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抗抑郁药效果不佳?让人工智能来开药

LINDSEY LEAKE
2024-08-20

数据显示,患者经常会尝试至少两种药物,才能找到对症之药。不过人工智能有望解决这一问题。

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弗吉尼亚州费尔法克斯乔治梅森大学公共卫生学院的研究人员开发了免费工具MeAgainMeds.com,利用人工智能向抑郁症患者推荐最适合的抗抑郁药物。图片来源:RIDVAN_CELIK—GETTY IMAGES

生活中很少有事在第一次尝试时就能奏效,抗抑郁药也不例外。美国国家心理健康研究所(National Institute of Mental Health)的数据显示,患者经常会尝试至少两种药物,才能找到对症之药。不过人工智能有望解决这一问题。

弗吉尼亚州费尔法克斯乔治梅森大学(George Mason University)的研究人员改进了免费工具MeAgainMeds.com,利用人工智能根据患者人口统计特征和病史推荐抗抑郁药。乔治梅森大学公共卫生学院健康信息学教授法罗克·阿莱米主导了该项研究。

“很多服用抗抑郁药的人都感觉自己变得陌生,Me Again Meds工具正是针对这一问题设计,”阿莱米接受《财富》杂志采访时表示,“我们想帮助选择副作用更少,效果也更好的抗抑郁药。”

阿莱米的努力也有自己的原因。一位亲人自杀后,近年来阿莱米投入大量精力研究抑郁症管理方面的人工智能。

研发Me Again Meds的过程中,阿莱米和同事发表了几项研究。2021年发表在《电子临床医学》(eClinicalMedicine)杂志上的一项研究中,研究人员用OptumLabs健康保险数据库分析了近370万名确诊重度抑郁症且正服用抗抑郁药的美国患者。从2001年到2018年,患者经历了超过1020万次治疗或疗程。

研究人员分析了服用15种最常见处方抗抑郁药的患者,包括西酞普兰(Celexa)、艾司西酞普兰(Lexapro)、氟西汀(Prozac)和舍曲林(Zoloft)等,发现不同人群服药后的效果差异巨大。例如,服用氟西汀的青少年男孩中有25%症状缓解,而65-79岁的女性有59%症状缓解。

没有哪种药适合所有人群,在年龄/性别分组中,最好的抗抑郁药比最差的药疗效平均高出20多倍。阿莱米团队研究显示,如果临床医生开出缓解率最高的药物,症状缓解的患者人数将增加1.5倍,症状缓解的治疗也将增加160万次。

“人们在找到最合适的药之前都要试上三到四次。很多人甚至一直找不到合适的药,”阿莱米说,“非裔美国人开不到合适的药物,西裔美国人吃错药;各种少数族裔之间差异被忽视。病史信息也遭忽视。”

身为工程师,阿莱米总是希望系统能正常运行。尽管临床医生出于好意,但美国医疗系统开抗抑郁药的做法“总让人想起了18世纪的医学水平,”他说,“为什么做不到一开始就吃正确的药?”

2004年3月23日,星期二,在迈阿密拍摄的几瓶抗抑郁药(从左到右)Wellbutrin(安非他酮)、Paxil(帕罗西汀)、Lexapro(艾司西酞普兰)、Effexor(文拉法辛)、Zoloft(舍曲林)和氟西汀。图片来源:JOE RAEDLE—GETTY IMAGES

人工智能能否满足抗抑郁药的需求?

在2021年同一项研究中,阿莱米的团队跨越年龄和生理性别的限制为患者匹配了最有效的抗抑郁药。他们综合参与研究者的病史,生成了近17000个患者分组。研究人员没想到医生和患者要筛选如此多选项,于是选择用人工智能,由此推出了第一代Me Again Meds。

举例来说,如果是41—64岁的酒精依赖男性,Me Again Meds根据对700多名有类似病史患者的分析,判断舍曲林缓解症状机会最大。如果患者是20—40岁女性,体型肥胖且有多囊卵巢综合征,Me Again Meds建议服用安非他酮,同时提醒该药物可能无效,因为数据库中符合该标准的患者很少。该站不要求提供识别信息,但会提供报告ID,可与医生分享。

