演示地址

It is equally dangerous at either extreme — to have either an expanding concept of mental disorder that eliminates normal or to have an expanding concept of normal that eliminates mental disorder.― Allen Frances, MD

在任何一种极端情况下，危险都是同样危险的-拥有扩大的精神障碍消除正常现象的概念，或拥有扩大的消除精神紊乱的正常概念。——艾伦·弗朗西斯，医学博士

Psychiatry is a relatively new medical specialty, having been included with other medical specialties in the mid-19th Century. But it was primarily limited to psychiatric hospitals.

精神病学是一种相对较新的医学专业，在19世纪中叶已被包括在其他医学专业中。 但这主要限于精神病医院。

As was true of much of medicine at the time, treatment was rudimentary, often harsh, and generally ineffective. Psychiatrists did not treat outpatients, i.e., anyone who functioned even minimally in everyday society. Instead, neurologists treated “nervous” conditions, named for their presumed origin in disordered nerves.

就像当时的许多药物一样，治疗是基本的，通常是严厉的，并且通常是无效的。 精神科医生不治疗门诊病人，即在日常社会中工作甚少的人。 取而代之的是，神经科医生治疗“神经性”疾病，这种疾病的根源是神经紊乱。

Gradually, with Freud’s promotion of psychoanalysis as a fashionable medical intervention for the ills of wealthy patients, the treatments changed. Less-well-to-do patients were still subjected to caging, tubbing, bloodletting, extreme confinement, and other questionable interventions, both new and old.

逐渐地，随着弗洛伊德将精神分析作为一种针对富裕病人的疾病的流行医学干预手段，治疗方法发生了变化。 情况较差的患者仍然接受笼子，输液管，放血，极端禁闭和其他可疑的新旧干预措施 。

从放血到沙发，什么？ (From Bloodletting to the Couch to What?)

What could possibly motivate so many physicians to declare suchbenefits of bleeding if it was a useless procedure? Does not this triedand tested practice, persisting doggedly for most of two millennia,actually comprise the longest clinical trial in medical history, involvingthousands of physicians and millions of patients?

如果这是无用的手术，那么有什么可能会促使如此多的医生宣布这种出血的益处？ 难道这个经过两个世纪的顽固坚持，久经考验的实践实际上构成了医学史上最长的临床试验，涉及成千上万的医生和数百万的患者吗？

The same questions would seem appropriate today regarding therapeutic techniques and their efficacy.

关于治疗技术及其功效，今天同样的问题似乎是适当的。

One comic take on analysis is in Woody Allen’s film, Annie Hall, where he says, “I was in analysis. I was suicidal. As a matter of fact, I would have killed myself, but I was in analysis with a strict Freudian, and if you kill yourself, they make you pay for the sessions you miss.”

伍迪·艾伦 ( Woody Allen )的电影《 安妮·霍尔 ( Annie Hall) 》中对漫画进行了一种分析，他说：“ 我当时正在分析。 我自杀了。 事实上，我本来会自杀的，但是我一直在严格的弗洛伊德主义下进行分析，如果您自杀，他们会让您为错过的课程付费 。”

Truly psychotic patients were still chained to hospital walls and shuttered in back wards, but the couch and conversation soon became the medical “instrument” for treatment.

真正的精神病患者仍然被拴在医院的墙壁上，并被关在后面的病房里，但是沙发和谈话很快就变成了治疗的医疗“工具”。

The diagnostic method, in an age of advancing medical equipment, remained conversation with patients with questionable results. The issue of accuracy in diagnosis arose not merely because of the inability to communicate adequately, but inferences on the part of the clinician. Poor interviewing skills were also a sore point in psychiatric diagnosis.

在医疗设备不断发展的时代， 这种诊断方法仍然与患者交谈，结果令人怀疑。 诊断准确性的问题不仅是由于无法充分沟通而引起的，还在于临床医生的推论。 面试技巧差也是精神病诊断的痛点。

A need for a more accurate assessment for diagnostic determination was evident, as was the need for objective screening measures. But the accurate diagnosis, as determined by the DSM (Diagnostic & Statistical Manual of Mental Disorders), could still be a hit-or-miss proposition. Despite the guidelines in the manual, conversation was the tool that could prove its undoing.

