Cure her of that! Canst thou not minister to a mind diseased, pluck from the memory a rooted sorrow, raze out the written troubles of the brain, and with some sweet oblivious antidote cleanse the stuffed bosom of that perilous stuff which weighs upon her heart — Macbeth, William Shakespeare

治愈她！ 您不能服侍患病的心灵，从记忆中抽出根深蒂固的悲伤，消除大脑的书面烦恼，并用一些甜美而无用的解毒剂来清除那颗充塞在她心中的危险物质的馅怀—麦克白，威廉·莎士比亚

The current state of big data in psychiatry has evolved from the epidemiologic foundation reported in 1984 and laid well before. The foundation was built on the development of defined diagnosis, diagnostic assessment tools that could be used in large samples…

精神病学大数据的现状是从1984年报道的流行病学基础发展而来的。 该基础建立在可用于大样本的定义的诊断和诊断评估工具的开发上……

Psychiatry is an evolving medical profession that is in the process of investigating the utility of complex data sets to solve complex human mental health disorders. One of the main stumbling blocks, which is quickly apparent, is that the data is biased by the fallible humans who originally entered it into any records.

精神病学是一个不断发展的医学专业，正在研究复杂数据集用于解决复杂的人类心理健康疾病的效用。 显而易见的主要绊脚石之一是，数据受最初输入任何记录的易犯错误的人的偏见。

Unlike other disciplines in medicine that depend on physical measures such as blood pressure, serologic analysis, or imaging, psychiatry is dependent on communication. Language and how it is used or interpreted is Alice’s rabbit hole. Biological psychiatry may have changed the field, but it is still heavily dependent on language.

与其他依靠血压，血清学分析或影像学等物理测量方法的医学学科不同，精神病学依赖于沟通。 语言以及如何使用或解释它是爱丽丝的兔子洞。 生物精神病学可能已经改变了这个领域，但是它仍然严重依赖语言。

Depending on the accuracy of the original records can be an algorithmic nightmare. Accept the data as it is provided, knowing that there’s no way to correct it, and you build bias into the database. How can you screen this bias out to ensure a cleaner endproduct?

取决于原始记录的准确性，可能是算法上的噩梦。 接受没有提供的数据，请接受提供的数据，然后在数据库中建立偏见。 您如何筛选出这种偏差以确保最终产品更清洁？

一个错误信息的临床试验实例 (One Clinical Trials Example of Misinformation)

I was once charged with traveling to 17 research sites in the United States in my role as the clinical monitor to ensure the accuracy of administration and scoring of a test for cognition in Alzheimer’s patients.

我曾经以负责临床监测的角色被带到美国的17个研究地点，以确保对阿尔茨海默氏病患者进行认知测试的给药和评分的准确性。

In that role, I saw psychologists, psychiatrists, and advanced-practice nurses who were to provide data from their scoring of our test materials.

在这个角色中，我看到了心理学家，精神病学家和高级实践护士，他们将从他们对我们测试材料的评分中提供数据。

One portion of the test required that the patient identify objects presented to them on a table. A specific item, a Lone Ranger-type mask, has only one correct answer, a mask. Any other response would be scored as incorrect and an indication of impaired recognition of the target item.

测试的一部分要求患者识别桌子上呈现给他们的物体。 特定项目，即“ 独行侠”型面具，只有一个正确答案，即面具。 任何其他响应都将被评为不正确，并表明目标项目的识别能力受损。

The test had been designed and validated by psychologists working at a major medical center on the East Coast of the US. This fact led to problems for any patient who was tested in the Southern States. In Midwestern states, a similar situation arose with another item.

该测试是由在美国东海岸的一家大型医学中心工作的心理学家设计和验证的。 这一事实给在南部各州接受检查的任何患者带来了麻烦。 在中西部各州，另一项目也出现了类似情况。

In the South, door knockers were often shaped in a mask-like configuration, and they are called not a “mask” but a “door face.” Southerners who gave this regionally-correct response were scored against in the test. They lost points.

在南方，门环通常做成类似面具的形状，它们不是“ 面具 ”，而是“ 门面 ”。 做出此区域正确答案的南方人在测试中被打分。 他们失去了分数。

A similar problem arose with one or two other items in the test where regional expressions were given rather than the East Coast-equivalent of it. Bias personified.

测试中的其他一个或两个项目也出现了类似的问题，其中给出的是区域表达方式，而不是等同于东海岸地区的表达方式。 偏见人格化。

How many patients in clinical trials of various medications receive incorrect scoring because of regional bias or language differences?

多少种因区域偏倚或语言差异而在各种药物的临床试验中获得错误的评分？

患者临床访谈 (Clinical Interviews of Patients)

Psychiatric patients come from all walks of life, different educational levels, and cultures. Some of them have accents that are misinterpreted by clinicians who then make inaccurate notes. These notes will become part of a clinical record prime for database inclusion.

精神病患者来自各行各业，不同的教育水平和文化。 他们中有些人的口音被临床医生误解，然后他们做出了不准确的记录。 这些注释将成为临床记录中包含数据库的主要内容。

I sat with a psychiatrist who grew up outside the United States. She was interviewing a Latino patient regarding a clinical matter. During the interview, the man used a street term for another patient, and the psychiatrist thought he said something else.

我和在美国以外长大的精神科医生坐在一起。 她正在就临床问题采访一名拉丁裔患者。 在面谈中，该男子用街头术语称呼另一位患者，精神科医生认为他还说了别的话。

He hadn’t, and I knew it because I was familiar with the word and how inflammatory it could be. What did she write in her notes? He was belligerent and argumentative. He wasn’t.