患者的反馈非常积极,然而阿莱米表示临床医生的反应喜忧参半。例如,在焦点小组和访谈中有医生表示,分析模型与现实世界中抗抑郁药处方的细微差别做不到完全匹配,对他们正治疗的患者代表性也不足。尽管数据库容量很大,一些临床医生认为数据库并不能算抑郁症患者的随机样本。

过去三年里,Me Again Meds改动过几次。3月《心理健康政策与经济学》(Journal of Mental Health Policy and Economics)杂志发表的一项研究中,阿莱米的团队分析了网站约2500名接受过心理治疗的患者分组。不过,Me Again Meds仍然是基于调查的人工智能,根据受访者之前的答案输出多种选择。工具也很简单,只需几分钟即可完成。很快就会出现更先进的聊天机器人。

“最终目标是打造独立的人工智能系统,为患者提供诊断并为行为健康提供治疗建议,”阿莱米说,“接诊过程是漫长的对话,已发表文献中还没出现过长对话。”

去年,乔治梅森大学推出了聊天机器人原型网站,现在仍在使用;对话刚开始,机器人就会询问患者有没有经历过重度抑郁。根据美国疾病控制与预防中心(Centers for Disease Control and Prevention)2015年至2018年收集的数据,超过13%的美国成年人服用抗抑郁药,包括18%的女性和8%的男性。新冠疫情期间,服药者更多。

“我们意识到,用药咨询服务的需求会非常大,”阿莱米说。

阿莱米说,在推广人工智能医生方面,患者安全是最重要的问题。例如,如果患者表现出自杀风险,聊天机器人要终止对话,立刻让患者与受过训练的真人联系。哪怕现阶段规模很小,也要让真人实时监控聊天,有助于维护聊天机器人平稳运行。而且,阿莱米和同事们正在努力减少人工智能幻觉,也就是虚假或误导性信息。他们还在开发转诊系统,帮助没有基础医保的患者联系医生。

“这个产品非常复杂;不是点开关就能工作的工具,”阿莱米说,“产品中有很多重要部分,我们正逐个组件调试。”

阿莱米预计,年底前聊天机器人的人工监控功就能上线。他还在努力解决为有色人种患者开具抗抑郁药处方时的差异。最近阿莱米团队获得了美国国立卫生研究院(NIH)资助,使用Me Again Meds和NIH的All of Us数据库研究黑人抑郁症患者对药物的反应。

根据美国国家心理健康研究所的数据,患者经常会尝试至少两种药物,才能找到对症之药。图片来源:DA-KUK—GETTY IMAGES

抗抑郁药治疗什么?

抗抑郁药并不受限于名称,治疗对象不仅仅是临床抑郁症。美国食品和药物管理局(Food and Drug Administration)已批准某些抗抑郁药治疗下列疾病:

• 双相情感障碍

• 暴食症

• 广泛焦虑症

• 强迫症

• 其他抑郁症

• 惊恐障碍

• 创伤后应激障碍

• 社交焦虑症

此外,临床医生可能会超说明书使用抗抑郁药治疗偏头痛、慢性疼痛和失眠等。

Me Again Meds可能会询问各种情绪、抑郁和焦虑障碍,但其根本目标还是帮助确诊重度抑郁症的患者。

下次看医生加入人工智能

阿莱米希望Me Again Meds能为患者和医疗服务提供者提供强大的资源,但他指出提供的内容不构成医疗建议。网站主要为患者和医生之间的讨论提供信息,Me Again Meds可以推荐,只有持证临床医生才有权开药。

如果已在服用抗抑郁药,如无医嘱不要停药。如果不听医生指导,可能出现抗抑郁药物停药综合征。(财富中文网)

译者:梁宇

审校:夏林

生活中很少有事在第一次尝试时就能奏效,抗抑郁药也不例外。美国国家心理健康研究所(National Institute of Mental Health)的数据显示,患者经常会尝试至少两种药物,才能找到对症之药。不过人工智能有望解决这一问题。

弗吉尼亚州费尔法克斯乔治梅森大学(George Mason University)的研究人员改进了免费工具MeAgainMeds.com,利用人工智能根据患者人口统计特征和病史推荐抗抑郁药。乔治梅森大学公共卫生学院健康信息学教授法罗克·阿莱米主导了该项研究。

