Subdomain Adaptation With Manifolds Discrepancy Alignment.

Reducing domain divergence is a key step in transfer learning. Existing works focus on the minimization of global domain divergence. However, two domains may consist of several shared subdomains, and differ from each other in each subdomain.

Digital Health during COVID-19: Informatics Dialogue with the World Health Organization.

Background: On December 16, 2020 representatives of the International Medical Informatics Association (IMIA), a Non-Governmental Organization in official relations with the World Health Organization (WHO), along with its International Academy for Health Sciences Informatics (IAHSI), held an open dialogue with WHO Director General (WHO DG) Tedros Adhanom Ghebreyesus about the opportunities and challenges of digital health during the COVID-19 global pandemic.

Objectives: The aim of this paper is to report the outcomes of the dialogue and discussions with more than 200 participants representing different civil society organizations (CSOs).

Methods: The dialogue was held in form of a webinar.

Modeling Multi-View Dependence in Bayesian Networks for Alzheimer's Disease Detection.

Early detection of Alzheimer's disease is important for deploying interventions to prevent or slow disease progression. We propose a multi-view dependence modeling framework that integrates multiple data sources to distinguish patients at different stages of the disease. We design interpretable models that can handle heterogeneous data types including neuro-images, bio- and clinical markers, and historical and genotypical characteristics of the subjects.

Knowledge-Driven Interpretation of Multi-View Data in Medicine.

We propose a new approach to clinical decision support with interpretable recommendations from multi-view data. We introduce a Bayesian network structure learning method to help identify the relevant factors and their relationships. Guided by minimal domain knowledge, this method highlights the significant associations among the demography, medical and family history, lifestyle, and biomarker data to facilitate informed clinical decisions.

A Deep Learning Approach to Neuroanatomical Characterisation of Alzheimer's Disease.

Alzheimer's disease (AD) is a neurological degenerative disorder that leads to progressive mental deterioration. This work introduces a computational approach to improve our understanding of the progression of AD. We use ensemble learning methods and deep neural networks to identify salient structural correlations among brain regions that degenerate together in AD; this provides an understanding of how AD progresses in the brain.

Research Strategies for Biomedical and Health Informatics. Some Thought-provoking and Critical Proposals to Encourage Scientific Debate on the Nature of Good Research in Medical Informatics.

Background: Medical informatics, or biomedical and health informatics (BMHI), has become an established scientific discipline. In all such disciplines there is a certain inertia to persist in focusing on well-established research areas and to hold on to well-known research methodologies rather than adopting new ones, which may be more appropriate.

Objectives: To search for answers to the following questions: What are research fields in informatics, which are not being currently adequately addressed, and which methodological approaches might be insufficiently used? Do we know about reasons? What could be consequences of change for research and for education?

Methods: Outstanding informatics scientists were invited to three panel sessions on this topic in leading international conferences (MIE 2015, Medinfo 2015, HEC 2016) in order to get their answers to these questions.

Fusing Heterogeneous Data for Alzheimer's Disease Classification.

In multi-view learning, multimodal representations of a real world object or situation are integrated to learn its overall picture. Feature sets from distinct data sources carry different, yet complementary, information which, if analysed together, usually yield better insights and more accurate results. Neuro-degenerative disorders such as dementia are characterized by changes in multiple biomarkers.

Automatic detection and quantification of brain midline shift using anatomical marker model.

Brain midline shift (MLS) is a significant factor in brain CT diagnosis. In this paper, we present a new method of automatically detecting and quantifying brain midline shift in traumatic injury brain CT images. The proposed method automatically picks out the CT slice on which midline shift can be observed most clearly and uses automatically detected anatomical markers to delineate the deformed midline and quantify the shift.

Unsupervised medical image classification by combining case-based classifiers.

We introduce an automated pathology classification system for medical volumetric brain image slices. Existing work often relies on handcrafted features extracted from automatic image segmentation. This is not only a challenging and time-consuming process, but it may also limit the adaptability and robustness of the system.

An automated pathological class level annotation system for volumetric brain images.

We introduce an automated, pathological class level annotation system for medical volumetric brain images. While much of the earlier work has mainly focused on annotating regions of interest in medical images, our system does not require annotated region level training data nor assumes perfect segmentation results for the regions of interest; the time and effort needed for acquiring training data are hence significantly reduced. This capability of handling high-dimensional noisy data, however, poses additional technical challenges, since statistical estimation of models for such data is prone to over-fitting.

A generative model based approach to retrieving ischemic stroke images.

