Big Data See expressed: support pregnancy tracking

Ovuline is a prenatal tracking device based on data analysis. It was previously funded by http://www.aliyun.com/zixun/aggregation/16739.html ">lightbank, Launch Capital and other companies with a total of 1.4 million dollars." Today, they upgraded their intelligent pregnancy products to make their ovulation predictions more accurate and integrate self-service quantization devices such as Fitbit and Withings to provide real-time feedback.

The company's founder and chief executive, Paris Wallace, founded Good Start Genetics at Harvard Business School. The company in Boston has just announced that they are getting 28 million of the investment. During the 4-year development of the company, Wallace came into contact with a number of fertility-related things that gave him the ability to solve the public health problem that he considered to be the least discussed in the United States. In the United States, there are 7.3 million of men and women with infertility problems, and usually they can only bear it silently.

Ovuline's goal is to allow women to increase their chances of conceiving in non-traditional ways, such as the basal body temperature mapping method.

Most service or mobile applications now use very basic tools to monitor and track women's ability to conceive, but these are either algorithms that are based on a female's physiological cycle, or provide spreadsheets that allow women to manually enter data such as basal body temperature. But ovuline a step further.

It not only allows manual input of various data related to female fertility, such as basal body temperature, cervical fluid analysis, ovulation test results, physical symptoms, emotional conditions, and sleep, weight, nutrient intake and daily activity, it also supports self-service quantification devices such as FitBit. It can automatically collect data, such as sleep cycles.

Since its launch in June 2012, the number of users has grown to 50,000, and the amount of data related to services has reached 2.5 million. These data are critical to the correct prediction of Ovuline. It uses machine learning methods and a large amount of data to establish a special algorithm to more accurately predict the ovulation period for women-the best time to conceive.

Now that you have the data, ovuline can make real-time predictions when women enter data and submit it. This means that the service (either on the Web or on the mobile side) can immediately tell the end user where it is today and what to do next.

The more important question, of course, is whether this approach is going to work. Wallace said the company has received thousands of emails from users thanking them for their successful pregnancies after using the service. Sometimes you get 10 to 15 emails a day. In fact, the company's most successful case is not from the user, but from within the company: the company's chief technology officer and machine learning expert Alex Baron.

"Most families start with--you know, ooxx--, but this kid started building algorithms to predict his wife's ovulation. Wallace smiled and said. Their child, Michael, is now 4 months old. ）

An interesting data related to the rate of successful pregnancies is that the average pregnancy user becomes pregnant within 60 days of becoming a Ovuline member. ' The average speed of the country is 4-6 months, and we are 3 times times the average speed, ' says Wallace.

Soon, Ovuline will provide services to those Members who have just been pregnant. Later this year, they will develop a pregnancy tracking application that will also integrate self-help quantization devices to monitor key health indicators, such as daily activity levels and blood pressure, to help women spend a healthy period of pregnancy. In addition, this application and the pregnancy tracker mentioned earlier will support more devices in the future.

(Responsible editor: The good of the Legacy)