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To quantify and compare the time doctors and nurses spent on direct patient care, medication-related tasks, and interactions before and after electronic medication management system (eMMS) introduction.

Patient portals are becoming increasingly common, but the safety of patient messages and eVisits has not been well studied. Unlike patient-to-nurse telephonic communication, patient messages and eVisits involve an asynchronous process that could be hazardous if patients were using it for time-sensitive symptoms such as chest pain or dyspnea.

Drugs have tremendous potential to cure and relieve disease, but the risk of unintended effects is always present. Healthcare providers increasingly record data in electronic patient records (EPRs), in which we aim to identify possible adverse events (AEs) and, specifically, possible adverse drug events (ADEs).

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.

Applying the science of networks to quantify the discriminatory impact of the ICD-9-CM to ICD-10-CM transition between clinical specialties.

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

To explore the feasibility of a novel approach using an augmented one-class learning algorithm to model in-laboratory complications of percutaneous coronary intervention (PCI).

Biomarkers for subtyping triple negative breast cancer (TNBC) are needed given the absence of responsive therapy and relatively poor prediction of survival. Morphology of cancer tissues is widely used in clinical practice for stratifying cancer patients, while genomic data are highly effective to classify cancer patients into subgroups. Thus integration of both morphological and genomic data is a promising approach in discovering new biomarkers for cancer outcome prediction. Here we [...]