Flashback gets $2.1M to make sense of "dirty" medical data via machine learning

Garrett Reim

File 31067

Flashback Technologies, a medical data machine learning startup, has just raised a $2 million Series A round. The round was led by private equity firm PAC Partners and included financing from former investors. In total the company has raised $12 million. Flashback has a patent pending software program called CipherSensor, which it claims is more able than previous software systems to analyze terabytes of so-called “dirty” medical data. 

“We are very excited about closing this most recent round of financing, the timing and success of which is a terrific validation of our capability to deliver a ground-breaking technology to the healthcare marketplace”, said Gordon Van Dusen, president & CEO.

Initially, Flashback aims to use CipherSensor to help make treatment decisions for patients in trauma and critical care facilities, because the complex set of variables swirling around those circumstances tend to make data analysis difficult.

The software can collect and analyze data via machine learning in “areas where data is complex, high volume and ‘dirty,’ areas where other data analytic solutions have often proven ineffective,” said founder and chief technology officer, Greg Grudic.

To make sense of large, chaotic data sets, the company claims its CipherSensor software takes a more agnostic and open-minded approach to looking at data. As such, the company claims it is better at analyzing large volumes of data where the bias of other software analytics programs may have missed insights or provided false conclusions.

“The CipherSensor Technology analyzes these large data streams without any knowledge of their origin or potential role, but rather in the context of the desired outcomes or criteria of success in which the data streams exist,” said Flashback’s website. “The CipherSensor computational engine consumes massive amounts of information, determines the relevant information in the dataset based on its relationship to the outcome of interest, and utilizes that information to create predictions of how likely the outcome of interest is at any given time.”

The company doesn’t plan to launch its first product until the end of 2015; it is sustaining itself on investment capital. Funding from this Series A round will be used to build infrastructure, hire additional staff, and provide additional working capital. 

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