Health Care

Analytics
Sandra Hernandez 0
Experimente las nuevas posibilidades: Precisión al ver con claridad

Ya no se trata de imaginar cosas. Cada día las empresas enfrentan miles de desafíos. Desde decisiones de negocio hasta procesos operativos, pasando por la manera de relacionarse con sus clientes o de preparar los informes de cumplimientos regulatorios o cuidarse de los ataques o fraudes. No son escenarios que

Data Visualization
Sanjay Matange 0
Category highlighting

When presenting information in form of a graph we show the data and let the reader draw the inferences.  However, often one may want to draw the attention of the reader towards some aspect of the graph or data.  For one such case, a user asked how to highlight one

Advanced Analytics | Machine Learning
Andreas Becks 0
Stop Algorithm Porn! Warum Machine Learning alleine kein Wundermittel ist

In meinem vorherigen Beitrag  ging es darum, wie sich das Internet of Things (IoT) über den aktuellen Hype hinaus geschäftsfähig machen, also operationalisieren, lässt. Und um die Hürden, die Unternehmen in Sachen Analytics dafür überwinden müssen. Immer wieder spreche ich in diesem Zusammenhang mit Kunden über ein Thema, das nicht

Machine Learning
Jeanne (Hyunjin) Byun 0
머신러닝 + 웨어러블 의료 기기 = 더욱 건강한 미래

미국의 최고 권위의 과학자인 버니바 부시(Vannevar Bush)가 기계가 생각하고 학습하는 미래를 예측한 것이 1945년이었습니다. 당시까지만 해도 과학적 공상으로만 보였던 것이 이제는 Google 검색 결과 같이 평범한 사물까지도 머신러닝의 산물이 되고 있습니다. 넷플릭스(Netflix)는 머신러닝을 사용하여 개인 맞춤형 영화 추천 서비스를 제공하고 있습니다. eHarmony는 머신러닝을 통해 사랑까지 수량화하여 예측합니다. 은행들은 사이버 감시를 비롯해 사기 및 악용

Work & Life at SAS
Cheryl Wheelock 0
Diabetes Alert Day

The American Diabetes Association recognizes March 28, 2017 as Diabetes Alert Day. The intention of this day is to bring awareness to the prevalence of Type 2 Diabetes in America. http://www.diabetes.org/are-you-at-risk/alert-day/ I have a strong family history of type 2 diabetes, which throughout the years has motivated me to learn

Data Visualization
Sanjay Matange 0
Getting Started with SGPLOT - Part 4 - Series Plot

This is the 4th installment of the Getting Started series.  The audience is the user who is new to the SG Procedures.  Experienced users may also find some useful nuggets of information here. Series plots are frequently used to visualize a numeric response on the y-axis by another numeric variable on

Analytics | Machine Learning | Risk Management
Christian Engel 0
Data Scientists im Gesundheitswesen – Chancen im morbiditätsorientierten Risikostrukturausgleich

Seit 2009 sollen der Gesundheitsfonds und der morbiditätsorientierte Risikostrukturausgleich in der deutschen gesetzlichen Krankenversicherung (GKV) für eine ausgewogenere Verteilung der Einnahmen bei den Kassen sorgen. Ziel ist ein sozialer Ausgleich für unterschiedliche Einkommensstruktur und Krankheitslasten bei den Mitgliedern. Über einen sehr interessanten Nebeneffekt, den dieses regulatorische System ungewollt ausgelöst hat,

Artificial Intelligence | Machine Learning
Alison Bolen 0
12 machine learning articles to catch you up on the latest trend

Machine learning is a type of artificial intelligence that uses algorithms to iteratively learn from data and finds hidden insights in data without being explicitly programmed where to look or how to find the answer. Here at SAS, we hear questions every day about machine learning: what it is, how it compares to

Analytics | Fraud & Security Intelligence
John Stultz 0
What can agencies learn from massive Medicaid fraud busts?

On June 22nd, the U.S. Department of Justice announced the largest Medicaid fraud bust in history. The National Health Care Fraud Takedown included 301 defendants charged, $900 million in false billings, 61 medical professionals and 29 doctors, across 36 states. In another case, investigators in New York uncovered more than

Data for Good | Data Management
Dan Stevens 0
A playbook for analyzing real world intelligence in a health care setting

Real world data collected in a functioning health care setting instead of a controlled clinical environment can provide opportunities for new and deeper insights across life science and health care organizations. However, managing, analyzing and extracting actionable information from the varied available sources can present unique challenges. The sheer size of these

1 6 7 8 9 10 11