Ontology-driven indexing of public datasets for translational bioinformatics

The volume of publicly available genomic scale data is increasing. Genomic datasets in public repositories are annotated with free-text fields describing the pathological state of the studied sample. These annotations are not mapped to concepts in any ontology, making it difficult to integrate these datasets across repositories. We have previously developed methods to map text-annotations of tissue microarrays to concepts in the NCI thesaurus and SNOMED-CT

Automation of a problem list using natural language processing

BACKGROUND: The medical problem list is an important part of the electronic medical record in development in our institution. To serve the functions it is designed for, the problem list has to be as accurate and timely as possible. However, the current problem list is usually incomplete and inaccurate, and is often totally unused. To alleviate this issue, we are building an environment where the problem list can be easily and effectively maintained. METHODS: For this project, 80 medical problems were selected for their frequency of use in our future clinical field of evaluation (cardiovascular). We have developed an Automated Problem List system composed of two main components: a background and a foreground application. The background application uses Natural Language Processing (NLP) to harvest potential problem list entries from the list of 80 targeted problems detected in the multiple free-text electronic documents available in our electronic medical record. These proposed medical problems drive the foreground application designed for management of the problem list. Within this application, the extracted problems are proposed to the physicians for addition to the official problem list. RESULTS: The set of 80 targeted medical problems selected for this project covered about 5% of all possible diagnoses coded in ICD-9-CM in our study population (cardiovascular adult inpatients), but about 64% of all instances of these coded diagnoses. The system contains algorithms to detect first document sections, then sentences within these sections, and finally potential problems within the sentences. The initial evaluation of the section and sentence detection algorithms demonstrated a sensitivity and positive predictive value of 100% when detecting sections, and a sensitivity of 89% and a positive predictive value of 94% when detecting sentences. CONCLUSION: The global aim of our project is to automate the process of creating and maintaining a problem list for hospitalized patients and thereby help to guarantee the timeliness, accuracy and completeness of this information

Hitomi (ASTRO-H) X-ray Astronomy Satellite

The Hitomi (ASTRO-H) mission is the sixth Japanese x-ray astronomy satellite developed by a large international collaboration, including Japan, USA, Canada, and Europe. The mission aimed to provide the highest energy resolution ever achieved at E  >  2  keV, using a microcalorimeter instrument, and to cover a wide energy range spanning four decades in energy from soft x-rays to gamma rays. After a successful launch on February 17, 2016, the spacecraft lost its function on March 26, 2016, but the commissioning phase for about a month provided valuable information on the onboard instruments and the spacecraft system, including astrophysical results obtained from first light observations. The paper describes the Hitomi (ASTRO-H) mission, its capabilities, the initial operation, and the instruments/spacecraft performances confirmed during the commissioning operations for about a month