耗费大量成本和后勤服务。

人工智能还可以帮助研究人员进一步按照病毒变异株和亚变异株对患者进行分类,从而发现与不同感染期相关联的长期新冠的模式。

例如,王飞表示,在第一波疫情中“有许多人住院,许多患者被送进ICU,还有许多人使用了机械辅助通气。当时的死亡率也是最高的。”

这批患者的长期新冠是由新冠病毒导致的还是由重症监护后综合征导致的?后者是由创伤性的、令人虚弱的ICU治疗所导致的,其中可能包括插管治疗和长期卧床等。潜在症状包括长期肌无力、记忆问题和创伤后应激障碍等。

王飞希望人工智能能够尽快解开这个谜题。

最近一项算法辅助研究发现,长期新冠患者疫情之前使用类皮质激素的比率较高。许多重症新冠患者在医院中接受了类固醇治疗,尤其是使用呼吸机的患者。类固醇是否是导致长期新冠的原因,或者在其中发挥了一定作用?或者类固醇只是代表病情更严重的患者,这些患者由于基础病症或更严重的病毒感染进程,患长期新冠的风险更高。

王飞没有答案,也没有人能够确定。数以百万计患者感染病毒之后幸存了下来,却发现自己还要经历一场最为残酷的战斗,王飞希望利用人工智能进行研究,找到更多答案,尽快为他们找到更多治疗方法。

虽然许多人认为奥密克戎变异株似乎更加温和,但有数据表明,BA.2亚变异株引发长期新冠的风险高于BA.1。

王飞警告称:“如果你看过所有这些数据,你会在日常生活中非常小心,继续做好防护。因为这场疫情远没有结束。”(财富中文网)

翻译:刘进龙

审校:汪皓

Long COVID may be too big a problem for humans to solve—alone, at least.

Increasingly, researchers are turning to artificial intelligence to help them sort through the electronic medical records of millions of long-COVID patients in hopes of better understanding the enigmatic condition with hundreds of potential symptoms.

In some cases, A.I. is helping patients: In a study published last month in The Lancet Digital Health, researchers trained three machine-learning models to identify potential long-COVID patients among hundreds who previously had COVID. Both the models and humans agreed on probable “long haulers” in the vast majority of cases, showing that A.I. can help flag patients who have a high probability of experiencing the chronic condition and get them to care.

Fei Wang, assistant professor of health care policy and research at Weill Cornell Medicine in New York, is coauthor of a recently published study that examined patterns of diagnoses in long-COVID patients.

The researchers used machine learning to examine the electronic health records of thousands of patients and found four patterns among long-COVID patients, he said:

• More severe patients with blood and heart issues, many of whom likely were infected during the initial wave to hit New York City in the spring of 2020. This group had the largest number of patients with preexisting conditions.

• More mild patients with respiratory issues accompanied by sleep problems.

• Patients with new musculoskeletal complaints and neuropsychiatric problems.

• Patients who now suffer from gastrointestinal issues, including abdominal pain.

Long COVID is “so complex because it involves not just an infection” but potential fallout in the lungs and nearly every organ system in the body, in addition to inflammation, immune system issues—“lots of complicated reactions,” Wang told Fortune.

The sooner researchers can categorize patients and ascertain the cause of their disease—perhaps organ damage in some, and out-of-control inflammation in others—the sooner targeted therapies can be developed. It’s possible that some patients complaining of new ailments after COVID have unrelated issues, veritable red herrings—which is why A.I.’s assistance in sussing out patterns among the masses is critical, Wang said.

Later on, when treatments are developed, the patient lists developed by machine learning can be used to recruit patients for trials—a task that can be expensive and logistically tricky.

A.I. can also help researchers further categorize patients by variant and subvariant, enabling them to recognize patterns of long COVID that may correlate with various waves of infection.

For example, the first wave of COVID saw “lots of people being hospitalized, lots in the ICU, lots of mechanical ventilation,” Wang said. “The mortality rate was also the highest then.”

Is the long COVID of such patients caused by the coronavirus or Post-intensive Care Syndrome? The latter is caused by a traumatic and debilitating ICU stay that may have included intubation and prolonged bed confinement. Potential symptoms can include persistent muscle weakness, memory problems, and post-traumatic stress disorder.

It’s a puzzle Wang hopes A.I. can solve, with rapidity.

A recent algorithm-assisted study found a high rate of pre-COVID corticosteroid use in long-COVID patients. Many patients with severe COVID were treated with steroids in the hospital, especially those who were on ventilators. Do steroids cause long COVID, or play a role in causing it? Or are they merely indicative of sicker patients who might be at greater risk of long COVID owing to underlying medical conditions or a more severe course of the virus?

Wang isn’t sure; no one is. But he hopes the use of A.I. in research can lead to more answers, and more treatments, sooner for the millions who survived the virus only to find another—perhaps the biggest—battle lies ahead.

Although many are talking about Omicron as if it’s a more mild strain of COVID, some data suggests that BA.2 is associated with a greater risk of long COVID than BA.1.

“If you look at all this data—you need to be careful about your daily life and protection,” Wang cautioned. “We’ve not ended this pandemic yet.”

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