Abstract

Abstract
Background and objective: Traumatic Brain Injury (TBI) is one of the leading causes of injury-related mortality in the world, with severe cases reaching mortality rates of 30-40%. It is highly heterogeneous both in causes and consequences making more complex the medical interpretation and prognosis. Gathering clinical, demographic, and laboratory data to perform a prognosis requires time and skill in several clinical specialties. Artificial intelligence (AI) methods can take advantage of existing data by performing helpful predictions and guiding physicians toward a better prognosis and, consequently, better healthcare. The objective of this work was to develop learning models and evaluate their capability of predicting the mortality of TBI. The predictive model would allow the early assessment of the more serious cases and scarce medical resources can be pointed toward the patients who need them most. Methods: Long Short Term Memory (LSTM) and Transformer architectures were tested and compared in performance, coupled with data imbalance, missing data, and feature selection strategies. From the Medical Information Mart for Intensive Care III (MIMIC-III) dataset, a cohort of TBI patients was selected and an analysis of the first 48 hours of multiple time series sequential variables was done to predict hospital mortality. Results: The best performance was obtained with the Transformer architecture, achieving an AUC of 0.907 with the larger group of features and trained with class proportion class weights and binary cross entropy loss. Conclusions: Using the time series sequential data, LSTM and Transformers proved to be both viable options for predicting TBI hospital mortality in 48 hours after admission. Overall, using sequential deep learning models with time series data to predict TBI mortality is viable and can be used as a helpful indicator of the well-being of patients.

Abstract
PPG signal is a valuable resource for continuous heart rate monitoring; however, this signal suffers from artifact movements, which is particularly relevant during physical exercise and makes this biomedical signal difficult to use for heart rate detection during those activities. The purpose of this study was to develop learning models to determine heart rate using data from wearables (PPG and acceleration signals) and dealing with noise during physical exercise. Learning models based on CNNs and LSTMs were developed to predict the heart rate. The PPG signal was combined with data from accelerometers trying to overcome the noise movement on the PPG signal. Two datasets were used on this work: the 2015 IEEE Signal Processing Cup (SPC) dataset was used for training and testing, and another dataset was used for validation of the learning model (PPG-DaLiA dataset). The predictions obtained by the learning model represented a mean average error of 7.033±5.376 bpm for the SCP dataset, while a mean average error of 9.520±8.443 bpm for the validation set. The use of acceleration data increases the performance of the learning models on the prediction of the heart rate, showing the benefits of using this source of data to overcome the noise movement problem on the PPG signal. The combination of PPG signal with acceleration data could allow the learning models to use more information regarding the motion artifacts that affect the PPG and improve performance on the physiological event detections, which will largely spread the use of wearables on the healthcare applications for continuous monitor the physiological state allowing early and accurate detection of pathological events.

Abstract
Amongst the different types of cancer, lung cancer is the one with the highest mortality rate and consequently, there is an urgent need to develop early detection methods to improve the survival probabilities of the patients. Due to the millions of deaths that are caused annually by cancer, there is large interest int the scientific community to developed deep learning models that can be employed in computer aided diagnostic tools.Currently, in the literature, there are several works in the Radiomics field that try to develop new solutions by employing learning models for lung nodule classification. However, in these types of application, it is usually required to extract the lung nodule from the input images, while using a segmentation mask made by a radiologist. This means that in a clinical scenario, to be able to employ the developed learning models, it is required first to manually segment the lung nodule. Considering the fact that several patients are attended daily in the hospital with suspicion of lung cancer, the segmentation of each lung nodule would become a tiresome task. Furthermore, the available algorithms for automatic lung nodule segmentation are not efficient enough to be used in a real application.In response to the current limitations of the state of the art, the proposed work attempts to evaluate a multitasking approach where both the segmentation and the classification task are executed in parallel. As a baseline, we also study a sequential approach where first we employ DL models to segment the lung nodule, corp the lung nodule from the input image and then finally, we classify the cropped nodule. Our results show that the multitasking approach is better than to sequentially execute the segmentation and classification task for lung nodule classification. For instances, while the multitasking approach was able to achieve an AUC of 84.49% in the classification task, the sequential approach was only able to achieve an AUC of 72.43%. These results show that the proposed multitasking approach can become a viable alternative to the classification and segmentation of lung nodules. © 2023 IEEE.

Abstract
Bone marrow edema (BME) or bone marrow lesion is the term attributed to an observed signal change within the bone marrow in magnetic resonance imaging (MRI). BME can be originated from multiple mechanisms, with pain being the main symptom. The presence of BME is an unspecific but sensitive sign with a wide differential diagnosis, that may act as a guide that leads to a systematic and correct interpretation of the magnetic resonance examination. An automatic approach for BME detection and quantification aims to reduce the overload of clinicians, decreasing human error and accelerating the time to the correct diagnosis. In this work, the bone region on the MRI slice was split into several patches and a CNN-based model was trained to detect BME in each patch from the MRI slice. The learning model developed achieved an AUC of 0.853 ± 0.056, showing that the CNN-based model can be used to detect BME in the MRI and confirming the patch strategy implemented to deal with the small data size and allowing the neural network to learn the specific information related with the classification task by reducing the region of the image to be considered. A learning model that can help clinicians with BME identification will decrease the time and the error for the diagnosis, and represent the first step for a more objective assessment of the BME. © 2023 IEEE.

Abstract
Microsatellite Instability (MSI) is an important biomarker in cancer patients, showing a defective DNA mismatch repair system. Its detection allows the use of immunotherapy to treat cancer, an approach that is revolutionizing cancer treatment. MSI is especially relevant for three types of cancer: Colon Adenocarcinoma (COAD), Stomach Adenocarcinoma (STAD), and Uterus corpus endometrial cancer (UCEC). In this work, learning algorithms were employed to predict MSI using RNA-seq data from The Cancer Genome Atlas (TCGA) database, with a focus on the selection of the most informative genomic features. The Multi-Layer Perceptron (MLP) obtained the best score (AUC = 98.44%), showing that it is possible to exploit information from RNA-seq data to find relevant relationships with the instability levels of microsatellites (MS). The accurate prediction of MSI with transcription data from cancer patients will help with the correct determination of MSI status and adequate prescription of immunotherapy, creating more precise and personalized patient care. At the genetic level, the study revealed a high expression of genes related to cell regulation functions, and a low expression of genes responsible for Mismatch Repair functions, in patients with high instability.