Collaborating Authors

Health State Estimation Artificial Intelligence

Life's most valuable asset is health. Continuously understanding the state of our health and modeling how it evolves is essential if we wish to improve it. Given the opportunity that people live with more data about their life today than any other time in history, the challenge rests in interweaving this data with the growing body of knowledge to compute and model the health state of an individual continually. This dissertation presents an approach to build a personal model and dynamically estimate the health state of an individual by fusing multi-modal data and domain knowledge. The system is stitched together from four essential abstraction elements: 1. the events in our life, 2. the layers of our biological systems (from molecular to an organism), 3. the functional utilities that arise from biological underpinnings, and 4. how we interact with these utilities in the reality of daily life. Connecting these four elements via graph network blocks forms the backbone by which we instantiate a digital twin of an individual. Edges and nodes in this graph structure are then regularly updated with learning techniques as data is continuously digested. Experiments demonstrate the use of dense and heterogeneous real-world data from a variety of personal and environmental sensors to monitor individual cardiovascular health state. State estimation and individual modeling is the fundamental basis to depart from disease-oriented approaches to a total health continuum paradigm. Precision in predicting health requires understanding state trajectory. By encasing this estimation within a navigational approach, a systematic guidance framework can plan actions to transition a current state towards a desired one. This work concludes by presenting this framework of combining the health state and personal graph model to perpetually plan and assist us in living life towards our goals.

Effective Learning of Probabilistic Models for Clinical Predictions from Longitudinal Data Machine Learning

Such information includes: the database in modern hospital systems, usually known as Electronic Health Records (EHR), which store the patients' diagnosis, medication, laboratory test results, medical image data, etc.; information on various health behaviors tracked and stored by wearable devices, ubiquitous sensors and mobile applications, such as the smoking status, alcoholism history, exercise level, sleeping conditions, etc.; information collected by census or various surveys regarding sociodemographic factors of the target cohort; and information on people's mental health inferred from their social media activities or social networks such as Twitter, Facebook, etc. These health-related data come from heterogeneous sources, describe assorted aspects of the individual's health conditions. Such data is rich in structure and information which has great research potentials for revealing unknown medical knowledge about genomic epidemiology, disease developments and correlations, drug discoveries, medical diagnosis, mental illness prevention, health behavior adaption, etc. In real-world problems, the number of features relating to a certain health condition could grow exponentially with the development of new information techniques for collecting and measuring data. To reveal the causal influence between various factors and a certain disease or to discover the correlations among diseases from data at such a tremendous scale, requires the assistance of advanced information technology such as data mining, machine learning, text mining, etc. Machine learning technology not only provides a way for learning qualitative relationships among features and patients, but also the quantitative parameters regarding the strength of such correlations.

The Risk to Population Health Equity Posed by Automated Decision Systems: A Narrative Review Artificial Intelligence

Artificial intelligence is already ubiquitous, and is increasingly being used to autonomously make ever more consequential decisions. However, there has been relatively little research into the consequences for equity of the use of narrow AI and automated decision systems in medicine and public health. A narrative review using a hermeneutic approach was undertaken to explore current and future uses of AI in medicine and public health, issues that have emerged, and longer-term implications for population health. Accounts in the literature reveal a tremendous expectation on AI to transform medical and public health practices, especially regarding precision medicine and precision public health. Automated decisions being made about disease detection, diagnosis, treatment, and health funding allocation have significant consequences for individual and population health and wellbeing. Meanwhile, it is evident that issues of bias, incontestability, and erosion of privacy have emerged in sensitive domains where narrow AI and automated decision systems are in common use. As the use of automated decision systems expands, it is probable that these same issues will manifest widely in medicine and public health applications. Bias, incontestability, and erosion of privacy are mechanisms by which existing social, economic and health disparities are perpetuated and amplified. The implication is that there is a significant risk that use of automated decision systems in health will exacerbate existing population health inequities. The industrial scale and rapidity with which automated decision systems can be applied to whole populations heightens the risk to population health equity. There is a need therefore to design and implement automated decision systems with care, monitor their impact over time, and develop capacities to respond to issues as they emerge.

Finding Cut from the Same Cloth: Cross Network Link Recommendation via Joint Matrix Factorization

AAAI Conferences

With the emergence of online forums associated with major diseases, such as diabetes mellitus, many patients are increasingly dependent on such disease-specific social networks to gain access to additional resources. Among these patients, it is common for them to stick to one disease-specific social network, although their desired resources might be spread over multiple social networks, such as patients with similar questions and concerns. Motivated by this application, in this paper, we focus on cross network link recommendation, which aims to identify similar users across multiple heterogeneous social networks. The problem setting is different from existing work on cross network link prediction, which either tries to link accounts of the same user from different social networks, or aims to match users with complementary expertise or interest. To approach the problem of cross network link recommendation, we propose to jointly decompose the user-keyword matrices from multiple social networks, while requiring them to share the same topics and user group-topic association matrices. This constraint comes from the fact that social networks dedicated to the same disease tend to share the same topics as well as the interests of users groups in certain topics. Based on this intuition, we construct a generic optimization framework, provide four instantiations and an iterative optimization algorithm with performance analysis. In the experiments, we demonstrate the superiority of the proposed algorithm over state-of-the-art techniques on various real-world data sets.

Using Contextual Information to Improve Blood Glucose Prediction Machine Learning

Blood glucose value prediction is an important task in diabetes management. While it is reported that glucose concentration is sensitive to social context such as mood, physical activity, stress, diet, alongside the influence of diabetes pathologies, we need more research on data and methodologies to incorporate and evaluate signals about such temporal context into prediction models. Person-generated data sources, such as actively contributed surveys as well as passively mined data from social media offer opportunity to capture such context, however the self-reported nature and sparsity of such data mean that such data are noisier and less specific than physiological measures such as blood glucose values themselves. Therefore, here we propose a Gaussian Process model to both address these data challenges and combine blood glucose and latent feature representations of contextual data for a novel multi-signal blood glucose prediction task. We find this approach outperforms common methods for multi-variate data, as well as using the blood glucose values in isolation. Given a robust evaluation across two blood glucose datasets with different forms of contextual information, we conclude that multi-signal Gaussian Processes can improve blood glucose prediction by using contextual information and may provide a significant shift in blood glucose prediction research and practice.