Biomechanisms of Comorbidity: Reviewing Integrative Analyses of Multi-omics Datasets and Electronic Health Records.

OBJECTIVES: Disease comorbidity is a pervasive phenomenon impacting patients' health outcomes, disease management, and clinical decisions. This review presents past, current and future research directions leveraging both phenotypic and molecular information to uncover disease similarity underpinning the biology and etiology of disease comorbidity. METHODS: We retrieved ~130 publications and retained 59, ranging from 2006 to 2015, that comprise a minimum number of five diseases and at least one type of biomolecule. We surveyed their methods, disease similarity metrics, and calculation of comorbidities in the electronic health records, if present. RESULTS: Among the surveyed studies, 44% generated or validated disease similarity metrics in context of comorbidity, with 60% being published in the last two years. As inputs, 87% of studies utilized intragenic loci and proteins while 13% employed RNA (mRNA, LncRNA or miRNA). Network modeling was predominantly used (35%) followed by statistics (28%) to impute similarity between these biomolecules and diseases. Studies with large numbers of biomolecules and diseases used network models or naïve overlap of disease-molecule associations, while machine learning, statistics, and information retrieval were utilized in smaller and moderate sized studies. Multiscale computations comprising shared function, network topology, and phenotypes were performed exclusively on proteins. CONCLUSION: This review highlighted the growing methods for identifying the molecular mechanisms underpinning comorbidities that leverage multiscale molecular information and patterns from electronic health records. The survey unveiled that intergenic polymorphisms have been overlooked for similarity imputation compared to their intragenic counterparts, offering new opportunities to bridge the mechanistic and similarity gaps of comorbidity.

Wnt7a is a novel inducer of ß-catenin-independent tumor-suppressive cellular senescence in lung cancer.

Cellular senescence is an initial barrier for carcinogenesis. However, the signaling mechanisms that trigger cellular senescence are incompletely understood, particularly in vivo. Here we identify Wnt7a as a novel upstream inducer of cellular senescence. In two different mouse strains (C57Bl/6J and FVB/NJ), we show that the loss of Wnt7a is a major contributing factor for increased lung tumorigenesis owing to reduced cellular senescence, and not reduced apoptosis, or autophagy. Wnt7a-null mice under de novo conditions and in both the strains display E-cadherin-to-N-cadherin switch, reduced expression of cellular senescence markers and reduced expression of senescence-associated secretory phenotype, indicating a genetic predisposition of these mice to increased carcinogen-induced lung tumorigenesis. Interestingly, Wnt7a induced an alternate senescence pathway, which was independent of ß-catenin, and distinct from that of classical oncogene-induced senescence mediated by the well-known p16(INK4a) and p19(ARF) pathways. Mechanistically, Wnt7a induced cellular senescence via inactivation of S-phase kinase-associated protein 2, an important alternate regulator of cellular senescence. Additionally, we identified Iloprost, a prostacyclin analog, which initiates downstream signaling cascades similar to that of Wnt7a, as a novel inducer of cellular senescence, presenting potential future clinical translational strategies. Thus pro-senescence therapies using either Wnt7a or its mimic, Iloprost, might represent a new class of therapeutic treatments for lung cancer.

Terminological mapping for high throughput comparative biology of phenotypes.

Comparative biological studies have led to remarkable biomedical discoveries. While genomic science and technologies are advancing rapidly, our ability to precisely specify a phenotype and compare it to related phenotypes of other organisms remains challenging. This study has examined the systematic use of terminology and knowledge based technologies to enable high-throughput comparative phenomics. More specifically, we measured the accuracy of a multi-strategy automated classification method to bridge the phenotype gap between a phenotypic terminology (MGD: Phenoslim) and a broad-coverage clinical terminology (SNOMED CT). Furthermore, we qualitatively evaluate the additional emerging properties of the combined terminological network for comparative biology and discovery science. According to the gold standard (n = 100), the accuracies (precision / recall) of the composite automated methods were 67% / 97% (mapping for identical concepts) and 85% / 98% (classification). Quantitatively, only 2% of the phenotypic concepts were missing from the clinical terminology, however, qualitatively the gap was larger: conceptual scope, granularity and subtle yet significant, homonymy problems were observed. These results suggest that, as observed in other domains, additional strategies are required for combining terminologies.

An evaluation of hybrid methods for matching biomedical terminologies: mapping the gene ontology to the UMLS.

