Marpai takes a preventive healthcare approach to improving outcomes and reducing healthcare costs using the world’s first and only purpose-built, deep learning health prediction framework.
Deep Learning, the most advanced AI, delivers the greatest form of health state prediction available today—allowing Marpai to predict near-term health events to prevent chronic illness (like Type 2 Diabetes) and major procedures (like knee surgery) before they happen which protects health plan members well beyond traditional preventive health solutions.
Powered by a deep neural network brain that mimics the logic and learning of the human brain, Marpai’s deep learning platform anticipates and prevents health events with unmatched speed and accuracy. We stop chronic illness before it develops, identifying health risks with maximum accuracy.
To stay ahead of the developing health issues and prevent unknown risks from developing, the predictive power of Marpai’s deep learning-based solution is a necessity.
What is Deep Learning
Deep learning is the most advanced subset of AI, leveraging deep neural networks that take inspiration from how the human brain works.
Deep Learning is an artificial intelligence method that imitates the way the human brain works in the sense of processing data and creating patterns for use in decision making. It utilizes a hierarchical level of artificial neural networks to carry out the learning process involved in machine learning. The artificial neural networks are built like the human brain, with neuron nodes connected like an interconnected web.
As more data is fed into the neural network, it becomes better at intuitively understanding the meaning of new data. This allows it to predict and prevent increasingly advanced threats such as health risks. It does not require a (human) expert to help it understand the significance of new features.
The advantage of deep learning over other forms of machine learning is the end-to-end processing of data. These three factors contribute to deep learning’s greater level of accuracy.
Key Differences Between Machine Learning and Deep Learning
Machine Learning systems rely on feature engineering which is limited to the knowledge of the expert who has to handcraft the features for detection. Machine learning-based solutions are still producing low detection rates and high false-positive rates. With Deep Learning, the algorithm analyzes all the raw data in a file, and is not limited by an expert’s capabilities. This represents a quantum leap in computer science. For future health states, this enables a more advanced level of prediction; with higher detection rates, lower false-positive rates and the ability to detect health risks effectively in zero-time.
Other differences include:
Talent: State-of-the-art deep learning solutions require deep learning experts, which are in short supply. At Marpai Labs, we have over 20 working side by side with healthcare providers in a dedicated R&D setting.
Platform: Developing a deep learning framework is an extremely complex task, which only a few companies have successfully accomplished. Publicly available deep learning frameworks are sufficient to meet the needs for most computer vision applications, but are inefficient for other applications, such as health state predictions. Very few companies are currently practicing deep learning in this domain, due to the number of significant challenges (e.g. scanning thousands of files per second).
Raw Data: Deep Learning is typically applied directly to raw data. Any answer that involves “feature extraction” or “manual preprocessing” suggests that machine learning is used. Unlike traditional Machine Learning methods where applied features must be identified by an expert and then hand coded per domain and data type, Deep Learning is applied directly to raw data without any required domain knowledge. In Deep Learning, the features are identified by the algorithm itself. The process in which the algorithms are learning higher level representation of features in deeper layers is called representation learning.
Low Level of False Positives: As the Deep Learning algorithm analyzes 100% of the data and is not subject to human error, false positives are dramatically diminished.
Brain Training Time: Deep Learning algorithms take significantly more time to train than those of traditional machine learning. Traditional machine learning algorithms typically take from a few seconds to a few hours to train, while deep learning algorithms take just several hours to train.
File Coverage: Traditional Machine Learning algorithms require different human engineered features for every file type (PDF, DOC, EXE, etc.). In contrast. Deep Learning is input agnostic, and therefore not file type dependent. This allows deep learning to be easily applied without requiring substantial modifications or adaptations.