Administrative Data Linkage in Brazil: Potentials for Health Technology Assessment.

Health technology assessment (HTA) is the systematic evaluation of the properties and impacts of health technologies and interventions. In this article, we presented a discussion of HTA and its evolution in Brazil, as well as a description of secondary data sources available in Brazil with potential applications to generate evidence for HTA and policy decisions. Furthermore, we highlighted record linkage, ongoing record linkage initiatives in Brazil, and the main linkage tools developed and/or used in Brazilian data. Finally, we discussed the challenges and opportunities of using secondary data for research in the Brazilian context. In conclusion, we emphasized the availability of high quality data and an open, modern attitude toward the use of data for research and policy. This is supported by a rigorous but enabling legal framework that will allow the conduct of large-scale observational studies to evaluate clinical, economical, and social impacts of health technologies and social policies

BEKG: A Built Environment Knowledge Graph

Practices in the built environment have become more digitalized with the rapid development of modern design and construction technologies. However, the requirement of practitioners or scholars to gather complicated professional knowledge in the built environment has not been satisfied yet. In this paper, more than 80,000 paper abstracts in the built environment field were obtained to build a knowledge graph, a knowledge base storing entities and their connective relations in a graph-structured data model. To ensure the retrieval accuracy of the entities and relations in the knowledge graph, two well-annotated datasets have been created, containing 2,000 instances and 1,450 instances each in 29 relations for the named entity recognition task and relation extraction task respectively. These two tasks were solved by two BERT-based models trained on the proposed dataset. Both models attained an accuracy above 85% on these two tasks. More than 200,000 high-quality relations and entities were obtained using these models to extract all abstract data. Finally, this knowledge graph is presented as a self-developed visualization system to reveal relations between various entities in the domain. Both the source code and the annotated dataset can be found here: https://github.com/HKUST-KnowComp/BEKG

DEEP LEARNING METHODS FOR MULTI-MODAL HEALTHCARE DATA

Abstract: Today, enormous transformations are happening in health care research and applications. In the past few years, there has been exponential growth in the amount of healthcare data generated from multiple sources. This growth in data has led to many new possibilities and opportunities for researchers to build different models and analytics for improving healthcare for patients. While there has been an increase in research and successful application of prediction and classification tasks, there are many other challenges in improving overall healthcare. Some of these challenges include optimizing physician performance, reducing healthcare costs, and discovering new treatments for diseases. - Often, doctors have to perform many time-consuming tasks, which leads to fatigue and misdiagnosis. Many of these tasks could be automated to save time and release doctors from menial tasks enabling them to spend more time improving the quality of care. - Health dataset contains multiple modalities such as structured sequence, unstructured text, images, ECG, and EEG signals. Successful application of machine learning requires methods to utilize these diverse data sources. - Finally, current healthcare is limited by the treatments available on the market. Often, many treatments do not make it beyond clinical trials, which leads to a lot of lost opportunities. It is possible to improve the outcome of clinical trials and ultimately improve the quality of treatment for the patients with machine learning models for different clinical trial-related tasks. In this dissertation, we address these challenges by - Predictive Models: Building deep learning models for sleep clinics to save time and effort needed by doctors for sleep staging, apnea, limb movement detection - Generative Models: Developing multimodal deep learning systems that can produce text reports and augment doctors in clinical practice. - Interpretable Representation Models: Applying multimodal models to help in clinical trial recruitment and counterfactual explanations for clinical trial outcome predictions to improve clinical trial success.Ph.D