[PERSON], [PERSON], and [PERSON] reported an IoT system designed to facilitate remote monitoring of patients living at their own home in [4]. They introduced a protocol that converts between the ISO/IEEE 11073 protocol and the oneM2M protocol, and an algorithm to schedule biomedical data to be delivered to medical staff based on their urgency using an algorithm called multiclass Q-learning. Their experiment showed that the system worked well with the proposed multiclass Q-learning algorithm compared with the existing multiclass based dynamic priority scheduling algorithm. In [5], [PERSON] et al. introduced a question-driven methodology for analyzing emergency room (ER) processes. According to the methodology, the frequently-posed questions about ER processes were classified, and a reference model was provided to guide the extraction of ER process data. The methodology was validated using a case study conducted in an ER in Chile, which showed that it can help improve the understanding of specific pathologies, triage severity, and patient discharge destinations. In [6], [PERSON] et al. proposed a solution to the problem of Chinese medical question-answer matching, which could be beneficial for millions of users. They introduced a novel framework that is based on end-to-end character-level embeddings instead of word embeddings to avoid Chinese word segmentation in text pre-processing. Multi-scale convolutional neural networks were used to extract contextual information from question and answer sentences at different scales. The framework was validated with a new text corpus called cMedQA, which was collected from an online Chinese health and wellness forum. [PERSON] and [PERSON] [7] presented a method that helps monitor healthcare information and public disease from the vast tweets in twitter. They proposed to use word-embeddings to identify truly health-related tweets. They introduced two algorithms based on the continuous bag-of-words model. The weights of the words were based on the words' relative importance in the classification task. The method was validated using two healthcare-related datasets, which showed that their algorithms performed better than competing approaches by over 9%. In [8], [PERSON] et al. described an early warning system in the form of a mobile app for malaria outbreaks. The system depends on satellite-based meteorological data obtained from Climate Forecast System Reanalysis. Subsequently, hidden ecological factors of malaria were identified by analyzing the causal relationships among meteorological variables (such as minimum and maximum average temperature, relative humidity, etc.) using partial least squares path modeling [9]. Then, machine learning algorithms were used to find patterns that can be used to predict malaria outbreaks. In [10], [PERSON] et al. presented a hospital recommendation system based on satisfaction surveys submitted by patients. They followed an unsupervised data-driven method to analyze the survey data. The key is to identify major factors by clustering individuals with similar satisfaction factors and introduce proactive measures to improve patient care. Based on actual patient survey data of a hospital, they made 19 recommendations, and among them, ten were statistically significant according to the Chi-square test. Automatic emotion recognition can play a big role in patient care and treatment of mental health patients. In [11], [PERSON] et al. reported their work on detecting human emotion from EEG signals via hybrid deep neural networks. They did so in two steps. In the first step, they integrated spatial characteristics, frequency domain, and temporal characteristics of the EEG signals, and converted the result into a two-dimensional image. This led to a series of EEG multidimensional feature image sequences that contain information regarding human emotion variations. In the second step, they performed the emotion recognition task by combining convolution neural networks and long short-term-memory recurrent neural networks.