Determine an optimal generalization model with deep neu-ral networks for a medical task is an expensive process that generally requires large amounts of data and computing power. Furthermore, scale deep learning workflows over a wide range of emerging heterogeneous system architecture increases the programming expressiveness complexity for model training and the computing orchestration. We introduce Diag-noseNET, a programming framework designed for scaling deep learning models over heterogeneous systems applied to medical diagnosis. It is designed as a modular framework to enable the deep learning workflow management and allows the expressiveness of neural networks written in TensorFlow, while its runtime abstracts the data locality, micro batch-ing and the distributed orchestration to scale the neural network model from a GPU workstation to multi-nodes. The main approach is composed through a set of gradient computation modes to adapt the neural network according to the memory capacity, the workers' number, the coordination method and the communication protocol (GRPC or MPI) for achieving a balance between accuracy and energy consumption. The experiments carried out allow to evaluate the computational performance in terms of accuracy, convergence time and worker scalability to determine an optimal neural architecture over a mini-cluster of Jetson TX2 nodes. These experiments were performed using two medical cases of study, the former dataset is composed by clinical descriptors collected during the first week of hospitalization of patients in the Provence-Alpes-Côte d'Azur region; the second dataset uses a short ECG records between 30 and 60 seconds, obtained as part of the PhysioNet 2017 Challenge.