Siraj Raval has a video exploring a paper about genomics and creating reliable machine learning systems.

Deep learning classifiers make the ladies (and gentlemen) swoon, but they often classify novel data that’s not in the training set incorrectly with high confidence. This has serious real world consequences! In Medicine, this could mean misdiagnosing a patient. In autonomous vehicles, this could mean ignoring a stop sign. Machines are increasingly tasked with making life or death decisions like that, so it’s important that we figure out how to correct this problem! I found a new, relatively obscure yet extremely fascinating paper out of Google Research that tackles this problem head on. In this episode, I’ll explain the work of these researchers, we’ll write some code, do some math, do some visualizations, and by the end I’ll freestyle rap about AI and genomics. I had a lot of fun making this, so I hope you enjoy it!

Likelihood Ratios for Out-of-Distribution Detection paper: 

The researcher’s code:

A research paper from Google Health published in Nature magazine has reported that machine learning, based on Google’s TensorFlow algorithm, can be used to reduce the level of false positives in breast cancer scans.

In the Google Health paper, based on training an AI algorithm to identify breast cancer using a large representative dataset from the UK and the US, the researchers reported an absolute reduction of 5.7% in false positives in the US dataset. The UK dataset exhibited a 1.2% reduction in false positive results.

Here’s an interesting blog blost co-authored by Shweta Mishra and Vinil Menon, both of CitiusTech. CitiusTech is a specialist provider of healthcare technology services which helps its customers to accelerate innovation in healthcare.  CitiusTech used Azure Cosmos DB to simplify the real-time collection and movement of healthcare data from variety of sources in a secured manner.

With the proliferation of patient information from established and current sources, accompanied with scrupulous regulations, healthcare systems today are gradually shifting towards near real-time data integration. 

The rise of Internet of Things (IoT) has enabled ordinary medical devices, wearables, traditional hospital deployed medical equipment to collect and share data. Within a wide area network (WAN), there are well defined standards and protocols, but with the ever increasing number of devices getting connected to the internet, there is a general lack of standards compliance and consistency of implementation. Moreover, data collation and generation from IoT enabled medical/mobile devices need specialized applications to cope with increasing volumes of data.

Here’s an interesting article on how machine learning can make our healthcare system more efficient and improve patient outcomes.

Health system leaders, whom Mr. Garg has spoken with, estimate more than half of their patient visits are unscheduled, a problem that is not well-understood or analyzed by those outside of the health care industry. While there are tools available to help manage patient information, he’s seen firsthand how staff are too busy to sort through dashboards and extrapolate how to best adapt to the ever-changing environment in front of them. This often results in a frustrated patient experience, exhausted care teams and operational inefficiencies.