Extraction and Quantification of Words Representing Degrees of Diseases: Combining the Fuzzy C-Means Method and Gaussian Membership.

BACKGROUND: Due to the development of medical data, a large amount of clinical data has been generated. These unstructured data contain substantial information. Extracting useful knowledge from this data and making scientific decisions for diagnosing and treating diseases have become increasingly necessary. Unstructured data, such as in the Marketplace for Medical Information in Intensive Care III (MIMIC-III) data set, contain several ambiguous words that demonstrate the subjectivity of doctors, such as descriptions of patient symptoms. These data could be used to further improve the accuracy of medical diagnostic system assessments. To the best of our knowledge, there is currently no method for extracting subjective words that express the extent of these symptoms (hereinafter, "degree words"). OBJECTIVE: Therefore, we propose using the fuzzy c-means (FCM) method and Gaussian membership to quantify the degree words in the clinical medical data set MIMIC-III. METHODS: First, we preprocessed the 381,091 radiology reports collected in MIMIC-III, and then we used the FCM method to extract degree words from unstructured text. Thereafter, we used the Gaussian membership method to quantify the extracted degree words, which transform the fuzzy words extracted from the medical text into computer-recognizable numbers. RESULTS: The results showed that the digitization of ambiguous words in medical texts is feasible. The words representing each degree of each disease had a range of corresponding values. Examples of membership medians were 2.971 (atelectasis), 3.121 (pneumonia), 2.899 (pneumothorax), 3.051 (pulmonary edema), and 2.435 (pulmonary embolus). Additionally, all extracted words contained the same subjective words (low, high, etc), which allows for an objective evaluation method. Furthermore, we will verify the specific impact of the quantification results of ambiguous words such as symptom words and degree words on the use of medical texts in subsequent studies. These same ambiguous words may be used as a new set of feature values to represent the disorders. CONCLUSIONS: This study proposes an innovative method for handling subjective words. We used the FCM method to extract the subjective degree words in the English-interpreted report of the MIMIC-III and then used the Gaussian functions to quantify the subjective degree words. In this method, words containing subjectivity in unstructured texts can be automatically processed and transformed into numerical ranges by digital processing. It was concluded that the digitization of ambiguous words in medical texts is feasible.

Cost-Effectiveness of a Continuous Glucose Monitoring Mobile App for Patients With Type 2 Diabetes Mellitus: Analysis Simulation.

BACKGROUND: Apps for real-time continuous glucose monitoring (CGM) on smartphones and other devices linked to CGM systems have recently been developed, and such CGM apps are also coming into use in Japan. In comparison with conventional retrospective CGM, the use of CGM apps improves patients' own blood glucose control, which is expected to help slow the progression of type 2 diabetes mellitus (DM) and prevent complications, but the effect of their introduction on medical costs remains unknown. OBJECTIVE: Our objective in this study was to perform an economic appraisal of CGM apps from the viewpoint of assessing public medical costs associated with type 2 DM, using the probability of developing type 2 DM-associated complications, and data on medical costs and utility value to carry out a medical cost simulation using a Markov model in order to ascertain the cost-effectiveness of the apps. METHODS: We developed a Markov model with the transition states of insulin therapy, nephrosis, dialysis, and cardiovascular disease, all of which have a major effect on medical costs, to identify changes in medical costs and utility values resulting from the introduction of a CGM app and calculated the incremental cost-effectiveness ratio (ICER). RESULTS: The ICER for CGM app use was US $33,039/quality-adjusted life year (QALY). CONCLUSIONS: Sensitivity analyses showed that, with the exception of conditions where the transition probability of insulin therapy, utility value, or increased medical costs increases, the ICER for the introduction of CGM apps was below the threshold of US $43,478/QALY used by the Central Social Insurance Medical Council. Our results provide basic data on the cost-effectiveness of introducing CGM apps, which are currently starting to come into use.

The Pre-Eclampsia Ontology: A Disease Ontology Representing the Domain Knowledge Specific to Pre-Eclampsia.

