Systems of Intelligence based on our paradigm “mobile-first, cloud-first”: this is not just a claim. It is inspiring a wave of innovation in medical advance, research & development and patient treatment. The Systems of Intelligence drive the digital transformation across all aspects of chemical, pharmaceutical, life sciences and health care organizations, and change the way to engage with customers, optimize operations, transform products and empower employees. And the best thing is: every inspiring project triggers numerous others. Sounds a bit enthusiastic? Just give it a chance!
Let’s start with the first example diabetes care. Today, we look at diabetes management as more than just reading blood sugar to connecting an insulin pump through IOT (Internet of Things). It is about “decoding” the entire patient journey from life style and diet, exercise, treatment and diagnostics. With this we can apply advanced analytics and machine learning on all data allowing to predict risks, necessary changes in therapy, provide new personalized services “beyond the pill” for patients as well as generate new insights about the disease for researchers.
That said, have a look at the diabetes project supporting parents for remote monitoring the glucose level of their children with diabetes Type 1 in real-time and send them advice if they need to have a snack or take a rest.
Machine Learning to Improve Multiple Sclerosis Therapies
Another exciting innovation is the collaboration between Microsoft Research’s lab in Cambridge, UK, and Novartis on our research project to improve the way Multiple Sclerosis patients are assessed through the Expanded Disability Status Scale (EDSS).
Novartis’ goal was to support the doctors getting a more consistent reading of how a patient performed on each of the tests, bringing a new level of uniformity that would help doctors better assess the progress of the disease. That, in turn, could speed up the process of getting the right treatments to patients. For Microsoft it was the unique opportunity to break new ground in the field of machine learning while also helping patients get treatment faster with a more standardized MS test. Our new video illustrates the joint announcement.
Less Hospital Readmission Using Machine Learning and Artificial Intelligence
Chronic conditions such as diabetes, chronic obstructive pulmonary disease (COPD), and congestive heart failure (CHF) often cause hospital readmission, which is in the vast majority of cases avoidable with accurate prioritization and personalized care protocols. One leading solution example in this area is RaaS (Readmission Score as a Service), a chronic care management predictive platform that was developed by the University of Washington (UW) Tacoma’s Center for Data Science.
RaaS compares a patient’s medical information to a database of heart-failure outcomes, using advanced machine learning techniques to arrive at a risk-of-readmission factor as well as corresponding actionable guidelines for the patient-provider team. The hundreds of machine learning models of RaaS have been developed by using both the R statistical computing programming language, and Microsoft Azure Machine Learning. RaaS relies on historical patient data from multiple sources, including anonymized electronic medical records, claims, labs, medications, and psycho-social factors, all labeled with observed outcomes that the machine learning models access and share in sync to provide continuous monitoring for personalized patient alerts.
Molecular Informatics and Next Generation Sequencing (NGS)
A fundamental big data challenge Microsoft is working on is related to Next-generation sequencing (NGS) technologies, which generate data at a rate much faster than that of the growth of compute and storage capacity. Researchers predict that the amount of genomic data will exceed that of YouTube, and genomic computations will exceed 5 million compute days per year by 2020.
By the end of 2016, Microsoft will host large, open Genomics data sets on the Microsoft Azure cloud platform for the first time at no cost. Such datasets, including 1000 Genomes, will be valuable for researchers and practitioners at universities, hospitals, and other health institutions who are making precision medicine a reality.
The overall umbrella in this exciting area is “Molecular Informatics”. Here just a few insights into current developments:
Bio Model Analyzer is a sketching tool that enables users to draw out a biological system of interest (e.g. a genetic regulatory network) by dragging and dropping cells, their contents (DNA, proteins, etc.), extracellular components and relationships onto a simple canvas.
Azimuth is a machine learning system that allows researchers to more quickly and effectively use the powerful gene editing tool CRISPR. It predicts, which part of a gene to target when a scientist wants to knockout – or shut off – a gene. Machine learning enables the model to make predictions for any gene of interest, including those not seen in the experimental training data.
Project Premonition is a system that aims to detect infectious disease outbreaks before they become widespread, with the goal of preventing major health disasters. Project Premonition seeks to detect pathogens in animals before these pathogens make people sick. It does this by treating a “mosquito as a device” that can find animals and sample their blood.
Virginia Tech boosted research with high-performance cloud computing. Provide storage and processing resources to handle exponentially increasing data and accelerate speed to insight by Cloud based data analysis. “Azure is enabling us to keep up with the data deluge in the DNA sequencing space. “We’re not only analyzing data faster, but analyzing it more intelligently.” said Wu Feng, Professor of Computer Science.
Outlook for Systems of Intelligence
We are just beginning to see the potential of the Systems of Intelligence by integrating devices, services and data in life sciences and healthcare. With big data analysis, researchers can better identify the right patients to enroll in clinical trials, resulting in smaller, shorter, less expensive, yet more powerful trials. Similarly, predictive modeling of biological processes and drug effects could result in better identification of potential molecules with high probability of successful development into future drugs.
In terms of how it all connects, think of our devices as part of a broader ecosystem. By connecting devices such as the Microsoft Band or Hololens to the cloud, you can collect information about users and their environment, analyze the data in the cloud (potentially in real time against large datasets), and provide feedback to the user that modifies their behavior. This could be used in a diagnostic setting, in research and development, in manufacturing, or even by patients.
To conclude, a great example of this is Microsoft Cognitive Services. A core Microsoft developer living in London, who lost the use of his eyes at age seven, found inspiration in computing and is helping build Seeing AI, a research project that helps people who are visually impaired or blind to better understand who and what is around them Introducing the Seeing AI app.
I bet, now you share my enthusiasm!