Kainematics believes that AI can help in achieving a faster and more accurate care for the benefit of the patients, by supporting citizens, clinicians and policy makers taking the right decisions.
In order to support evidence-based decisions, it leverages advanced AI algorithms to revolutionize healthcare by providing solutions that improve diagnosis, optimize patient-flows for treatment in smart hospitals, enhance distant monitoring during the transition of care from hospital to home, and use predictive analytics for public healthcare systems to achieve economies of scale and evaluate policies.
- AI-powered diagnostics utilize deep learning models such as convolutional neural networks (CNNs) for medical image analysis to detect diseases like cancer at early stages.
- Clustering algorithms like Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), Fuzzy C-means, Autoencoders with K-means can help identify patterns and trends in large datasets, aiding in public health surveillance, personalized risk calculation and triage-group definition.
- Clinical decision support systems (DSS) that employ a human-in-the-loop approaches like Reinforcement Learning (RL) with Expert feedback such as the Deep Q-Networks (DQN), Policy Gradient Methods and Human-in-the-loop Generative Adversarial Networks (HITL-GANs) with Human Discriminators combine AI insights with clinician expertise to improve decision-making accuracy and patient outcomes.
- Evidence-based decision-making tools for policymakers use predictie analytics to inform health policies, considering constraints such as budget and resource availability.
- Wearable data analytics support the transition from hospital to home by monitoring patient health metrics in real-time, enabling early intervention and reducing readmissions. More specific, Long Short-Term Memory (LSTM) networks can continuously monitor vital signals and other patient data for early warning systems.
- Custom AI-powered RAG (retrieval-augmented generation) co-pilots can enhance clinicians, policy makers, and patients by providing contextually relevant information (summarization of clinical notes, automate data extraction, etc) and recommendations, thereby improving clinical practices, policy development, and patient engagement.