Pre-eclampsia (PE) is a clinical syndrome characterized by new-onset hypertension and proteinuria at ≥20 weeks of gestation, and is a leading cause of maternal and perinatal morbidity and mortality. Previous studies have gathered abundant data about PE such as risk factors and pathological findings. However, most of these data are not semantically structured. Clinical data on PE patients are often generated with semantic heterogeneity such as using disparate terminology to describe the same phenomena. In clinical studies, interoperability of heterogenic clinical data is required in various situations. In such a situation, it is necessary to develop an interoperable and standardized semantic framework to research the pathology of PE more comprehensively and to achieve interoperability of heterogenic clinical data of PE patients. In this study, we developed an ontology representing clinical features, treatments, genetic factors, environmental factors, and other aspects of the current knowledge in the domain of PE. We call this pre-eclampsia ontology "PEO". To achieve interoperability with other ontologies, the core structure of PEO was compliant with the hierarchy of the Basic Formal Ontology (BFO). The PEO incorporates a wide range of key concepts and terms of PE from clinical and biomedical research in structuring the knowledge base that is specific to PE; therefore, PEO is expected to enhance PE-specific information retrieval and knowledge discovery in both clinical and biomedical research fields.

Network Analysis of a Comprehensive Knowledge Repository Reveals a Dual Role for Ceramide in Alzheimer's Disease.

Alzheimer's disease (AD) is the most common cause of senile dementia. Many inflammatory factors such as amyloid-ß and pro-inflammatory cytokines are known to contribute to the inflammatory response in the AD brain. Sphingolipids are widely known to have roles in the pathogenesis of inflammatory diseases, where the precise roles for sphingolipids in inflammation-associated pathogenesis of AD are not well understood. Here we performed a network analysis to clarify the importance of sphingolipids and to model relationships among inflammatory factors and sphingolipids in AD. In this study, we have updated sphingolipid signaling and metabolic cascades in a map of AD signaling networks that we named "AlzPathway," a comprehensive knowledge repository of signaling pathways in AD. Our network analysis of the updated AlzPathway indicates that the pathways related to ceramide are one of the primary pathways and that ceramide is one of the important players in the pathogenesis of AD. The results of our analysis suggest the following two prospects about inflammation in AD: (1) ceramide could play important roles in both inflammatory and anti-inflammatory pathways of AD, and (2) several factors such as Sphingomyelinase and Siglec-11 may be associated with ceramide related inflammation and anti-inflammation pathways in AD. In this study, network analysis of comprehensive knowledge repository reveals a dual role for ceramide in AD. This result provides a clue to clarify sphingolipids related inflammatory and anti-inflammatory pathways in AD.

AlzPathway, an Updated Map of Curated Signaling Pathways: Towards Deciphering Alzheimer's Disease Pathogenesis.

Alzheimer's disease (AD) is a complex neurodegenerative disorder in which loss of neurons and synaptic function causes dementia in the elderly. To clarify AD pathogenesis and develop drugs for AD, thousands of studies have elucidated signaling pathways involved. However, knowledge of AD signaling pathways has not been compiled as a pathway map. In this chapter, we introduce the manual construction of a pathway map in AD which we call "AlzPathway", that comprehensively catalogs signaling pathways in the field of AD. We have collected and manually curated over 100 review articles related to AD, and have built the AD pathway map. AlzPathway is currently composed of thousands of molecules and reactions in neurons, brain blood barrier, presynaptic, postsynaptic, astrocyte, and microglial cells, with their cellular localizations. AlzPathway provides a systems-biology platform of comprehensive AD signaling and related pathways which is expected to contribute to clarification of AD pathogenesis and AD drug development.

Network-Based Analysis for Uncovering Mechanisms Underlying Alzheimer's Disease.

Alzheimer's disease (AD) is known to be a multifactorial neurodegenerative disorder, and is one of the main causes of dementia in the elderly. Many studies have demonstrated molecules involved in the pathogenesis of AD, however its underlying mechanisms remain obscure. It may be simplistic to try to explain the disease based on the role of a few genes only. Accumulating new, huge amount of information from e.g. genome, proteome and interactome datasets and new knowledge, we are now able to clarify and characterize diseases essentially as a result of dysfunction of molecular networks. Recent studies have indicated that relevant genes affected in human diseases concentrate in a part of the network, often called as "disease module." In the case of AD, some disease-associated pathways seem different, but some of them are clearly disease-related and coherent. This suggests the existence of a common pathway that negatively drives from healthy state to disease state (i.e., the disease module(s)). Additionally, such disease modules should dynamically change through AD progression. Thus, network-level approaches are indispensable to address unknown mechanisms of AD. In this chapter, we introduce network strategies using gene co-expression and protein interaction networks.

