Introduction to the ACM TIST Special Issue on Intelligent Healthcare Informatics

Healthcare Informatics is a research area dealing with the study and application of computer science and information and communication technology to face both theoretical/methodological and practical issues in healthcare, public health, and everyday wellness. Intelligent Healthcare Informatics may be defined as the specific area focusing on the use of artificial intelligence (AI) theories and techniques to offer important services (such as a component of complex systems) to allow integrated systems to perceive, reason, learn, and act intelligently in the healthcare arena. One of the many peculiarities of healthcare is that decision support systems need to be integrated with several heterogeneous systems supporting both collaborative work and process coordination and the management and analysis of a huge amount of clinical and health data, to compose intelligent, process-aware health information systems. After some pioneering work focusing explicitly on specific medical aspects and providing some efficient, even ad hoc, solutions, in recent years, AI in healthcare has been faced by researchers with different backgrounds and interests, taking into consideration the main results obtained in the more general and theoretical/methodological area of intelligent systems. Moreover, from a focus on reasoning strategies and deep knowledge representation, research in healthcare intelligent systems moved to data-intensive clinical tasks, where there is the need for supporting healthcare decision making in the presence of overwhelming amounts of clinical data. Significant solutions have been provided through a multidisciplinary combination of the results from the different research areas and their associated cultures, ranging from algorithms, to information systems and databases, to human-computer interaction, to medical informatics. To this regard, it is interesting to observe that, from one side, medical informaticians benefited by the general solutions coming from the generic computer science area, tailoring them to specific medical domains, while from the other side, computer scientists found several (still open) challenges in the medical and, more generally, health domains. This ACM Transactions on Intelligent Systems and Technology (ACM TIST) special issue contains articles discussing fundamental principles, algorithms, or applications for process-aware health information systems. Such articles are a sound answer to the research challenges for novel techniques, combinations of tools, and so forth to build effective ways to manage and deal in an integrated way with healthcare processes and data

Estimating a Ranked List of Human Genetic Diseases by Associating Phenotype-Gene with Gene-Disease Bipartite Graphs

With vast amounts of medical knowledge available on the Internet, it is becoming increasingly practical to help doctors in clinical diagnostics by suggesting plausible diseases predicted by applying data and text mining technologies. Recently, Genome-Wide Association Studies (GWAS) have proved useful as a method for exploring phenotypic associations with diseases. However, since genetic diseases are difficult to diagnose because of their low prevalence, large number, and broad diversity of symptoms, genetic disease patients are often misdiagnosed or experience long diagnostic delays. In this article, we propose a method for ranking genetic diseases for a set of clinical phenotypes. In this regard, we associate a phenotype-gene bipartite graph (PGBG) with a gene-disease bipartite graph (GDBG) by producing a phenotype-disease bipartite graph (PDBG), and we estimate the candidate weights of diseases. In our approach, all paths from a phenotype to a disease are explored by considering causative genes to assign a weight based on path frequency, and the phenotype is linked to the disease in a new PDBG. We introduce the Bidirectionally induced Importance Weight (BIW) prediction method to PDBG for approximating the weights of the edges of diseases with phenotypes by considering link information from both sides of the bipartite graph. The performance of our system is compared to that of other known related systems by estimating Normalized Discounted Cumulative Gain (NDCG), Mean Average Precision (MAP), and Kendall’s tau metrics. Further experiments are conducted with well-known TF · IDF, BM25, and Jenson-Shannon divergence as baselines. The result shows that our proposed method outperforms the known related tool Phenomizer in terms of NDCG@10, NDCG@20, MAP@10, and MAP@20; however, it performs worse than Phenomizer in terms of Kendall’s tau-b metric at the top-10 ranks. It also turns out that our proposed method has overall better performance than the baseline methods

Estimating a Ranked List of Human Genetic Diseases by Associating Phenotype-Gene with Gene-Disease Bipartite Graphs

With vast amounts of medical knowledge available on the Internet, it is becoming increasingly practical to help doctors in clinical diagnostics by suggesting plausible diseases predicted by applying data and text mining technologies. Recently, Genome-Wide Association Studies (GWAS) have proved useful as a method for exploring phenotypic associations with diseases. However, since genetic diseases are difficult to diagnose because of their low prevalence, large number, and broad diversity of symptoms, genetic disease patients are often misdiagnosed or experience long diagnostic delays. In this article, we propose a method for ranking genetic diseases for a set of clinical phenotypes. In this regard, we associate a phenotype-gene bipartite graph (PGBG) with a gene-disease bipartite graph (GDBG) by producing a phenotype-disease bipartite graph (PDBG), and we estimate the candidate weights of diseases. In our approach, all paths from a phenotype to a disease are explored by considering causative genes to assign a weight based on path frequency, and the phenotype is linked to the disease in a new PDBG. We introduce the Bidirectionally induced Importance Weight (BIW) prediction method to PDBG for approximating the weights of the edges of diseases with phenotypes by considering link information from both sides of the bipartite graph. The performance of our system is compared to that of other known related systems by estimating Normalized Discounted Cumulative Gain (NDCG), Mean Average Precision (MAP), and Kendall’s tau metrics. Further experiments are conducted with well-known TF · IDF, BM25, and Jenson-Shannon divergence as baselines. The result shows that our proposed method outperforms the known related tool Phenomizer in terms of NDCG@10, NDCG@20, MAP@10, and MAP@20; however, it performs worse than Phenomizer in terms of Kendall’s tau-b metric at the top-10 ranks. It also turns out that our proposed method has overall better performance than the baseline methods