Participant: Accenture Federal Services Proposed Solution: Accenture Federal Services AI Challenge Geographic Location: Arlington, Virginia
Participant: Ann Arbor Algorithms Inc. Proposed Solution: Generalizing Time-to-event Algorithms to Deep Learning- based Prediction for CMS Data Geographic Location: Sterling Heights, Michigan
Participant: Booz Allen Hamilton Proposed Solution: Booz Allen Launch Stage Submission Geographic Location: McLean, Virginia
Participant: Columbia University Department of Biomedical Informatics Proposed Solution: The CLinically Explainable Actionable Risk (CLEAR) Model from Columbia University Department of Biomedical Informatics Geographic Location: New York, New York
Participant: Deloitte Consulting LLP Proposed Solution: Further, Faster: The Deloitte Team’s Approach to Harnessing the Power of AI to Improve Health Outcomes Geographic Location: Arlington, Virginia
Participant: Geisinger Proposed Solution: Reducing Adverse Events and Avoidable Hospital Readmissions by Empowering Clinicians and Patients Geographic Location: Danville, Pennsylvania
Participant: Health Data Analytics Institute Proposed Solution: HDAI’s Analytic Platform Technology for Healthcare Improvement Geographic Location: Dedham, Massachusetts
Participant: HealthEC, LLC Proposed Solution: Leveraging Artificial Intelligence to Predict and Improve Health Outcomes, Maximize Quality Improvement, and Reduce Costs Geographic Location: Edison, New Jersey
Participant: Hospital of the University of Pennsylvania Proposed Solution: The Intelligent Risk Project Geographic Location: Philadelphia, Pennsylvania
Participant: IBM Corporation Proposed Solution: AI for Explainable Adverse Event Prediction: Empowering Beneficiaries and Providers to Improve Health Outcomes Geographic Location: Yorktown, New York
Participant: Innovative Decisions Inc. (IDI) Proposed Solution: Multi-Modeling with Augmented Datasets for Positive Health Outcomes (MADPHO) Geographic Location: Vienna, Virginia
Participant: Jefferson Health Proposed Solution: Using AI to Improve Medicare Population Health, Optimize Ambulatory Scheduling, and Reduce Adverse Events at Hospitals Geographic Location: Philadelphia, Pennsylvania
Participant: KenSci Inc. Proposed Solution: Assistive Intelligence for Unplanned Admissions and Adverse Events Prediction Geographic Location: Seattle, Washington
Participant: Lightbeam Health Solutions, LLC Proposed Solution: AI Risk Predictions- preventing hospital, ER and SNF admissions Geographic Location: Irving, Texas
Participant: Mathematica Policy Research, Inc. Proposed Solution: The CPC+ AI Model by Mathematica Geographic Location: Princeton, New Jersey
Participant: Mederrata Proposed Solution: Boosting medical error and readmission prediction by leveraging Deep Learning, Topological Data Analysis, and Bayesian modeling Geographic Location: Columbus, Ohio
Participant: Merck & Co., Inc. Proposed Solution: Actionable AI to Prevent Unplanned Admissions and Adverse Events Geographic Location: Kenilworth, New Jersey
Participant: North Carolina State University (NCSU) Proposed Solution: Multi-Layered Feature Selection and Dynamic Personalized Scoring Geographic Location: Raleigh, North Carolina
Participant: Northrop Grumman Systems Corporation (NGSC) Proposed Solution: Reducing Patient Risk through Actionable Artificial Intelligence: AI Risk Avoidance System (ARAS) Geographic Location: Herndon, Virginia
Participant: Northwestern Medicine Proposed Solution: A human-machine solution to enhance delivery of relationship-oriented care Geographic Location: Chicago, Illinois
Participant: Observational Health Data Sciences and Informatics (OHDSI) Proposed Solution: OHDSI Submission Geographic Location: New York, New York
Participant: University of Virginia Health System Proposed Solution: Actionable AI Geographic Location: Charlottesville, Virginia
【 在 wdong (万事休) 的大作中提到: 】 : AAA应该是这批企业中per-capita intelligence最高的企业。 : Artificial Intelligence (AI) Health Outcomes Challenge : https://innovation.cms.gov/initiatives/artificial-intelligence-health- : outcomes-challenge/ : Participant: Accenture Federal Services : Proposed Solution: Accenture Federal Services AI Challenge : Geographic Location: Arlington, Virginia : Participant: Ann Arbor Algorithms Inc. : Proposed Solution: Generalizing Time-to-event Algorithms to Deep Learning-: based Prediction for CMS Data : ...................