Integrated Database And Knowledge Base For Genomic Prospective Cohort Study In Tohoku Medical Megabank Toward Personalized Prevention And Medicine.

The Tohoku Medical Megabank project is a national project to revitalization of the disaster area in the Tohoku region by the Great East Japan Earthquake, and have conducted large-scale prospective genome-cohort study. Along with prospective genome-cohort study, we have developed integrated database and knowledge base which will be key database for realizing personalized prevention and medicine.

Audit Trail Management System in Community Health Care Information Network.

After the Great East Japan Earthquake we constructed a community health care information network system. Focusing on the authentication server and portal server capable of SAML&ID-WSF, we proposed an audit trail management system to look over audit events in a comprehensive manner. Through implementation and experimentation, we verified the effectiveness of our proposed audit trail management system.

Development of an Ontology for Periodontitis.

BACKGROUND: In the clinical dentists and periodontal researchers' community, there is an obvious demand for a systems model capable of linking the clinical presentation of periodontitis to underlying molecular knowledge. A computer-readable representation of processes on disease development will give periodontal researchers opportunities to elucidate pathways and mechanisms of periodontitis. An ontology for periodontitis can be a model for integration of large variety of factors relating to a complex disease such as chronic inflammation in different organs accompanied by bone remodeling and immune system disorders, which has recently been referred to as osteoimmunology. METHODS: Terms characteristic of descriptions related to the onset and progression of periodontitis were manually extracted from 194 review articles and PubMed abstracts by experts in periodontology. We specified all the relations between the extracted terms and constructed them into an ontology for periodontitis. We also investigated matching between classes of our ontology and that of Gene Ontology Biological Process. RESULTS: We developed an ontology for periodontitis called Periodontitis-Ontology (PeriO). The pathological progression of periodontitis is caused by complex, multi-factor interrelationships. PeriO consists of all the required concepts to represent the pathological progression and clinical treatment of periodontitis. The pathological processes were formalized with reference to Basic Formal Ontology and Relation Ontology, which accounts for participants in the processes realized by biological objects such as molecules and cells. We investigated the peculiarity of biological processes observed in pathological progression and medical treatments for the disease in comparison with Gene Ontology Biological Process (GO-BP) annotations. The results indicated that peculiarities of Perio existed in 1) granularity and context dependency of both the conceptualizations, and 2) causality intrinsic to the pathological processes. PeriO defines more specific concepts than GO-BP, and thus can be added as descendants of GO-BP leaf nodes. PeriO defines causal relationships between the process concepts, which are not shown in GO-BP. The difference can be explained by the goal of conceptualization: PeriO focuses on mechanisms of the pathogenic progress, while GO-BP focuses on cataloguing all of the biological processes observed in experiments. The goal of conceptualization in PeriO may reflect the domain knowledge where a consequence in the causal relationships is a primary interest. We believe the peculiarities can be shared among other diseases when comparing processes in disease against GO-BP. CONCLUSIONS: This is the first open biomedical ontology of periodontitis capable of providing a foundation for an ontology-based model of aspects of molecular biology and pathological processes related to periodontitis, as well as its relations with systemic diseases. PeriO is available at http://bio-omix.tmd.ac.jp/periodontitis/.

Modularity in the evolution of yeast protein interaction network.

Protein interaction networks are known to exhibit remarkable structures: scale-free and small-world and modular structures. To explain the evolutionary processes of protein interaction networks possessing scale-free and small-world structures, preferential attachment and duplication-divergence models have been proposed as mathematical models. Protein interaction networks are also known to exhibit another remarkable structural characteristic, modular structure. How the protein interaction networks became to exhibit modularity in their evolution? Here, we propose a hypothesis of modularity in the evolution of yeast protein interaction network based on molecular evolutionary evidence. We assigned yeast proteins into six evolutionary ages by constructing a phylogenetic profile. We found that all the almost half of hub proteins are evolutionarily new. Examining the evolutionary processes of protein complexes, functional modules and topological modules, we also found that member proteins of these modules tend to appear in one or two evolutionary ages. Moreover, proteins in protein complexes and topological modules show significantly low evolutionary rates than those not in these modules. Our results suggest a hypothesis of modularity in the evolution of yeast protein interaction network as systems evolution.

A method to associate all possible combinations of genetic and environmental factors using GxE landscape plot.