“很多服用抗抑郁药的人都感觉自己变得陌生,Me Again Meds工具正是针对这一问题设计,”阿莱米接受《财富》杂志采访时表示,“我们想帮助选择副作用更少,效果也更好的抗抑郁药。”

阿莱米的努力也有自己的原因。一位亲人自杀后,近年来阿莱米投入大量精力研究抑郁症管理方面的人工智能。

研发Me Again Meds的过程中,阿莱米和同事发表了几项研究。2021年发表在《电子临床医学》(eClinicalMedicine)杂志上的一项研究中,研究人员用OptumLabs健康保险数据库分析了近370万名确诊重度抑郁症且正服用抗抑郁药的美国患者。从2001年到2018年,患者经历了超过1020万次治疗或疗程。

研究人员分析了服用15种最常见处方抗抑郁药的患者,包括西酞普兰(Celexa)、艾司西酞普兰(Lexapro)、氟西汀(Prozac)和舍曲林(Zoloft)等,发现不同人群服药后的效果差异巨大。例如,服用氟西汀的青少年男孩中有25%症状缓解,而65-79岁的女性有59%症状缓解。

没有哪种药适合所有人群,在年龄/性别分组中,最好的抗抑郁药比最差的药疗效平均高出20多倍。阿莱米团队研究显示,如果临床医生开出缓解率最高的药物,症状缓解的患者人数将增加1.5倍,症状缓解的治疗也将增加160万次。

“人们在找到最合适的药之前都要试上三到四次。很多人甚至一直找不到合适的药,”阿莱米说,“非裔美国人开不到合适的药物,西裔美国人吃错药;各种少数族裔之间差异被忽视。病史信息也遭忽视。”

身为工程师,阿莱米总是希望系统能正常运行。尽管临床医生出于好意,但美国医疗系统开抗抑郁药的做法“总让人想起了18世纪的医学水平,”他说,“为什么做不到一开始就吃正确的药?”

人工智能能否满足抗抑郁药的需求?

在2021年同一项研究中,阿莱米的团队跨越年龄和生理性别的限制为患者匹配了最有效的抗抑郁药。他们综合参与研究者的病史,生成了近17000个患者分组。研究人员没想到医生和患者要筛选如此多选项,于是选择用人工智能,由此推出了第一代Me Again Meds。

举例来说,如果是41—64岁的酒精依赖男性,Me Again Meds根据对700多名有类似病史患者的分析,判断舍曲林缓解症状机会最大。如果患者是20—40岁女性,体型肥胖且有多囊卵巢综合征,Me Again Meds建议服用安非他酮,同时提醒该药物可能无效,因为数据库中符合该标准的患者很少。该站不要求提供识别信息,但会提供报告ID,可与医生分享。

患者的反馈非常积极,然而阿莱米表示临床医生的反应喜忧参半。例如,在焦点小组和访谈中有医生表示,分析模型与现实世界中抗抑郁药处方的细微差别做不到完全匹配,对他们正治疗的患者代表性也不足。尽管数据库容量很大,一些临床医生认为数据库并不能算抑郁症患者的随机样本。

过去三年里,Me Again Meds改动过几次。3月《心理健康政策与经济学》(Journal of Mental Health Policy and Economics)杂志发表的一项研究中,阿莱米的团队分析了网站约2500名接受过心理治疗的患者分组。不过,Me Again Meds仍然是基于调查的人工智能,根据受访者之前的答案输出多种选择。工具也很简单,只需几分钟即可完成。很快就会出现更先进的聊天机器人。

“最终目标是打造独立的人工智能系统,为患者提供诊断并为行为健康提供治疗建议,”阿莱米说,“接诊过程是漫长的对话,已发表文献中还没出现过长对话。”

去年,乔治梅森大学推出了聊天机器人原型网站,现在仍在使用;对话刚开始,机器人就会询问患者有没有经历过重度抑郁。根据美国疾病控制与预防中心(Centers for Disease Control and Prevention)2015年至2018年收集的数据,超过13%的美国成年人服用抗抑郁药,包括18%的女性和8%的男性。新冠疫情期间,服药者更多。

