Understanding patient complaint characteristics using contextual clinical BERT embeddings.

In clinical conversational applications, extracted entities tend to capture the main subject of a patient's complaint, namely symptoms or diseases. However, they mostly fail to recognize the characterizations of a complaint such as the time, the onset, and the severity. For example, if the input is "I have a headache and it is extreme", state-of-the-art models only recognize the main symptom entity - headache, but ignore the severity factor of extreme, that characterises headache.

A Framework for Mixed-Type Multioutcome Prediction With Applications in Healthcare.

Health analysis often involves prediction of multiple outcomes of mixed type. The existing work is restrictive to either a limited number or specific outcome types. We propose a framework for mixed-type multioutcome prediction.

A Framework for Classifying Online Mental Health-Related Communities With an Interest in Depression.

Mental illness has a deep impact on individuals, families, and by extension, society as a whole. Social networks allow individuals with mental disorders to communicate with others sufferers via online communities, providing an invaluable resource for studies on textual signs of psychological health problems. Mental disorders often occur in combinations, e.

Multi-task transfer learning for in-hospital-death prediction of ICU patients.

Multi-Task Transfer Learning (MTTL) is an efficient approach for learning from inter-related tasks with small sample size and imbalanced class distribution. Since the intensive care unit (ICU) data set (publicly available in Physionet) has subjects from four different ICU types, we hypothesize that there is an underlying relatedness amongst various ICU types. Therefore, this study aims to explore MTTL model for in-hospital mortality prediction of ICU patients.