Related papers: Patient Similarity Analysis with Longitudinal Heal…
In epidemiological and clinical studies, identifying patients' phenotypes based on longitudinal profiles is critical to understanding the disease's developmental patterns. The current study was motivated by data from a Canadian birth cohort…
Doctors often rely on their past experience in order to diagnose patients. For a doctor with enough experience, almost every patient would have similarities to key cases seen in the past, and each new patient could be viewed as a mixture of…
The proliferation of consumer health devices such as smart watches, sleep monitors, smart scales, etc, in many countries, has not only led to growing interest in health monitoring, but also to the development of a countless number of…
Developing artificial intelligence (AI) tools for healthcare is a collaborative effort, bringing data scientists, clinicians, patients and other disciplines together. In this paper, we explore the collaborative data practices of research…
We present a new algorithm for clustering longitudinal data. Data of this type can be conceptualized as consisting of individuals and, for each such individual, observations of a time-dependent variable made at various times. Generically,…
Traditional machine learning methods face two main challenges in dealing with healthcare predictive analytics tasks. First, the high-dimensional nature of healthcare data needs labor-intensive and time-consuming processes to select an…
Clinicians decisions are becoming more and more evidence-based meaning in no other field the big data analytics so promising as in healthcare. Due to the sheer size and availability of healthcare data, big data analytics has revolutionized…
Nuanced cancer patient care is needed, as the development and clinical course of cancer is multifactorial with influences from the general health status of the patient, germline and neoplastic mutations, co-morbidities, and environment. To…
Electronic Health Records (EHRs) aggregate diverse information at the patient level, holding a trajectory representative of the evolution of the patient health status throughout time. Although this information provides context and can be…
In the last twenty years, data series similarity search has emerged as a fundamental operation at the core of several analysis tasks and applications related to data series collections. Many solutions to different mining problems work by…
The rapid development of diagnostic technologies in healthcare is leading to higher requirements for physicians to handle and integrate the heterogeneous, yet complementary data that are produced during routine practice. For instance, the…
The increasing capture and analysis of large-scale longitudinal health data offer opportunities to improve healthcare and advance medical understanding. However, a critical gap exists between (a) -- the observation of patterns and…
The connection between the design and delivery of health care services using information technology is known as health informatics. It involves data usage, validation, and transfer of an integrated medical analysis using neural networks of…
Health information technologies are transforming how mental healthcare is paid for through value-based care programs, which tie payment to data quantifying care outcomes. But, it is unclear what outcomes data these technologies should…
Electronic health records (EHR's) are only a first step in capturing and utilizing health-related data - the problem is turning that data into useful information. Models produced via data mining and predictive analysis profile inherited…
We provide an overview of the recent trends towards digitalization and large scale data analytics in healthcare. It is expected that these trends are instrumental in the dramatic changes in the way healthcare will be organized in the…
The availability of large and deep electronic healthcare records (EHR) datasets has the potential to enable a better understanding of real-world patient journeys, and to identify novel subgroups of patients. ML-based aggregation of EHR data…
In today's health care establishments there is a great diversity of information systems. Each with different specificities and capacities, proprietary communication methods, and hardly allow scalability. This set of characteristics hinders…
With recent advances in sensing and tracking technology, trajectory data is becoming increasingly pervasive and analysis of trajectory data is becoming exceedingly important. A fundamental problem in analyzing trajectory data is that of…
Personalization is being applied to great extend in many systems. This paper presents a multi-dimensional user data model and its application in web search. Online and Offline activities of the user are tracked for creating the user model.…