# Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners
## 7 Toy Examples  
Some toy examples have been developed to help beginners start with some available introductory code while using one of the world's most popular biomedical databases, the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) database [186,196], which is also available. [208]. Some results were obtained by selecting 1546 files for training, 1328 for validation and 525 for test, from the MIMIC dataset. Each file has a length of 250 samples.  
Examples are implemented in PyTorch [209], a famous open-source DL library for Python. A DL architecture, which is based on the U-Net network [210,211] has been designed: (1) To predict the arterial blood pressure signal by using the plethysmogram signal (_PLETH to ABP_ example) as input; and (2) to predict the plethysmogram signal (_ABP to PLETH_ example) by using the arterial blood pressure signal as input that is the reverse problem. The architecture is a one-dimensional CNN, called a fully convolutional network, with a deeply-supervised encoder-decoder connected through a series of skip pathways, and in which the input and the output are the same size. In particular, the network learns how to transform a one-dimensional time signal with a size of 250 samples into another signal with the same size (arterial blood pressure signal to plethysmogram signal or vice versa).  
For that, it uses convolution kernels of size 3 with stride and padding of size 1, batch normalization, and regularization through dropout. The results shown in this section must be regarded as toy examples and an interesting pointer for future work.  
The training process with 100 epochs and a batch size of 256 lasted around 18 min for both goals. By using the Mean Absolute Error (MAE) as loss function, the results were: 14.27 in training, 32.20 in validation and 17.41 in test, in the _PLETH to ABP_ example (Figure 3); In addition, 0.14 in training, 0.19 in validation and 0.20 in test, in the reverse problem _ABP to PLETH_ (Figure 4).  
These results are intended to be a possible starting point to scrutinize the possibilities of DL techniques in a given problem. After starting with the DL architecture, the type of layers, and the training strategy, other topics should be scrutinized. On the one hand, it is crucial to gain insight about the interest and nature of the healthcare problem. For instance, in this case, it is well known that the blood pressure has different profiles as we go further into the heart in the arterial tree, and information is partially lost after the attenuation from the arm to the fingers. This means that estimating the ABP signals from PLETH signals is a very hard problem, but it would be also very valuable in clinical settings and in wellness devices. On the other hand, a view of the results can give us some ideas about the scope and limitations of the method. Both for the direct and for the reverse problems, the algorithm seems to be capturing to some extent the average value of the predicted signals from the predictor signals in these examples, which should be further checked in other examples. In this step, error analysis and systematic visualization of results could be very helpful. On the other hand, we can see that the profile of the predicted waveform is partially captured by the system, whereas some waveform distortion still cannot be avoided and should be the next target. The interested reader is encouraged to continue searching for further possibilities on this problem. Nevertheless, it is important to be able to start working on a new problem with a first prototype of enough quality and then iterating it with as specific as possible targets for improvement.  
Figure 3: Two records (one in each row) with input signal (PLETH) on the left, and output signal (predicted ABP) and ground true signal (ABP) on the right, for the _PLETH to ABP_ example.