显然，需要更准确的评估以进行诊断确定，同时也需要客观的筛查措施 。 但是，由DSM( 《精神障碍诊断与统计手册》 )确定的准确诊断仍然可能是命中注定的命题。 尽管手册中有指导原则，但对话是证明其成功的工具。

The situation was summed up by Tom Insel, MD, co-founder of Mindstrong Health and direction of the NIMH from 2002–2015. “The way we do diagnosis today is really pretty limited. It’s a little bit like trying to diagnose heart disease without using any of the modern instruments, like an EKG, cardiac scans, blood lipids, and everything else.”

Mindstrong Health的共同创始人兼NIMH从2002年至2015年的指导医学博士Tom Insel总结了这种情况 。 “ 我们今天进行诊断的方式确实非常有限。 这就有点像在不使用任何现代仪器(例如心电图，心脏扫描，血脂和其他任何东西)的情况下尝试诊断心脏病。”

Now the advent of artificial intelligence may be providing better guidelines and more accurate diagnoses in its ability to mine verbal data from psychiatric consultations as well as streaming data from smartphones.

现在，人工智能的出现可能会提供更好的指南和更准确的诊断，以挖掘来自精神科咨询的口头数据以及来自智能手机的流数据。

人工智能的深度学习遇到精神病学诊断 (AI’s Deep Learning Meets Psychiatric Diagnosis)

Computational psychiatry may prove to be the breakthrough between conversation and accurate diagnostic tools for psychiatric diagnosis. The problem is that there are no current biomarkers that would provide data for psychiatric disorders that have neurologic underpinnings. Apps are filling the void.

计算精神病学可能被证明是对话和用于精神病学诊断的准确诊断工具之间的突破。 问题在于，当前没有任何生物标志物可以提供具有神经基础的精神疾病的数据。 应用正在填补空白。

As has previously been noted, mental health disorders are not simple and usually combine many disorders in a spectrum presentation. The problem, therefore, is how to discover how these groups of disorders might present in a diverse population, which would then allow clinicians to make a diagnostic determination.

如前所述，心理健康障碍并不简单，通常会在频谱显示中合并许多疾病。 因此，问题在于如何发现这些人群的疾病如何在不同人群中出现，从而使临床医生能够做出诊断性决定。

Perhaps what will be found is an incredibly fractured series of disordered models that fit a few individuals. Such a state would then present an additional challenge to plan an effective, valid treatment for these individuals. The simplicity that was previously seen in the prior generations has now been revealed to be complexity. And complexity is best handled by AI.

也许会发现一系列令人难以置信的破碎的无序模型，适合几个人。 这样的状态将给计划这些人的有效治疗提供了额外的挑战。 以前在前几代人中看到的简单性现在已被揭示为复杂性。 AI可以最好地处理复杂性。

Psychiatry’s dependence on language has been found wanting, and new AI methods have begun to emerge to meet a serious need.

人们发现精神病学对语言的依赖是缺乏的，并且新的AI方法已经开始出现以满足严重的需求。

Evaluating patients’ verbal fluency by counting the number of unique words (e.g., animals) produced in a short-period (e.g., 1–3 min) is one of the most widely employed cognitive tests in psychiatric research.

通过计算短时间内(例如1-3分钟)产生的唯一单词(例如动物)的数量来评估患者的语言流利程度是精神病学研究中使用最广泛的认知测试之一。

New technology, specifically automatic speech recognition and natural language processing, can derive new metrics on temporal dynamics and semantic relationships in verbal fluency responses.

新技术，特别是自动语音识别和自然语言处理 ，可以在语言流利性响应中获得有关时间动态和语义关系的新指标。

Seeking such information is the path forward that researchers have embarked upon. There may be a few methods in terms of deep learning and artificial intelligence that are of use.

寻找此类信息是研究人员走上的道路。 就深度学习和人工智能而言，可能有一些可用的方法。

当前正在测试的程序 (The Current Programs Being Tested)

One system in a small study of community-based psychiatric patients used an interactive voice app called MyCoachConnect to sample patient activities. Its primary purpose was to allow patients to check in with their therapists and provide a sense of support.

在一项基于社区的精神病患者的小型研究中，一个系统使用了名为MyCoachConnect的交互式语音应用来对患者的活动进行采样。 其主要目的是让患者与治疗师联系并提供支持感。

Two other apps were either customized or used with minor application changes, mindLAMP, and BiAffect. The latter is a phone app that monitors mood and how it impacts cognition. The former is open source and stands for “Learn, Assess, Manage, Present.”

