Clinical Decision Support Systems
Cyceer’s Neuromorphic Framework (NF) can be used to synthesize clinical decision support systems (CDSS) for a diverse range of healthcare disciplines such as the examination of real-time data from monitoring devices, analyses of patient and family history for the purpose of diagnosis, reviews of common characteristics and trends in medical record databases. Using a stratified approach, the CDSS can play a significant role in individual patient diagnosis and prognosis. Cognitive and knowledge based methods are applied constantly to analyse EMRs, medical / drug data and best practice guidelines, ensuring the latest techniques and best practice are available medical staff when making treatment decisions.
A CDSS can handle multi-dimensional and multi-criteria biomarker data (which can be conflicting) enabling a stratified response on an individual patient basis. Based on the current data, the system can forecast “what-if” outcomes and provide analysis of the affect of a prescribed treatment over time. Different version of the technology can also be used for patient monitoring.
- Efficient healthcare provision
- Accelerates development of new diagnostics, medicines and clinical pathways
- Better patient outcomes through more effective drug selection
- Reduced adverse reactions and side effects
- More cost effective solutions by not buying treatments that do not work and time wasted trying unsuitable medicines
The healthcare market is driven by government policies, performance evaluation, value and improved patient outcomes. Consequently, healthcare organisations can use IDSS to analyse data to improve overall operational performance. The results can determine the disease patterns, high risk patients and suggest the most suitable cost effective treatments.
In Silico Clinical Trials
Developing a new drug is a costly, risky and time consuming process. Consequently, it is essential that biopharmaceutical businesses and regulators employ innovative methods to quickly and efficiently get new drugs through the regulatory process and onto market.
In silico clinical trials refers to the adoption of computer simulation in the development or regulatory evaluation of a new drugs, medical device, or medical intervention.
One way to achieve this is to use artificial intelligence (AI) technology, combined with big data, to solve many key clinical trial challenges, such as, increasing trial efficiency through better protocol design, patient enrolment and retention, and evaluation start-up.
Cyceera’s neuromorphic processing and machine learning technology is flexible enough to be applied to these and a wide range of biomedical areas.
Analysis of big data using our Neuromorphic Framework has the potential to generate new insights into disease processes that could enable new treatment avenues. It has the potential to advance personalised medicine by identifying patients most likely to respond to specific treatments based on their individual characteristics.
Creating powerful predictive models will reduce the risk of drug development and enhance the chance of successful clinical trials. This will be beneficial to both patients and the pharmaceutical companies as it will bring products to patients sooner.
Cyceera’s Neuromorphic Framework for in silico clinical trials can be applied to the entire development and assessment cycle for both pharmaceuticals and medical devices and thus promote to a wider adoption of the technology throughout the industry.