对冲基金分析员用深度学习诊断心脏状况
http://blogs.wsj.com/venturecapital/2016/03/30/hedge-fund-analysts-use-deep-learning-to-diagnose-hearts-condition/
ByCAT ZAKRZEWSKIMar
30, 2016 9:11 pm ET
Two quantitative analysts using artificial intelligence in an
online data science competition showed they could diagnose heart disease about
as accurately as doctors.
在线数据科学比赛中应用了人工智能后，两位定量分析员发现，他们能够像医生一样准确地诊断心脏病了。
Qi Liu and Tencia Lee, hedge fund analysts and self-described
“quants,” built the winning algorithm in the competition, which could find indicators
of heart disease. The online data contest challenged participants to develop
machine algorithms that could measure cardiac volumes from MRIs provided by the
National Heart, Lung and Blood Institute.
两位自称为“数量分析专家”的对冲基金分析员，刘秦（音）和李特西亚（音）开发了这种能够发现心脏病的指向标获胜算法。这次在线数据竞赛要求参与者开发出一种机器算法，这种算法要能够从美国国立心肺血液研究院（the National Heart, Lung and Blood
Institute）提供的核磁共振成像影像中测量出心容积。
Mr. Liu and Ms. Leedidn’t know each other before they won the competition, beating out more than1,390 algorithms. They met each other in a forum on the Kaggle site, where thecompetition was hosted over a three-month period.
在打败其他超过1390算法赢得比赛前，刘先生和李女士并不认识彼此。他们是在一次Kaggle网站的论坛上遇到的，也正是Kaggle举办了这次为期超过三个月的数据科学比赛。
“We decided to combine our methods,” Ms. Lee said. “We decided
they were different enough from each other that we could do better than either
of us would alone.”
“我们决定把我们的方法结合起来”李女士说。“相比较而言，我们觉得我们的算法足够与众不同，所以如果我们两人合作的话，效果会比独自一个人单独做更好。”
Competitors worked on algorithms that could accomplish a manual
and slow process that normally is carried out by cardiologists. Usually it
takes doctors about 20 minutes to measure cardiac volumes and derive ejection
fraction data from an MRI. The algorithms can analyze the images much more
quickly.
在这次比赛中，参赛选手们被要求开发出一种算法，以实现通常要心脏病专家手动且缓慢才能做到的过程。通常想要做到这些，医生需要通过核磁共振成像技术，花费大约二十分钟，才能检测出心容积并得到射血分析数据。而在应用了算法以后，则可以更快分析出这些影像。
The data scientists had a set of more than 1,000 MRIs to work
with. Ms. Lee said the winning algorithm used a technique called deep learning.
在比赛中，数据科学家需要研究一组超过一千张的核磁共振成像照片。李女士说，他们在算法中是运用到了深度学习技术才得以获胜的。
The National Heart, Lung and Blood Institute will now test the
algorithm in clinical environments. Ms. Lee said she hopes one day the
algorithm can be used by health care professionals, but she said there is a
long road of testing and regulatory processes before it gets there.
现在，美国国立心肺血液研究院正在临床环境中测试这套算法。李女士说，她希望有以天，医疗保健专业人士能够运用到这套算法，但是她也表示，在那一天到来之前，这套算法的测试和规范过程还有很长的路要走。
“I certainly wouldn’t trust my doctor with what we just wrote,”
Ms. Lee said.
“我肯定不会相信我的医生只是运用了我写的算法。”李女士说。
Many companies are trying to use artificial intelligence to
improve medicine. Investors are increasingly backing startups in the category,
and public technology companies have signaled it is a sector they’re betting
on. Last year International Business Machines Corp. acquired Merge Healthcare
Inc. for $1 billion, according to The Wall Street Journal.
近些年来，许多公司正在试图运用人工智能去改进药物。支持这一领域创业公司的投资者正在不断增长，与此同时，公共科技公司也放出信号说，他们也将会把赌注押在这一领域。据华尔街日报报道，去年，IBM就以10亿美金收购了医学成像及临床系统供应商Merge Healthcare Inc，并将其与旗下沃森健康（Watson Health）部门合并。
The contest, the National Data Science Bowl, is sponsored by the
data science startup Kaggle and Booz Allen Hamilton.
这场国家数据科学碗（the National Data Science Bowl）比赛，是由数据科学创业平台Kaggle和博思艾伦咨询公司（Booz Allen Hamilton）赞助的。
This is the second time Kaggle has hosted a National Data
Science Bowl. The first competition challenged scientists to come up with
algorithms that could measure plankton population to predict global health.
这是Kaggle第二次举办国家数据科学碗比赛。第一次比赛是要求科学家们开发一种能够通过检测浮游生物数量来预测全球健康的算法。
Kaggle Chief Executive AnthonyGoldbloom said the winner of the firstNational Data Science Bowl now works as a research scientist at Google’sDeepMind. He said Kaggle competitions, which crowd source solutions to big dataproblems, have become a way for data scientists to quantify their skills whenapplying for jobs.
Mr. Goldbloom said the company is now seeking proposals for the
third competition’s challenge.
Kaggle的首席执行官安东尼·古德鲁姆说，第一次国家数据科学碗的获胜选手现在正在谷歌Deepmind做研究科学家。他说，Kaggle的竞赛能够为大数据问题众筹解决方案，这也成为数据科学家们申请工作时量化他们能力。
古德鲁姆先生说，公司正在为第三次竞赛寻找提案。
Write to Cat Zakrzewski at
[email protected]. Follow her on Twitter at @Cat_Zakrzewski.