UNLABELLED: Genome-wide association studies (GWAS) and linkage analysis has identified many single nucleotide polymorphisms (SNPs) related to disease. There are many unknown SNPs whose minor allele frequencies (MAFs) as low as 0.005 having intermediate effects with odds ratio between 1.5~3.0. Low frequency variants having intermediate effects on disease pathogenesis are believed to have complex interactions with environmental factors called gene-environment interactions (GxE). Hence, we describe a model using 3D Manhattan plot called GxE landscape plot to visualize the association of p-values for gene-environment interactions (GxE). We used the Gene-Environment iNteraction Simulator 2 (GENS2) program to simulate interactions between two genetic loci and one environmental factor in this exercise. The dataset used for training contains disease status, gender, 20 environmental exposures and 100 genotypes for 170 subjects, and p-values were calculated by Cochran-Mantel-Haenszel chi-squared test on known data. Subsequently, we created a 3D GxE landscape plot of negative logarithm of the association of p-values for all the possible combinations of genetic and environmental factors with their hierarchical clustering. Thus, the GxE landscape plot is a valuable model to predict association of p-values for GxE and similarity among genotypes and environments in the context of disease pathogenesis. ABBREVIATIONS: GxE - Gene-environment interactions, GWAS - Genome-wide association study, MAFs - Minor allele frequencies, SNPs - Single nucleotide polymorphisms, EWAS - Environment-wide association study, FDR - False discovery rate, JPT+CHB - HapMap population of Japanese in Tokyo, Japan - Han Chinese in Beijing.

A survey aimed at general citizens of the US and Japan about their attitudes toward electronic medical data handling.

OBJECTIVES: To clarify the views of the general population of two countries (US and Japan), concerning the handling of their medical records electronically. METHODS: We contacted people nationwide in the United States at random via Random Digit Dialing (RDD) to obtain 200 eligible responders. The questionnaire was for obtaining the information on their attitudes towards handling of their medical records, disclosure of the name of disease, secondary usage of information, compiling their records into a lifelong medical record, and access to their medical records on the Internet. We had also surveyed people of Shizuoka prefecture in Japan using same questionnaires sent by mail, for which we obtained 457 valid answers. RESULTS: Even in an unidentifiable manner, US people feel profit-oriented usage of medical data without specific consent is not acceptable. There is a significant difference between usage of unidentifiable medical data for profit (about 50% feel negatively) and for official/research purposes (about 30% feel negatively). About 60% of the US responders have a negative view on the proposal that unidentifiable medical information be utilized for profit by private companies to attain healthcare cost savings. As regards compiling a lifelong medical record, positive answers and negative answers are almost equally divided in the US (46% vs. 38%) while more positive attitudes are seen in Japan (74% vs. 12%). However, any incentive measures aimed at changing attitudes to such a compiling including the discount of healthcare costs or insurance fees are unwelcomed by people regardless of their age or health condition in both surveys. Regarding the access to their own medical record via the Internet, 38% of the US responders feel this is unacceptable while 50.5% were willing to accept it. CONCLUSIONS: Participants from the US think that the extent of the sharing their identifiable medical records should be limited to the doctors-in-charge and specified doctors referred to by their own doctors. On the other hand, Japanese people find it acceptable for doctors of the same hospital to share their medical records. Even in unidentifiable manner, people in both countries think the profits resulting from the secondary use of medical records should be returned to the public or patients. With regard to compiling a lifelong medical record, participants from the US provided both positive answers and negative answers, while more positive attitudes were observed in Japan. However, any incentives or measures aimed at changing attitudes towards such a compilation, including provision of a discount on healthcare costs or insurance fees, were not welcomed by participants from US as well as those from Japan, regardless of their age or health condition.

Identification of unstable network modules reveals disease modules associated with the progression of Alzheimer's disease.

