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To investigate machine learning for linking image content, human perception, cognition, and error in the diagnostic interpretation of mammograms.

To provide a natural language processing method for the automatic recognition of events, temporal expressions, and temporal relations in clinical records.

Patient localization can improve workflow in outpatient settings, which might lead to lower costs. The existing wireless local area network (WLAN) architecture in many hospitals opens up the possibility of adopting real-time patient tracking systems for capturing and processing position data; once captured, these data can be linked with clinical patient data.

To examine the feasibility of using statistical text classification to automatically identify health information technology (HIT) incidents in the USA Food and Drug Administration (FDA) Manufacturer and User Facility Device Experience (MAUDE) database.

This study shows the evolution of a biomedical observation dictionary within the Assistance Publique Hôpitaux Paris (AP-HP), the largest European university hospital group. The different steps are detailed as follows: the dictionary creation, the mapping to logical observation identifier names and codes (LOINC), the integration into a multiterminological management platform and, finally, the implementation in the health information system.

To employ machine learning methods to predict the eventual therapeutic response of breast cancer patients after a single cycle of neoadjuvant chemotherapy (NAC).

Health social networking sites (HSNS), virtual communities where users connect with each other around common problems and share relevant health data, have been increasingly adopted by medical professionals and patients. The growing use of HSNS like Sermo and PatientsLikeMe has prompted public concerns about the risks that such online data-sharing platforms pose to the privacy and security of personal health data. This paper articulates a set of privacy risks introduced [...]

To create a computable MEDication Indication resource (MEDI) to support primary and secondary use of electronic medical records (EMRs).

To develop a comprehensive temporal information extraction system that can identify events, temporal expressions, and their temporal relations in clinical text. This project was part of the 2012 i2b2 clinical natural language processing (NLP) challenge on temporal information extraction.