Integration of disparate biomedical terminologies is becoming increasingly important as links between biological science and clinical medicine grow. Mapping concepts in the Gene Ontology (GO) to the UMLS may help further this integration and allow for more efficient information exchange among researchers. Using a gold standard of GO term--UMLS concept mappings provided by the NCI, we examined the performance of various published and combined mapping techniques, in order to maximize precision and recall. We found that for the previously published techniques precision varied between (0.61-0.95), and recall varied from (0.65-0.90), whereas for the hybrid techniques, precision varied between (0.66-0.97), and recall from (0.59-0.93). Our study reveals the benefits of using mapping techniques that incorporate domain knowledge, and provides a basis for future approaches to mapping between distinct biomedical vocabularies.

Linking biomedical language information and knowledge resources: GO and UMLS.

Integration of various informatics terminologies will be an essential activity towards supporting the advancement of both the biomedical and clinical sciences. The GO consortium has developed an impressive collection of biomedical terms specific to genes and proteins in a variety of organisms. The UMLS is a composite collection of various medical terminologies, pioneered by the National Library of Medicine. In the present study, we examine a variety of techniques for mapping terms from one terminology (GO) to another (UMLS), and describe their respective performances for a small, curated data set attained from the National Cancer Institute, which had precision values ranging from 30% (100% recall) to 95% (74% recall). Based on each technique's performance, we comment on how each can be used to enrich an existing terminology (UMLS) in future studies and how linking biological terminologies to UMLS differs from linking medical terminologies.

Developing tailored theory-based educational content for WEB applications: illustrations from the MI-HEART project.

This paper describes how theory facilitated the development of educational content for the MI-HEART project, a tailored Web-based intervention designed to favorably influence the appropriateness and rapidity of decision-making in patients suffering from symptoms of acute myocardial infarction. There were five steps involved: 1) formulating the behavioral goal, 2) defining intervention objectives based on an analyses of the determinants of behavior, 3) developing an assessment tool to measure a person's status on these determinants, 4) creating tailored content that address individual variation on determinants of the health behavior and, 5) developing algorithms and a computer program that link responses from the assessment to specific tailored communication. The approach we describe largely distinguishes Web-based applications that are designed to change health behavior from those that simply impart information. Developers of Web-based applications that propose to improve health status by modifying health-related behaviors need the understanding that although it is said that we live in an "information age", simply increasing knowledge has not been effective in changing behaviors in most instances. Furthermore, the one-size fits all approach to developing educational content cannot address the needs, concerns and interests of different individuals. With informatics technology, our ability to collect information from individuals and provide educational content tailored to the specific information collected is not only possibly, but practical.

Disambiguating ambiguous biomedical terms in biomedical narrative text: an unsupervised method.

With the growing use of Natural Language Processing (NLP) techniques for information extraction and concept indexing in the biomedical domain, a method that quickly and efficiently assigns the correct sense of an ambiguous biomedical term in a given context is needed concurrently. The current status of word sense disambiguation (WSD) in the biomedical domain is that handcrafted rules are used based on contextual material. The disadvantages of this approach are (i) generating WSD rules manually is a time-consuming and tedious task, (ii) maintenance of rule sets becomes increasingly difficult over time, and (iii) handcrafted rules are often incomplete and perform poorly in new domains comprised of specialized vocabularies and different genres of text. This paper presents a two-phase unsupervised method to build a WSD classifier for an ambiguous biomedical term W. The first phase automatically creates a sense-tagged corpus for W, and the second phase derives a classifier for W using the derived sense-tagged corpus as a training set. A formative experiment was performed, which demonstrated that classifiers trained on the derived sense-tagged corpora achieved an overall accuracy of about 97%, with greater than 90% accuracy for each individual ambiguous term.

A study of abbreviations in the UMLS.

Abbreviations are widely used in medicine. The understanding of abbreviations is important for medical language processing and information retrieval systems. The Unified Medical Language System (UMLS) contains a large number of abbreviations. We hypothesized that extracting and studying the UMLS abbreviations can be helpful for understanding the characteristics of abbreviations in medicine. In this paper, we describe a method for extracting abbreviations from the UMLS. We evaluated the method and studied the ambiguous nature of the abbreviations. In addition, the coverage of the UMLS abbreviations in medical reports was studied. Using our method, we extracted 163,666 unique (abbreviation, full form) pairs from the UMLS with a precision of 97.5%, and a recall of 96%. The UMLS abbreviations were highly ambiguous: 33.1% of abbreviations with six characters or less had multiple meanings; the average number of different full forms for all abbreviations with six characters or less was 2.28. The coverage of the UMLS abbreviations in medical reports was over 66%.

