technology
Integrated Healthcare Data Intelligence
Sade Biotech is using an array of cutting edge technologies, managed by top professionals in order to Integrate continuous real world health data with collective wisdom and artificial intelligence. All components and the platform as a whole are HIPAA & GDPR compliant.
Neural Network in ASAYA™
In Asaya, a neural network is implemented to reduce noise and extract patient-features most relevant for comparison
By analysing the performance of millions of different feature-combinations and feature scaling, the neural network will not only extract relevant features, but will also engineer new relevant features based on the existing data. The use of boosting algorithms and neural network will enable Asaya platform to indicate, which of the patient-features have a higher correlation with treatment outcome and use then accordingly. For example, given a patient’s medical history, Asaya platform will tell that a specific disease (i.e. type-II diabetes) is more associated with treatment outcome than another (i.e. psoriasis).
By analysing the performance of millions of different feature-combinations and feature scaling, the neural network will not only extract relevant features, but will also engineer new relevant features based on the existing data. The use of boosting algorithms and neural network will enable Asaya platform to indicate, which of the patient-features have a higher correlation with treatment outcome and use then accordingly. For example, given a patient’s medical history, Asaya platform will tell that a specific disease (i.e. type-II diabetes) is more associated with treatment outcome than another (i.e. psoriasis).
ASAYA™ Machine Learning
Asaya platform varies its ML algorithms as the data is accumulated
We use a decision-tree based on modeled knowledge gathered from therapists, academic researches, and official guidelines. This decision-tree takes the patient’s data through a series of queries, and based on the gathered knowledge, then outputs the suitable chemovars, titration protocols and strains for the patient.
Mass-data from patients’ feedback trigger a collaborative filtering algorithm that is used to perform a multi-patient comparison of personal details and experiences, in order to provide the patient with the most suitable treatment, based on other patients’ collective experiences. We also integrate boosting algorithms, which can rank features based on their influence on the treatment recommendation and the between-patient comparison
We build a robust architecture that enables easy integration with variable sources of data such as: Connected Medical Devices transmitting dosage details and time-stamps, wearables recording physio-metrics and more. Such data becomes another module in Asaya’s Machine Learning infrastructure.
We use a decision-tree based on modeled knowledge gathered from therapists, academic researches, and official guidelines. This decision-tree takes the patient’s data through a series of queries, and based on the gathered knowledge, then outputs the suitable chemovars, titration protocols and strains for the patient.
Mass-data from patients’ feedback trigger a collaborative filtering algorithm that is used to perform a multi-patient comparison of personal details and experiences, in order to provide the patient with the most suitable treatment, based on other patients’ collective experiences. We also integrate boosting algorithms, which can rank features based on their influence on the treatment recommendation and the between-patient comparison
We build a robust architecture that enables easy integration with variable sources of data such as: Connected Medical Devices transmitting dosage details and time-stamps, wearables recording physio-metrics and more. Such data becomes another module in Asaya’s Machine Learning infrastructure.
Open Architecture
for IoT & Wearables Integration
Seamless integration of iot devices
We know the value of each data point and aspire for as many quality data points – therefore we invest in a simple to implement Application Protocol Interface (API), that will encourage integration and win-win with our partners.
We know the value of each data point and aspire for as many quality data points – therefore we invest in a simple to implement Application Protocol Interface (API), that will encourage integration and win-win with our partners.
Asaya Integration with Partners IoT Devices
To better understand how Asaya’s big data analytics and our partners IoT devices work together, we can outline the overall flow:
1. Our partner’s devices (i.e sleep monitoring device) are installed and use sensors for collecting and transmitting data (i.e sleep levels, patterns and timings)
2. Large amount of data is then collected, often in real time. Both structured data from prepared data sources (patient profiles, plant chemovars, etc.) and unstructured data from third party sources (heart-monitoring devices, skin-conductivity devices, precise-dosage vaporizers and more).
3. The data is graded for fidelity and accuracy – and is then added to subjective patient feedback towards building a multi-source treatment feedback legend.
4. Asaya’s engine processes the data, finds its correlated categories, comprables and interconnections, and creates analysis, insights, reports, charts, and other outputs. Such outputs are mostly displayed in ASAYA’s HCP dashboard and in some cases, they make their way to the patients mobile device. The analyzed data feeds back to the Asaya engine to increase its exactitude.
1. Our partner’s devices (i.e sleep monitoring device) are installed and use sensors for collecting and transmitting data (i.e sleep levels, patterns and timings)
2. Large amount of data is then collected, often in real time. Both structured data from prepared data sources (patient profiles, plant chemovars, etc.) and unstructured data from third party sources (heart-monitoring devices, skin-conductivity devices, precise-dosage vaporizers and more).
3. The data is graded for fidelity and accuracy – and is then added to subjective patient feedback towards building a multi-source treatment feedback legend.
4. Asaya’s engine processes the data, finds its correlated categories, comprables and interconnections, and creates analysis, insights, reports, charts, and other outputs. Such outputs are mostly displayed in ASAYA’s HCP dashboard and in some cases, they make their way to the patients mobile device. The analyzed data feeds back to the Asaya engine to increase its exactitude.