AAA应该是这批企业中per-capita intelligence最高的企业。
Artificial Intelligence (AI) Health Outcomes Challenge
https://innovation.cms.gov/initiatives/artificial-intelligence-health-
outcomes-challenge/
Participant: Accenture Federal Services
Proposed Solution: Accenture Federal Services AI Challenge
Geographic Location: Arlington, Virginia
Participant: Ann Arbor Algorithms Inc.
Proposed Solution: Generalizing Time-to-event Algorithms to Deep Learning-
based Prediction for CMS Data
Geographic Location: Sterling Heights, Michigan
Participant: Booz Allen Hamilton
Proposed Solution: Booz Allen Launch Stage Submission
Geographic Location: McLean, Virginia
Participant: ClosedLoop.ai
Proposed Solution: Healthcare's Data Science Platform
Geographic Location: Austin, Texas
Participant: Columbia University Department of Biomedical Informatics
Proposed Solution: The CLinically Explainable Actionable Risk (CLEAR) Model from Columbia University Department of Biomedical Informatics
Geographic Location: New York, New York
Participant: CORMAC
Proposed Solution: CORMAC Response to Challenge Questions
Geographic Location: Columbia, Maryland
Participant: Deloitte Consulting LLP
Proposed Solution: Further, Faster: The Deloitte Team’s Approach to
Harnessing the Power of AI to Improve Health Outcomes
Geographic Location: Arlington, Virginia
Participant: Geisinger
Proposed Solution: Reducing Adverse Events and Avoidable Hospital
Readmissions by Empowering Clinicians and Patients
Geographic Location: Danville, Pennsylvania
Participant: Health Data Analytics Institute
Proposed Solution: HDAI’s Analytic Platform Technology for Healthcare
Improvement
Geographic Location: Dedham, Massachusetts
Participant: HealthEC, LLC
Proposed Solution: Leveraging Artificial Intelligence to Predict and Improve Health Outcomes, Maximize Quality Improvement, and Reduce Costs
Geographic Location: Edison, New Jersey
Participant: Hospital of the University of Pennsylvania
Proposed Solution: The Intelligent Risk Project
Geographic Location: Philadelphia, Pennsylvania
Participant: IBM Corporation
Proposed Solution: AI for Explainable Adverse Event Prediction: Empowering
Beneficiaries and Providers to Improve Health Outcomes
Geographic Location: Yorktown, New York
Participant: Innovative Decisions Inc. (IDI)
Proposed Solution: Multi-Modeling with Augmented Datasets for Positive
Health Outcomes (MADPHO)
Geographic Location: Vienna, Virginia
Participant: Jefferson Health
Proposed Solution: Using AI to Improve Medicare Population Health, Optimize Ambulatory Scheduling, and Reduce Adverse Events at Hospitals
Geographic Location: Philadelphia, Pennsylvania
Participant: KenSci Inc.
Proposed Solution: Assistive Intelligence for Unplanned Admissions and
Adverse Events Prediction
Geographic Location: Seattle, Washington
Participant: Lightbeam Health Solutions, LLC
Proposed Solution: AI Risk Predictions- preventing hospital, ER and SNF
admissions
Geographic Location: Irving, Texas
Participant: Mathematica Policy Research, Inc.
Proposed Solution: The CPC+ AI Model by Mathematica
Geographic Location: Princeton, New Jersey
Participant: Mayo Clinic
Proposed Solution: Claims-based Learning Framework (CBLF)
Geographic Location: Rochester, Minnesota
Participant: Mederrata
Proposed Solution: Boosting medical error and readmission prediction by
leveraging Deep Learning, Topological Data Analysis, and Bayesian modeling
Geographic Location: Columbus, Ohio
Participant: Merck & Co., Inc.