Alzheimer's disease (AD), the most common cause of dementia, is associated with aging, and it leads to neuron death. Deposits of amyloid ß and aberrantly phosphorylated tau protein are known as pathological hallmarks of AD, but the underlying mechanisms have not yet been revealed. A high-throughput gene expression analysis previously showed that differentially expressed genes accompanying the progression of AD were more down-regulated than up-regulated in the later stages of AD. This suggested that the molecular networks and their constituent modules collapsed along with AD progression. In this study, by using gene expression profiles and protein interaction networks (PINs), we identified the PINs expressed in three brain regions: the entorhinal cortex (EC), hippocampus (HIP) and superior frontal gyrus (SFG). Dividing the expressed PINs into modules, we examined the stability of the modules with AD progression and with normal aging. We found that in the AD modules, the constituent proteins, interactions and cellular functions were not maintained between consecutive stages through all brain regions. Interestingly, the modules were collapsed with AD progression, specifically in the EC region. By identifying the modules that were affected by AD pathology, we found the transcriptional regulation-associated modules that interact with the proteasome-associated module via UCHL5 hub protein, which is a deubiquitinating enzyme. Considering PINs as a system made of network modules, we found that the modules relevant to the transcriptional regulation are disrupted in the EC region, which affects the ubiquitin-proteasome system.

Genomic Sequence Variation Markup Language (GSVML).

OBJECTIVE: With the aim of making good use of internationally accumulated genomic sequence variation data, which is increasing rapidly due to the explosive amount of genomic research at present, the development of an interoperable data exchange format and its international standardization are necessary. Genomic Sequence Variation Markup Language (GSVML) will focus on genomic sequence variation data and human health applications, such as gene based medicine or pharmacogenomics. DESIGN AND METHOD: We developed GSVML through eight steps, based on case analysis and domain investigations. By focusing on the design scope to human health applications and genomic sequence variation, we attempted to eliminate ambiguity and to ensure practicability. We intended to satisfy the requirements derived from the use case analysis of human-based clinical genomic applications. Based on database investigations, we attempted to minimize the redundancy of the data format, while maximizing the data covering range. We also attempted to ensure communication and interface ability with other Markup Languages, for exchange of omics data among various omics researchers or facilities. The interface ability with developing clinical standards, such as the Health Level Seven Genotype Information model, was analyzed. RESULTS: We developed the human health-oriented GSVML comprising variation data, direct annotation, and indirect annotation categories; the variation data category is required, while the direct and indirect annotation categories are optional. The annotation categories contain omics and clinical information, and have internal relationships. For designing, we examined 6 cases for three criteria as human health application and 15 data elements for three criteria as data formats for genomic sequence variation data exchange. The data format of five international SNP databases and six Markup Languages and the interface ability to the Health Level Seven Genotype Model in terms of 317 items were investigated. CONCLUSION: GSVML was developed as a potential data exchanging format for genomic sequence variation data exchange focusing on human health applications. The international standardization of GSVML is necessary, and is currently underway. GSVML can be applied to enhance the utilization of genomic sequence variation data worldwide by providing a communicable platform between clinical and research applications.

Research and education for biomedical informatics at Tokyo Medical and Dental University.

OBJECTIVES: Based on a basic concept of "Systems Life Science: understanding life and disease as a unified system", we move forward in research, empirical implementation, and making contributions to healthcare policy. METHODS: We integrate bioinformatics and medical informatics for identifying critical issues in biological science and solving medical challenges with a concept of "Systems Life Science" which consists of "Systems Evolutionary Biology" for basic science, "Systems Pathology" for clinical sciences, and an empirical medical informatics for future medicine. RESULTS: Our laboratory is an integrated laboratory consisting of a computational biology group in the School of Biomedical Sciences (SBS), a bioinformatics group in the Medical Research Institute (MRI), and a medical informatics group in the Information Center for Medical Sciences (ICMS) with a philosophy of "Empirical Systems Life Science". CONCLUSIONS: Based on the philosophy of "Empirical Systems Life Science", we continue to forward our research, education, systems implementations, and international standardization efforts. We believe that this approach will become a fundamental and effective way to uncover many of the secrets of life processes, and to help/solve complex issues for future medicine in this post genomic era with exceedingly rapidly growing amounts of -omics data and knowledge.

Interface analysis between GSVML and HL7 version 3.

In order to realize gene-based medicine, a number of key challenges must be overcome. Construction of infrastructure capable of integrating genetic and clinical information is one of those challenges. The Genomic Sequence Variation Markup Language (GSVML) and the Health Level Seven Version 3 (HL7v3) are important electronic data exchange standards for clinical genome infrastructure, and compatibility between these two standards will promote the above integration. In this study, we analyzed the interface between GSVML and HL7v3, primarily for the Clinical Genomics Domain, from a view of the GSVML, and were able to create a blueprint for a functional interface between GSVML and HL7v3. We expect that these analytical results will help accelerate the realization of gene-based medicine.