Approach

Our goal is to provide streamlined solutions driven by cutting-edge of AI technology that lead the industry in power and ease-of-use. The success behind all of our solutions lies in our expert team and the proprietary UPx method which lies at the heart of our process.

UPx Platform (Unsupervised, Physics Driven, and Explainable):

UPx represents the proprietary approach we apply to our universe of platforms and algorithms. Carefully refined by our team of scientists over the course of the past three years, the UPx system provides a robust infrastructure that enables our suite of products to deliver proven results with industry leading speed and precision.

Unsupervised: Deep learning and algorithmic based solutions are often limited by the need to label data and constantly supervise the learning process of the platform. Our proprietary technology has the ability to break free of these constraints, capable of acting as both student and continual teacher in order to maximize results while minimizing the need for human intervention.

Physics Driven: As an expert in time series data sets, we build all of our algorithms with an understanding that our networks must be aware of real world dynamics. In order to do so, our team of scientists infuses their intensive research and design process with the underlying principles of natural physics. The vast majority of machine learning networks lack the ability for human-like anticipation of future occurrences. At Dynam.AI, however, our physics-based approach has enabled us to build a suite of solutions capable of generating highly anticipatory, multi-dimensional perspectives beyond those generated by our own neural networks.

Explainable: An algorithm is only as powerful as your ability to understand it, and black box algorithms are built to keep their users in the dark. At Dynam.AI, we build products that are not only easy to use, but that offer a streamlined view of the underlying technology. This transparency enables us to swiftly modify our algorithms and gives you the power to understand the exact relationship between our platform and your success.

The UPx Impact:

Behavioral and event-based detection at the highest available level of accuracy.
Accelerated in-network learning processes enabling rapid results.
Unique ability to effectively make use of limited and disparate data sets.
Iterative, self-taught algorithms minimizing the need for intervention and oversight.
User friendly platforms based on open box algorithms for maximum transparency and comprehension.

In a global survey of more than 3,000 executives, approximately 75% of participants said they believe AI holds the key to new business opportunities for their companies. Nearly 85% of respondents believe AI will give them a distinct competitive advantage in business. Yet, despite this excitement over AI’s potential, only one in five companies currently uses AI, and an astounding 60% of companies lack a strategy altogether.

Sloan Review, MIT

Case Study

Reducing Hospital Re-admission Rate

Hospitals around the United States struggle with preventing re-admits – when a patient has to return to the hospital within 30 days of being discharged for the same medical condition. Re-admits are extremely costly for medical insurance carriers and patients. In fact, Medicare charges hospitals a 3% penalty for re-admits, and in 2015, hospitals across the United States were fined $420 million in re-admission penalties.

In 2017, a major university hospital group looked to Dynam.AI data scientists to help them identify the variables causing re-admits and to predict the likelihood of a patient’s re-admission risk during the initial visiting.

Solution

We took into consideration various data sets, such as demographics or cause of the original visit to create a risk score for patients. Staff assignment, performance, and work schedules were also built into the model. Additional data input variables included the total number of returning patients, total visits per year, age, ailments, triage code, facility, marital status, sex, type of accident, primary payer code, and various other factors when determining the best AI technology solution. In total, over 62,000 patient visit records were included. Our solution was built with full data transparency to elevate the visibility of key factors causing re-admission and enable real-time decision making.

Results

Our solution achieved 90% prediction accuracy for patient re-admits. Armed with this predictability and visibility into factors closely related to re-admits, the hospitals were able to make changes that resulted in a 30% reduction in re-admission and savings of $20 million in fines for the university hospital group.

Differentiators

As leaders in time series data, our algorithms and techniques allow us to deliver unrivaled AI technology providing not only superior prediction accuracy but also the ability to fully explain why those predictions are made. Our algorithms and models are a highly specialized blend of techniques and approaches adopted from neuroscience, physics, machine learning, dynamical systems theory, and control theory. Combined with our client-focused approach, this allows us to deliver unmatched performance.

Time series data techniques provide a 360-degree view allowing businesses to grasp past changes, actively monitor what’s happening in the present, and predict how things may occur in the future.

Deep neural nets use more complex variables and represent a more accurate description of systems than other deep nets.

Counter to the typical black-box AI solutions, our explainable AI allows users to understand the AI’s logic, increasing confidence in the recommendations produced.

Our approach to neural nets enables our algorithms to exhibit constant self-improvement and superior agility to adjust to ever changing real-world dynamics.

We combine the best AI techniques available from across the industry with our own proprietary algorithms and methodologies, ensuring the most advanced solutions for our customers.

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