Proposed Solution: Actionable AI to Prevent Unplanned Admissions and Adverse Events
Geographic Location: Kenilworth, New Jersey
Participant: North Carolina State University (NCSU)
Proposed Solution: Multi-Layered Feature Selection and Dynamic Personalized Scoring
Geographic Location: Raleigh, North Carolina
Participant: Northrop Grumman Systems Corporation (NGSC)
Proposed Solution: Reducing Patient Risk through Actionable Artificial
Intelligence: AI Risk Avoidance System (ARAS)
Geographic Location: Herndon, Virginia
Participant: Northwestern Medicine
Proposed Solution: A human-machine solution to enhance delivery of
relationship-oriented care
Geographic Location: Chicago, Illinois
Participant: Observational Health Data Sciences and Informatics (OHDSI)
Proposed Solution: OHDSI Submission
Geographic Location: New York, New York
Participant: University of Virginia Health System
Proposed Solution: Actionable AI
Geographic Location: Charlottesville, Virginia
恭喜！很厉害。
十个字。
恭喜！发现四大里面有两家都出现了。
【 在 wdong (万事休) 的大作中提到: 】
: AAA应该是这批企业中per-capita intelligence最高的企业。
: Artificial Intelligence (AI) Health Outcomes Challenge
: https://innovation.cms.gov/initiatives/artificial-intelligence-health-
: outcomes-challenge/
: Participant: Accenture Federal Services
: Proposed Solution: Accenture Federal Services AI Challenge
: Geographic Location: Arlington, Virginia
: Participant: Ann Arbor Algorithms Inc.
: Proposed Solution: Generalizing Time-to-event Algorithms to Deep Learning-: based Prediction for CMS Data
: ...................
恭喜恭喜！了不起！
看了十分钟， 到底用啥预测啥都没看明白。 网站还挺慢。 http://dev.cmschallenge.com/timeline/challenge-stages/
这类数据集，DL比传统ML办法(SVM, boost decision tree, etc )优势在什么地方？
同问，除了AAA以外还有一家用了 deep learning. Mederrata 应该也是一个体户在做
，不知道会不会在这版上。
【 在 guvest (我爱你老婆Anna) 的大作中提到: 】
: 这类数据集，DL比传统ML办法(SVM, boost decision tree, etc )优势在什么地方？
其实还是针对数据的特点想办法，然后在DL或者传统ML的框架下实现。
DL的优势就是比传统方法更加flexible，能比较方便地实现更多的想法。
【 在 guvest (我爱你老婆Anna) 的大作中提到: 】
: 这类数据集，DL比传统ML办法(SVM, boost decision tree, etc )优势在什么地方？
congrats~
问一下 为什么没有湾区公司 great DC area倒是出奇的多
Cong~
厉害
【 在 wdong (万事休) 的大作中提到: 】
: AAA应该是这批企业中per-capita intelligence最高的企业。
: Artificial Intelligence (AI) Health Outcomes Challenge
: https://innovation.cms.gov/initiatives/artificial-intelligence-health-
: outcomes-challenge/
: Participant: Accenture Federal Services
: Proposed Solution: Accenture Federal Services AI Challenge
: Geographic Location: Arlington, Virginia
: Participant: Ann Arbor Algorithms Inc.
: Proposed Solution: Generalizing Time-to-event Algorithms to Deep Learning-: based Prediction for CMS Data
: ...................
精力无限啊你
我搞了个超牛的chief scientist弄的。
我自己最近一年一直在做物流。最近对仓库设计已经很有心得了。
就是不知道产品怎么卖。
【 在 goodtudou (goodtudou) 的大作中提到: 】
: 精力无限啊你
很牛啊。
大佬们隆隆炮响，给生物医学领域送来了AI。
卖产品是销售的事啊。应该不用你操心。国内老板们神通广大。假如几家自己投资的公司互
相倒一下销售额。过几年我估计你们就可以筹划上市了。
【 在 wdong(万事休) 的大作中提到: 】
<br>: 我搞了个超牛的chief scientist弄的。
<br>: 我自己最近一年一直在做物流。最近对仓库设计已经很有心得了。
<br>: 就是不知道产品怎么卖。
<br>
大神在做哪方面的仓库设计？能多介绍一下吗？
【 在 wdong (万事休) 的大作中提到: 】
: 我搞了个超牛的chief scientist弄的。
: 我自己最近一年一直在做物流。最近对仓库设计已经很有心得了。
: 就是不知道产品怎么卖。
Congratulations!