他没有，我知道这是因为我熟悉这个词以及它可能具有的煽动性。 她在笔记中写了什么？ 他好战又有争议。 他不是。

He was making a case for why he had been provoked into some action, but she didn’t see it that way — classic culture clash with resulting consequences for him. I had to explain to her what the term meant and the reason it was so offensive.

他提出了一个理由，说明为什么他被激怒了，但她却不这样认为-古典文化与他产生的后果发生了冲突。 我不得不向她解释这个词的含义以及它如此令人反感的原因。

Patients in psychiatric hospitals who are “out of control” and lash out are medicated and put in a quiet room. These rooms are often referred to as a “padded cell” although they don’t always have padding. It was a mistake in this instance. His chart then had a notation about this behavior and the intervention. All of it goes into a clinical database.

对精神病医院中“失控”并受到强烈抨击的患者进行药物治疗，并将其放在安静的房间里。 尽管这些房间并不总是具有填充物，但它们通常被称为“ 填充单元 ”。 在这种情况下，这是一个错误。 然后，他的图表就对此行为和干预进行了注释。 所有这些都进入临床数据库。

人工智能和大数据的惊人力量 (The Amazing Power of AI and Big Data)

A massive trove of clinical data is available worldwide, but much of it, from the previous decades, is in the form of handwritten notes. How can these notes be retrieved in a data-receptive form to be included in a database?

全世界都有大量的临床数据，但是从前几十年开始，其中大部分是以手写笔记的形式出现的 。 如何以可接收数据的形式检索这些笔记以包含在数据库中？

In fact, progress has been made in AI’s deciphering and utilizing handwriting. One of the advances is in the MNIST database. But this still leaves unresolved the initial interpretation by the clinician of the patient’s psychiatric disorder.

实际上，在AI的解密和利用手写方面已经取得了进展。 进步之一是在MNIST数据库中 。 但这仍然使临床医生对患者的精神疾病的初步解释仍未解决。

Until deep learning has the ability to recognize the hundreds or thousands of clinicians’ scribbles, much of the data, biased or not, is lost to us.

除非深度学习能够识别成千上万的临床医生的涂鸦，否则许多数据(无论是否带有偏见)都将丢失给我们。

But now clinicians are utilizing software to encode their session or testing notes, and this creates a more straightforward path for analysis. Symptoms and diagnoses are one area where AI can genuinely prove its mettle.

但是现在，临床医生正在使用软件对他们的会议或测试记录进行编码，这为分析提供了更直接的途径。 症状和诊断是AI可以真正证明其勇气的领域之一。

The algorithm can quickly retrieve, analyze, and make determinations about factors unseen by the clinician. All of this is done at lightning speed for instant incorporation or a warning regarding the conclusions the human calculated. Hal’s ever-watchful eye is there to aid, not override in the best-case scenario.

该算法可以快速检索，分析和确定临床医生看不见的因素。 所有这些都是以闪电般的速度完成的，以便立即加入，或者发出有关人类计算得出的结论的警告。 Hal时刻保持警惕 ，可以帮助您，但在最佳情况下不能忽略。

Churning data from thousands of sources may uncover relationships unseen and suggest a new direction for research or treatment. This is one of the gems of AI in psychiatry.

从数以千计的来源中收集数据可能会发现看不见的关系，并为研究或治疗提出新的方向。 这是人工智能在精神病学领域的瑰宝之一。

Why try four different medications for a specific mental illness when AI could pick out the most promising initially? Now, it is a trial-and-error method despite advances in the genetic predisposition for psychiatric illness.

当AI最初可以挑选出最有希望的方法时，为什么要针对特定​​的精神疾病尝试四种不同的药物呢？ 现在，尽管精神疾病的遗传易感性有所提高，但它仍然是一种反复试验的方法。

精神病学和人工智能的美好未来？ (A Rosy Future for Psychiatry and AI?)

The efforts to contain the COVID-19 virus include wearing masks, and here is a similarity to psychiatric databases. We cannot have clean data, free from bias if we don’t have clinicians who are sufficiently skilled in interviewing and interpreting both tests as well as language/cultural aspects of patients. The computer expression of “garbage in, garbage out” is significantly salient in this regard.

遏制COVID-19病毒的工作包括戴口罩，这与精神病学数据库相似。 如果我们没有足够的医师会见和解释测试以及患者的语言/文化方面的技能，那么我们就无法获得没有偏见的干净数据。 在这方面，“ 垃圾进，垃圾出 ”的计算机表达非常重要。

I’ve seen psychiatrists, untrained in psychological testing, administer tests with faults that any doctoral supervisor would have corrected. But the psychiatrists don’t have this type of supervision, and they shouldn’t be using tests for which they have questionable competence. Even the inter-rater reliability of some psychiatric tests is of concern.

我见过未经心理测试培训的精神科医生，对任何博士生导师会纠正的缺陷进行测试。 但是精神病医生没有这种监督，他们不应该使用他们的能力有问题的测试。 甚至某些精神病学检查的评分者间可靠性也值得关注。

In one instance, the psychiatrist was giving body cues regarding the answers he expected from the patient. He didn’t realize he was invalidating the testing. What were the consequences for that patient? I don’t know.

在一种情况下，精神科医生正在就他期望从病人那里得到的答案给出身体暗示。 他没有意识到自己正在使测试无效。 对那个病人有什么后果？ 我不知道。

The future use of AI in all of medicine and psychiatry, specifically, can be a promising one if the inherent bias is addressed before the databases begin to affect people’s lives in untoward ways.

如果在数据库开始以不良的方式影响人们的生活之前解决了固有的偏见，那么未来在所有医学和精神病学中使用AI可能是一个有前途的方法。