Design of an improved graph-based model for real-time anomaly detection in healthcare using hybrid CNN-LSTM and federated learning

Real-time monitoring and anomaly detection are essential in healthcare to ensure safe conditions for patients and maintain the integrity of medical data samples. The majority of existing systems, despite improvements in healthcare technologies, cannot capture the spatial and temporal patterns of multimodal data simultaneously, process high Volume data in real-time, and ensure the privacy of patients' identity effectively. In this work, we handle these limitations by proposing a complete approach that uses state-of-the-art deep learning and data processing architectures to realize resilient anomaly detection in healthcare systems. In this paper, we propose an advanced hybrid model for Convolutional and Long Short-Term Memory (CNN-LSTM), which exploits the main advantages of convoluted neural networks and LSTM networks. The proposed model will extract spatial features from medical images and temporal dependencies between them from patient vitals. These three components come together to give high accuracy in anomaly detection, where the accuracy rate was 94 % and a false positive rate as low as 2 % on a test dataset containing 10,000 patients. Furthermore, Apache Kafka will be integrated with TensorFlow Serving for real-time data processing to achieve scale. Conversely, Apache Kafka scales to high throughputs, providing low latencies in message processing, and TensorFlow Serving enables implementation of models into production at scale and serving predictions in real-time situations. This system processes over 100,000 messages per second with less than 50 ms of inference latency to enable the prompting of clinical responses. We use Federated Learning combined with Differential Privacy to solve our data security and privacy tasks. This will enable model training across multiple institutions without compromising the privacy of patient data samples. In the different use-case scenarios, local models update gradients to a central server while achieving model accuracy at 90 % with a privacy loss, epsilon, below 1 %. This will be used to implement a transformer-based multimodal integration model that will integrally fuse these diversified data types, including EHR text data, medical images, and time-series sensor data samples. This model improves the anomaly detection to an F1-score of 0.92, which means a performance increase of 15 % over all unimodal approaches. The proposed methods offer multifaceted solutions that provide advanced machine learning techniques, second-by-second real-time processing, and strict privacy measures all at once. This work significantly increases the reliability and security of healthcare systems, ensuring better patient outcomes while safeguarding sensitive medical data samples.