“我们意识到,用药咨询服务的需求会非常大,”阿莱米说。

阿莱米说,在推广人工智能医生方面,患者安全是最重要的问题。例如,如果患者表现出自杀风险,聊天机器人要终止对话,立刻让患者与受过训练的真人联系。哪怕现阶段规模很小,也要让真人实时监控聊天,有助于维护聊天机器人平稳运行。而且,阿莱米和同事们正在努力减少人工智能幻觉,也就是虚假或误导性信息。他们还在开发转诊系统,帮助没有基础医保的患者联系医生。

“这个产品非常复杂;不是点开关就能工作的工具,”阿莱米说,“产品中有很多重要部分,我们正逐个组件调试。”

阿莱米预计,年底前聊天机器人的人工监控功就能上线。他还在努力解决为有色人种患者开具抗抑郁药处方时的差异。最近阿莱米团队获得了美国国立卫生研究院(NIH)资助,使用Me Again Meds和NIH的All of Us数据库研究黑人抑郁症患者对药物的反应。

抗抑郁药治疗什么?

抗抑郁药并不受限于名称,治疗对象不仅仅是临床抑郁症。美国食品和药物管理局(Food and Drug Administration)已批准某些抗抑郁药治疗下列疾病:

• 双相情感障碍

• 暴食症

• 广泛焦虑症

• 强迫症

• 其他抑郁症

• 惊恐障碍

• 创伤后应激障碍

• 社交焦虑症

此外,临床医生可能会超说明书使用抗抑郁药治疗偏头痛、慢性疼痛和失眠等。

Me Again Meds可能会询问各种情绪、抑郁和焦虑障碍,但其根本目标还是帮助确诊重度抑郁症的患者。

下次看医生加入人工智能

阿莱米希望Me Again Meds能为患者和医疗服务提供者提供强大的资源,但他指出提供的内容不构成医疗建议。网站主要为患者和医生之间的讨论提供信息,Me Again Meds可以推荐,只有持证临床医生才有权开药。

如果已在服用抗抑郁药,如无医嘱不要停药。如果不听医生指导,可能出现抗抑郁药物停药综合征。(财富中文网)

译者:梁宇

审校:夏林

Few things in life work out the first time you try them, and antidepressants are no exception. According to the National Institute of Mental Health, it’s not uncommon for patients to try at least two such medications before finding an effective one. But artificial intelligence is on its way to solving that problem.

Researchers at George Mason University in Fairfax, Va., have revamped MeAgainMeds.com, their free tool that uses AI to recommend antidepressants to patients based on their demographics and medical history. Farrokh Alemi, PhD, a professor of health informatics at GMU’s College of Public Health, spearheaded the effort.

“Me Again Meds, it’s a play on the fact that many people who take antidepressants feel that they are not themselves,” Alemi tells Fortune. “We want to help them with a selection of an antidepressant that has fewer side effects for them and is more effective for them.”

The pursuit is personal. After losing a loved one to suicide, Alemi has in recent years dedicated the bulk of his research to AI in depression management.

Alemi and his colleagues have published several studies in conjunction with the development of Me Again Meds. In research published in 2021 in the journal eClinicalMedicine, they used the OptumLabs health insurance database to analyze nearly 3.7 million U.S. patients who had been diagnosed with major depression and were taking antidepressants. From 2001–2018, patients collectively recorded more than 10.2 million treatment episodes, or courses of medication.

Researchers assessed patients taking 15 of the most commonly prescribed antidepressants—including citalopram (Celexa), escitalopram (Lexapro), fluoxetine (Prozac), and sertraline (Zoloft)—and found vast differences in how the medications benefitted distinct groups of people. For instance, 25% of teenage boys treated with fluoxetine experienced symptom remission, while 59% of women ages 65–79 saw symptom remission on the same medication.

No medication was best for everyone and within the age/sex subgroups, the best antidepressant was on average over 20 times more effective than the worst. Alemi’s team showed that if clinicians had prescribed the medications with the highest remission rates, 1.5 times more patients, or 1.6 million more treatment episodes, would have had symptom remission.

“People are going through three or four trials before they get the right medication. Many don’t even get the right medication,” Alemi says. “African Americans are not given the right medication, Hispanics are given the wrong medication; all kinds of minority differences are ignored. All kinds of medical history information is ignored.”

An engineer by trade, Alemi expects systems to function well. Despite clinicians’ best intentions, the U.S. health care system’s practice of prescribing antidepressants “reminds me of 18th-century medicine,” he says. “Why are we not getting the right medication the first time around?”