Automating SNOMED coding using medical language understanding: a feasibility study.

This paper evaluates qualitatively the use of the MedLEE natural language processing system to code medical narratives directly into the SNOMED nomenclature, while retaining the MedLEE information model data structure. A gold standard is produced from narrative text manually coded in SNOMED. An automated parsing and SNOMED-coding of the narrative text is then automatically generated by MedLEE. By comparing MedLEE s output to that of the Gold Standard, the capacities of SNOMED and MedLEE to represent the clinical information are subsequently evaluated leading to qualitative observations on their respective strengths and constraints. In this study, MedLEE did code to SNOMED and captures the codes in a sub-structure amenable to interoperability with the description logic of SNOMED RT, showing an approach that augments and formalizes SNOMED s compositional representation methods to accurately capture information from clinical narratives.

Formal combinations of guidelines: a requirement for self-administered personalized health education.

This paper addresses a process in which we combined educational guidelines (EG) from heterogeneous sources in one set of coherent computable statements to support dynamically generated and precisely tailored patient education material. The Guideline Interchange Format (GLIF), predicate logic and decision tables were assessed. An extended formalism of GLIF was applied to break up composite sentences of the educational material in atomic sentences. The differentiation of atomic sentences and combinations of atomic sentences from heterogeneous sources lead to a simplified overall content and model, and a significant reduction of conditional sentences in the EG. The resulting streamlined and personalized guidelines are expected to provide an improved user experience.

Modeling patient response to acute myocardial infarction: implications for a tailored technology-based program to reduce patient delay.

We are examining ways in which a clinical information system can favorably influence the appropriateness and rapidity of decision-making in patients suffering from symptoms of acute myocardial infarction. In order to do so, we have developed a theoretically based cognitive model for patient decision making. Our model includes somatic and emotional awareness, perceived threat (vulnerability and susceptibility), expectations of symptoms, self-efficacy and response efficacy to explain the response of an individual their symptoms. Variables are explained within a framework that details how they are interrelated in the context of other moderating variables. With an understanding of the decision process, we are able to collect, maintain and access patient specific data to tailor technology-based interventions unique to the requirements of each individual at various phases of the decision process. Existing clinical information systems at Columbia-Presbyterian Medical Center already address issues related to patient relevant on-line data. Other patient specific information will be collected through on-line questionnaires. By basing our approach on the use of a cognitive model, we can assess the capacity of our interventions to modify variables important to the decision-making process, allowing us to pinpoint which interventions are effective and the reasons why they are ineffective.

The SNOMED model: a knowledge source for the controlled terminology of the computerized patient record.

This article will present a description of the semi-implicit semantic properties of the Systematized Nomenclature of Human and Veterinary Medicine, also called SNOMED International. It will focus on the formalism of SNOMED and its computational properties using the disease definition as an example. Additional information will be provided in order to convert semi-implicit links found between SNOMED concepts into explicit computational semantic links.

Comparing SNOMED and ICPC retrieval accuracies using relational database models.

While SNOMED International has been generally accepted by the international community of pathologists, its use for primary and secondary care remains limited. This can probably be attributed to the coding complexity of clinical concepts into this multiaxial postcoordinated nomenclature. The SNOMED editors propose the use of multiple codes (aggregates) for any nuanced clinical concept, thus allowing alternative rigorous representations of the concept with SNOMED codes. Some classification critics argue whether such redundant coding precludes precise retrieval of data. This research was initiated to compare the retrieval accuracies of a relational database using a simplified model of SNOMED against a classification-based model. SNOMED-based queries showed improvement over ICPC-based queries, regardless of the use of SNOMED cross-references. The addition of the latter significantly improved the queries sensitivity and false negative rate. In conclusion, the authors recommend using aggregates of SNOMED codes in relational database designs over classification-based designs in order to improve retrieval accuracy.

PureMD: a Computerized Patient Record software for direct data entry by physicians using a keyboard-free pen-based portable computer.

This paper describes the data acquisition features of the PureMD Computerized Patient Record (CPR) software designed specifically for physicians. The physician uses a stylus to point, draw and handwrite on a Dynamic Dialog Interface that provides the same flexibility as the paper record and numerous other advantages. The clinical data thus entered is highly organized, easily legible and retrievable in many ways. The underlying Medical Knowledge Base (MKB) was optimized for rapid, intuitive and consistent data entry and automatic coding with minimum handwriting.