另外两个应用程序是自定义的，也可以进行较小的应用程序更改后使用， 它们是mindLAMP和BiAffect 。 后者是一个电话应用程序，可以监视情绪以及它如何影响认知。 前者是开源的，代表“学习，评估，管理，呈现”。

MindLAMP is utilized on a smartphone and can data stream surveys, cognitive tests, GPS, exercise, medication side effects, and mood and is customizable by the psychiatrist and patient working together. Fifteen institutions around the world are using this open-source app in their research.

MindLAMP用在智能手机上，可以进行数据流调查，认知测试，GPS，运动，药物副作用和情绪，并且可以由精神科医生和患者共同定制。 全球有15个机构在研究中使用此开源应用程序。

BiAffect was used in a study to assess patient’s fluctuations in speech. These researchers believed that a clinician wouldn’t have the degree of sensitivity to voice fluctuations, nor could it be quantified accurately, and something else had to be sourced.

BiAffect在一项研究中用于评估患者的言语波动 。 这些研究人员认为，临床医生对语音波动不会有足够的敏感度，也无法对其进行准确量化，因此必须寻找其他方法。

A number of clinical observations suggest that reduced speech activity and changes in voice features such as pitch may be sensitive and valid measures of prodromal symptoms of depression and effect of treatment.

许多临床观察表明，言语活动的减少和声音特征(例如音调)的改变可能是抑郁症的前驱症状和治疗效果的敏感而有效的度量。

Testing out these AI applications found them to be as accurate as clinicians in identifying persons with schizophrenia as opposed to healthy individuals. In other studies by additional researchers, tools such as the Natural Language Toolkit and the Kaldi speech recognition toolkit were used to make needed adjustments and assessments of speech. How can these new AI tools help?

测试这些AI应用程序后发现，它们在识别精神分裂症患者而非健康患者方面与临床医生一样准确。 在其他研究人员进行的其他研究中，使用了诸如自然语言工具包和Kaldi语音识别工具包之类的工具来进行必要的语音调整和评估。 这些新的AI工具如何提供帮助？

A prominent researcher in the field of neurocognitive disorders, Murali Doraiswamy, MD, believes the future of AI will fill an unmet need, a shortage of psychiatrists and therapists in the US, but moreso in poorer countries of the world.

医学博士Murali Doraiswamy博士是神经认知障碍领域的杰出研究者，他认为AI的未来将满足未满足的需求，美国精神病学家和治疗师的短缺，但在世界上较贫穷的国家中更是如此。

“You can use, for example, a multiclustering algorithm if you have 45 different types of information on a person — it could be genomic, could be biomarkers, could be brain scans, could be metabolomics, the microbiome. No individual human can actually go through all this to try to identify patterns, it would be too complicated.”

“例如，如果一个人有45种不同类型的信息，则可以使用多聚类算法 -它可能是基因组，可能是生物标记，可能是脑部扫描，可能是代谢组学(即微生物组)。 任何人都无法真正地通过所有这些过程来识别模式，这太复杂了。”

Language may still be the tool most useful in psychiatric evaluations. But it will now be assessed by algorithms written specifically to detect mental disorders as opposed to spotting typical concerns of living.

语言可能仍然是在精神病学评估中最有用的工具。 但是，现在将通过专门为检测精神障碍而不是发现典型的生活问题而编写的算法来对其进行评估。

其他参考： (Further references:)

Voice analysis as an objective state marker in bipolar disorder

语音分析作为双相情感障碍的客观状态标记

Furthering the reliable and valid measurement of mental healthscreening, diagnoses, treatment, and outcomes through healthinformation technology

通过健康信息技术进一步可靠 ，有效地衡量心理健康筛查，诊断，治疗和结果

Clinical state tracking in serious mental illnessthrough computational analysis of speech

通过语音计算分析对严重精神疾病的临床状态进行跟踪

App uses voice analysis, AI to track wellness of people with mental illness

应用程序使用语音分析 ，人工智能来跟踪精神疾病患者的健康状况

Deep learning in mental health outcome research: a scoping review

深度学习在心理健康结果研究中的作用域回顾