Can AI keep up with demand for antidepressants?

In the same 2021 study, Alemi’s team went beyond age and biological sex to match patients to the most effective antidepressants. They incorporated study participants’ medical histories to generate nearly 17,000 patient subgroups. Not expecting doctors and patients to sift through so many options, researchers turned to AI, delivering the first iteration of Me Again Meds.

For example, if you’re a man 41–64 years old with alcohol dependence, Me Again Meds determines sertraline may be most likely to relieve your symptoms based on an analysis of more than 700 patients with a similar medical history. If you’re a woman 20–40 years old with obesity and polycystic ovary syndrome, Me Again Meds recommends bupropion (Wellbutrin), with the caveat that the medication may be ineffective because so few patients in the database match your criteria. The website doesn’t ask for identifying information but provides a report ID you can share with your doctor.

Though patient feedback has been overwhelmingly positive, Alemi says clinicians’ reactions have been mixed. In focus groups and interviews, for example, providers said the analytical model failed to match the nuance of real-world antidepressant prescription and wasn’t representative of the patients they treat. Despite the database’s volume, some clinicians also took issue that it wasn’t a randomized sample of patients with depression.

Me Again Meds has been revised several times in the last three years. Most recently, in a study published in March in the Journal of Mental Health Policy and Economics, Alemi’s team analyzed roughly 2,500 of the site’s subgroups of patients who had received psychotherapy. Still, Me Again Meds remains a survey-based AI that outputs varying multiple-choice questions based on respondents’ previous answers. It’s also brief, taking just minutes to complete. A more advanced chatbot is coming soon.

“Our eventual goal is to create a standalone AI system that diagnoses patients and suggests treatments for the patient in behavioral health,” Alemi says. “That intake process is a long conversation, and I don’t see any long conversations right now in the published literature.”

Last year, GMU launched a prototype chatbot site that’s still active; the conversation kicks off with the bot asking the patient if they’ve experienced major depression. More than 13% of U.S. adults use antidepressants—including 18% of women and 8% of men—according to data the Centers for Disease Control and Prevention collected from 2015–18. The COVID-19 pandemic exacerbated their use.

“We are conscious that the demand for the service would be very large,” Alemi says.

Patient safety is a top concern in bringing an artificial clinician to scale, Alemi says. For example, if a patient is displaying risk factors for suicide, the chatbot would need to terminate the conversation and connect the patient to a live person who is trained to help. Even at a smaller scale, having people monitor chats in real time will help keep the chatbot running smoothly. What’s more, Alemi and his colleagues are working to reduce AI hallucination, or the generation of false or misleading information. They’re also developing a referral system to connect patients without a primary care provider to a prescribing clinician.

“This is a very complicated product; it’s not something that you click on the switch and it works,” Alemi says. “It has many significant parts, and we are working component by component to put it in place.”

Alemi expects the chatbot’s human monitoring feature will be live by the end of the year. He’s also tackling the disparities he sees in the prescription of antidepressants to patients of color. Alemi’s team recently received a grant from the National Institutes of Health (NIH) to research how Black patients with depression respond to medication, using Me Again Meds and the NIH’s All of Us database.

What do antidepressants treat?

Contrary to what its name suggests, antidepressant medication is prescribed to treat more than clinical depression. The Food and Drug Administration has approved certain antidepressants to treat these disorders:

• Bipolar disorder

• Bulimia

• Generalized anxiety disorder

• Obsessive-compulsive disorder

• Other depressive disorders

• Panic disorder

• Posttraumatic stress disorder

• Social anxiety disorder

In addition, clinicians may prescribe antidepressants for off-label use to treat conditions such as migraine, chronic pain, and insomnia.

While Me Again Meds may ask you about a variety of mood, depression, and anxiety disorders, it was designed to help people diagnosed with major depression.

Integrating AI into your next doctor’s appointment

Alemi hopes Me Again Meds proves a powerful resource for patients and providers but notes it doesn’t constitute medical advice. The website is meant to inform discussion between you and your doctor, and only a licensed clinician can prescribe medication Me Again Meds may recommend.

If you’re already taking an antidepressant, don’t stop doing so unless instructed by your doctor; antidepressant discontinuation syndrome may occur without a doctor’s guidance.

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