Using Web Services for Linking Genomic Data to Medical Information Systems

Clinicomics-guided distant metastasis prediction in breast cancer via artificial intelligence

BACKGROUND

Breast cancer has become the most common malignant tumour worldwide. Distant metastasis is one of the leading causes of breast cancer-related death. To verify the performance of clinicomics-guided distant metastasis risk prediction for breast cancer via artificial intelligence and to investigate the accuracy of the created prediction models for metachronous distant metastasis, bone metastasis and visceral metastasis.

METHODS

We retrospectively enrolled 6703 breast cancer patients from 2011 to 2016 in our hospital. The figures of magnetic resonance imaging scanning and ultrasound were collected, and the figures features of distant metastasis in breast cancer were detected. Clinicomics-guided nomogram was proven to be with significant better ability on distant metastasis prediction than the nomogram constructed by only clinical or radiographic data.

RESULTS

Three clinicomics-guided prediction nomograms on distant metastasis, bone metastasis and visceral metastasis were created and validated. These models can potentially guide metachronous distant metastasis screening and lead to the implementation of individualized prophylactic therapy for breast cancer patients.

CONCLUSION

Our study is the first study to make cliniomics a reality. Such cliniomics strategy possesses the development potential in artificial intelligence medicine.

Upper-level Ontologies for Health Information Systems

Objectives: We examine the potential of archetype patterns for upper-level ontology development in health information systems (HISs).

Methods: Archetype patterns, based on the integration of archetype concepts and design patterns, are conceptualized and developed for ontology in the HIS domain. The UML provides the underlying modeling support.

Results: We argue in favor of the archetype pattern providing models for upper-level ontologies in HIS. This, in turn, has the potential to offer a foundation for interoperability across HIS applications and enterprises. Our research also shows the limitations of current ontology development methods as well as the challenges faced in archetype pattern development. The framework has potential for general, widespread usage. Overall, this approach supports ontology development in HIS.

Conclusions: The research demonstrates the applicability of archetype patterns to ontology development in HIS. While numerous ontologies exist in biomedicine, there are few well-developed ontologies for general healthcare. Properly modeled, archetype patterns have potential to reconcile the differences in high-level design views across health care inter-enterprises. Future research can focus on governance, standards and tools for archetype patterns, as well as development of a comprehensive set of high-level healthcare archetype patterns.

Improving oncology outcomes through targeted therapeutics will require electronic delivery systems

Typically, chemotherapy selection takes into account patient demographic data, including disease symptoms, family history, environmental factors and concurrent medications. Although validated and approved genomics tests are available for targeted therapeutics, a major challenge facing healthcare is the ability to process the genomic data in the patient’s context and to return clinically interpretable dosing guidance to the physician in a realistic time frame. Delivery of these targeted therapeutics, made possible by clinical decision support systems connected to an electronic health record may help drive both the acceptance and adaptation of an electronic health record system, as well as provide personalized information at point-of-care, as part of the routine workflow. The realization of targeted therapeutics will depend on the concerted efforts of stakeholder groups as they address political, ethical, socioeconomical and technical challenges to achieve personalized medicine adoption through real-world implementation.

Biomedical ontologies: toward scientific debate

OBJECTIVES

Biomedical ontologies have been very successful in structuring knowledge for many different applications, receiving widespread praise for their utility and potential. Yet, the role of computational ontologies in scientific research, as opposed to knowledge management applications, has not been extensively discussed. We aim to stimulate further discussion on the advantages and challenges presented by biomedical ontologies from a scientific perspective.

METHODS

We review various aspects of biomedical ontologies going beyond their practical successes, and focus on some key scientific questions in two ways. First, we analyze and discuss current approaches to improve biomedical ontologies that are based largely on classical, Aristotelian ontological models of reality. Second, we raise various open questions about biomedical ontologies that require further research, analyzing in more detail those related to visual reasoning and spatial ontologies.

RESULTS

We outline significant scientific issues that biomedical ontologies should consider, beyond current efforts of building practical consensus between them. For spatial ontologies, we suggest an approach for building "morphospatial" taxonomies, as an example that could stimulate research on fundamental open issues for biomedical ontologies.

CONCLUSIONS

Analysis of a large number of problems with biomedical ontologies suggests that the field is very much open to alternative interpretations of current work, and in need of scientific debate and discussion that can lead to new ideas and research directions.

Translational integrity and continuity: personalized biomedical data integration

Translational research data are generated in multiple research domains from the bedside to experimental laboratories. These data are typically stored in heterogeneous databases, held by segregated research domains, and described with inconsistent terminologies. Such inconsistency and fragmentation of data significantly impedes the efficiency of tracking and analyzing human-centered records. To address this problem, we have developed a data repository and management system named TraM (http://tram.uchicago.edu), based on a domain ontology integrated entity relationship model. The TraM system has the flexibility to recruit dynamically evolving domain concepts and the ability to support data integration for a broad range of translational research. The web-based application interfaces of TraM allow curators to improve data quality and provide robust and user-friendly cross-domain query functions. In its current stage, TraM relies on a semi-automated mechanism to standardize and restructure source data for data integration and thus does not support real-time data application.