The Application of Image Recognition and Machine Learning to Capture Readings of Traditional Blood Pressure Devices: A Platform to Promote Population Health Management to Prevent Cardiovascular Diseases

Digital solutions for Blood Pressure Monitoring (or Telemonitoring) have sprouted in recent years, innovative solutions are often connected to the Internet of Things (IoT), with mobile health (mHealth) platform. However, clinical validity, technology cost and cross-platform data integration remain as the major barriers for the application of these solutions. In this paper, we present an IoT-based and AI-embedded Blood Pressure Telemonitoring (BPT) system, which facilitates home blood pressure monitoring for individuals. The highlights of this system are the machine learning techniques to enable automatic digits recognition, with F1 score of 98.5%; and the cloud-based portal developed for automated data synchronization and risk stratification. Positive feedbacks on trial implementation are received from three clinics. The overall system architecture, development of machine learning model in digit identification and cloud-based telemonitoring are addressed in this paper, alongside the followed implications

Detecting Hypoglycemia Incidents Reported in Patients\u27 Secure Messages: Using Cost-Sensitive Learning and Oversampling to Reduce Data Imbalance

BACKGROUND: Improper dosing of medications such as insulin can cause hypoglycemic episodes, which may lead to severe morbidity or even death. Although secure messaging was designed for exchanging nonurgent messages, patients sometimes report hypoglycemia events through secure messaging. Detecting these patient-reported adverse events may help alert clinical teams and enable early corrective actions to improve patient safety. OBJECTIVE: We aimed to develop a natural language processing system, called HypoDetect (Hypoglycemia Detector), to automatically identify hypoglycemia incidents reported in patients\u27 secure messages. METHODS: An expert in public health annotated 3000 secure message threads between patients with diabetes and US Department of Veterans Affairs clinical teams as containing patient-reported hypoglycemia incidents or not. A physician independently annotated 100 threads randomly selected from this dataset to determine interannotator agreement. We used this dataset to develop and evaluate HypoDetect. HypoDetect incorporates 3 machine learning algorithms widely used for text classification: linear support vector machines, random forest, and logistic regression. We explored different learning features, including new knowledge-driven features. Because only 114 (3.80%) messages were annotated as positive, we investigated cost-sensitive learning and oversampling methods to mitigate the challenge of imbalanced data. RESULTS: The interannotator agreement was Cohen kappa=.976. Using cross-validation, logistic regression with cost-sensitive learning achieved the best performance (area under the receiver operating characteristic curve=0.954, sensitivity=0.693, specificity 0.974, F1 score=0.590). Cost-sensitive learning and the ensembled synthetic minority oversampling technique improved the sensitivity of the baseline systems substantially (by 0.123 to 0.728 absolute gains). Our results show that a variety of features contributed to the best performance of HypoDetect. CONCLUSIONS: Despite the challenge of data imbalance, HypoDetect achieved promising results for the task of detecting hypoglycemia incidents from secure messages. The system has a great potential to facilitate early detection and treatment of hypoglycemia

Principles and Applications of Data Science

Data science is an emerging multidisciplinary field which lies at the intersection of computer science, statistics, and mathematics, with different applications and related to data mining, deep learning, and big data. This Special Issue on “Principles and Applications of Data Science” focuses on the latest developments in the theories, techniques, and applications of data science. The topics include data cleansing, data mining, machine learning, deep learning, and the applications of medical and healthcare, as well as social media

Title

Abstrac

Deep learning for prediction of depressive symptoms in a large textual dataset

Depression is a common illness worldwide with potentially severe implications. Early identification of depressive symptoms is a crucial first step towards assessment, intervention, and relapse prevention. With an increase in data sets with relevance for depression, and the advancement of machine learning, there is a potential to develop intelligent systems to detect symptoms of depression in written material. This work proposes an efficient approach using Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN) to identify texts describing self-perceived symptoms of depression. The approach is applied on a large dataset from a public online information channel for young people in Norway. The dataset consists of youth’s own text-based questions on this information channel. Features are then provided from a one-hot process on robust features extracted from the reflection of possible symptoms of depression pre-defined by medical and psychological experts. The features are better than conventional approaches, which are mostly based on the word frequencies (i.e., some topmost frequent words are chosen as features from the whole text dataset and applied to model the underlying events in any text message) rather than symptoms. Then, a deep learning approach is applied (i.e., RNN) to train the time-sequential features discriminating texts describing depression symptoms from posts with no such descriptions (non-depression posts). Finally, the trained RNN is used to automatically predict depression posts. The system is compared against conventional approaches where it achieved superior performance than others. The linear discriminant space clearly reveals the robustness of the features by generating better clustering than other traditional features. Besides, since the features are based on the possible symptoms of depression, the system may generate meaningful explanations of the decision from machine learning models using an explainable Artificial Intelligence (XAI) algorithm called Local Interpretable Model-Agnostic Explanations (LIME). The proposed depression symptom feature-based approach shows superior performance compared to the traditional general word frequency-based approaches where frequency of the features gets more importance than the specific symptoms of depression. Although the proposed approach is applied on a Norwegian dataset, a similar robust approach can be applied on other depression datasets developed in other languages with proper annotations and symptom-based feature extraction. Thus, the depression prediction approach can be adopted to contribute to develop better mental health care technologies such as intelligent chatbots.publishedVersio