This paper proposes a generative model approach to automatically annotate medical images to improve the efficiency and effectiveness of image retrieval systems for teaching, research, and diagnosis. The generative model captures the probabilistic relationships among relevant classification tags, tentative lesion patterns, and selected input features. Operating on the imperfect segmentation results of input images, the probabilistic framework can effectively handle the inherent uncertainties in the images and insufficient information in the training data.

Efficient mining of haplotype patterns for linkage disequilibrium mapping.

Effective identification of disease-causing gene locations can have significant impact on patient management decisions that will ultimately increase survival rates and improve the overall quality of health care. Linkage disequilibrium mapping is the process of finding disease gene locations through comparisons of haplotype frequencies between disease chromosomes and normal chromosomes. This work presents a new method for linkage disequilibrium mapping.

A model driven approach to imbalanced data sampling in medical decision making.

Classification is an important medical decision support function that can be seriously affected by disproportionate class distribution in the training data. In medical decision making, the rate of misclassification and the cost of misclassifying a minority (positive) class as a majority (negative) class are especially high. In this paper, we propose a new model-driven sampling approach to balancing data samples.

Towards a multi-level game model for influenza epidemics.

Although game theory has been first invented to reason with economic scenarios with rational agents, it has since been extended into many other fields including biological and medical sciences. In this paper we propose to model the interactions between virus and human in an influenza epidemic in a two player, adversarial game scenario with multiple levels of abstraction. As conventional game representations are inadequate in this complex problem domain, we propose Object Oriented Multi-Agent Influence Diagrams (OO-MAID), a novel graphical representation for multi-level games, which takes advantage of both organizational information and probabilistic independence in the problem domain.

Writing for publication in biomedical informatics.

Writing for publication can be a rewarding activity for researchers at all levels of experience. However, many students and researchers are less familiar with the various aspects of the publication process. The purpose of this workshop is to provide participants with the knowledge, skills, and practical advice that can lead to successful scientific publications.

ECTracker--an efficient algorithm for haplotype analysis and classification.

This work aims at discovering the genetic variations of hemophilia A patients through examining the combination of molecular haplotypes present in hemophilia A and normal local populations using data mining methods. Data mining methods that are capable of extracting understandable and expressive patterns and also capable of making predictions based on inferences made on the patterns were explored in this work. An algorithm known as ECTracker is proposed and its performance compared with some common data mining methods such as artificial neural network, support vector machine, naive Bayesian, and decision tree (C4.

Predicting coronary artery disease with medical profile and gene polymorphisms data.

Coronary artery disease (CAD) is a main cause of death in the world. Finding cost-effective methods to predict CAD is a major challenge in public health. In this paper, we investigate the combined effects of genetic polymorphisms and non-genetic factors on predicting the risk of CAD by applying well known classification methods, such as Bayesian networks, naïve Bayes, support vector machine, k-nearest neighbor, neural networks and decision trees.

Biomedical knowledge discovery with topological constraints modeling in Bayesian networks: a preliminary report.

Serving as exploratory data analysis tools, Bayesian networks (BNs) can be automatically learned from data to compactly model direct dependency relationships among the variables in a domain. A major challenge in BN learning is to effectively represent and incorporate domain knowledge in the learning process to improve its efficiency and accuracy. In this paper, we examine two types of domain knowledge representation in BNs: matrix and rule.

Hybrid outcome prediction model for severe traumatic brain injury.

Numerous studies addressing different methods of head injury prognostication have been published. Unfortunately, these studies often incorporate different head injury prognostication models and study populations, thus making direct comparison difficult, if not impossible. Furthermore, newer artificial intelligence tools such as machine learning methods have evolved in the field of data analysis, alongside more traditional methods of analysis.

Set-based Cascading Approaches for Magnetic Resonance (MR) Image Segmentation (SCAMIS).

This paper introduces Set-based Cascading Approach for Medical Image Segmentation (SCAMIS), a new methodology for segmentation of medical imaging by integrating a number of algorithms. Existing approaches typically adopt the pipeline methodology. Although these methods provide promising results, the results generated are still susceptible to over-segmentation and leaking.

Patient-specific inference and situation-dependent classification using Context-Sensitive Networks.

Representations and inferences that capture a formal notion of "context" are needed to effectively support various analytic and learning tasks in many biomedical applications. In this paper, we formulate patient-specific inference and situation-dependent classification as context-aware reasoning tasks that can be effectively supported in probabilistic graphical networks. We introduce a new probabilistic graphical framework of Context Sensitive Networks (CSNs) to efficiently represent and reason with context-sensitive knowledge.