Artificial intelligence, and particularly machine learning (ML), is increasingly developed and deployed to support healthcare in a variety of settings. However, clinical decision support (CDS) technologies based on ML need to be portable if they are to be adopted on a broad scale. In this respect, models developed at one institution should be reusable at another. Yet there are numerous examples of portability failure, particularly due to naive application of ML models. Portability failure can lead to suboptimal care and medical errors, which ultimately could prevent the adoption of ML-based CDS in practice. One specific healthcare challenge that could benefit from enhanced portability is the prediction of 30-day readmission risk. Research to date has shown that deep learning models can be effective at modeling such risk. In this work, we investigate the practicality of model portability through a cross-site evaluation of readmission prediction models. To do so, we apply a recurrent neural network, augmented with self-attention and blended with expert features, to build readmission prediction models for two independent large scale claims datasets. We further present a novel transfer learning technique that adapts the well-known method of born-again network (BAN) training. Our experiments show that direct application of ML models trained at one institution and tested at another institution perform worse than models trained and tested at the same institution. We further show that the transfer learning approach based on the BAN produces models that are better than those trained on just a single institution's data. Notably, this improvement is consistent across both sites and occurs after a single retraining, which illustrates the potential for a cheap and general model transfer mechanism of readmission risk prediction.

The architecture Many institutions within the healthcare ecosystem are making is designed to accommodate trust and reproducibility as significant investments in AI technologies to optimize their business an inherent part of the AI life cycle and support the needs for a operations at lower cost with improved patient outcomes. Despite deployed AI system in healthcare. In what follows, we start with the hype with AI, the full realization of this potential is seriously a crisp articulation of challenges that we have identified to derive hindered by several systemic problems, including data privacy, the requirements for this architecture. We then follow with a description security, bias, fairness, and explainability. In this paper, we propose of this architecture before providing qualitative evidence a novel canonical architecture for the development of AI models of its capabilities in real world settings.