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IBM(R) Watson for Oncology (WfO) is a clinical decision-support system (CDSS) that provides evidence-informed therapeutic options to cancer-treating clinicians. A panel of experienced oncologists compared CDSS treatment options to treatment decisions made by clinicians to characterize the quality of CDSS therapeutic options and decisions made in practice.

Accurate risk prediction is important for evaluating early medical treatment effects and improving health care quality. Existing methods are usually designed for dynamic medical data, which require long-term observations. Meanwhile, important personalized static information is ignored due to the underlying uncertainty and unquantifiable ambiguity. It is urgent to develop an early risk prediction method that can adaptively integrate both static and dynamic health data.

We aim to develop a hybrid model for earlier and more accurate predictions for the number of infected cases in pandemics by (1) using patients' claims data from different counties and states that capture local disease status and medical resource utilization; (2) utilizing demographic similarity and geographical proximity between locations; and (3) integrating pandemic transmission dynamics into a deep learning model.

Machine learning is used to understand and track influenza-related content on social media. Because these systems are used at scale, they have the potential to adversely impact the people they are built to help. In this study, we explore the biases of different machine learning methods for the specific task of detecting influenza-related content. We compare the performance of each model on tweets written in Standard American English (SAE) vs [...]

The study sought to learn if it were possible to develop an ontology that would allow the Food and Drug Administration approved indications to be expressed in a manner computable and comparable to what is expressed in an electronic health record.

Our aim was to develop an efficient search strategy for prognostic studies and clinical prediction guides (CPGs), optimally balancing sensitivity and precision while independent of MeSH terms, as relying on them may miss the most current literature.

Central line-associated bloodstream infections (CLABSIs) are a common, costly, and hazardous healthcare-associated infection in children. In children in whom continued access is critical, salvage of infected central venous catheters (CVCs) with antimicrobial lock therapy is an alternative to removal and replacement of the CVC. However, the success of CVC salvage is uncertain, and when it fails the catheter has to be removed and replaced. We describe a machine learning approach [...]

To evaluate the effect of electronic health record (EHR)-integrated digital health tools comprised of a checklist and video on transitions-of-care outcomes for patients preparing for discharge.

Re-identification risk methods for biomedical data often assume a worst case, in which attackers know all identifiable features (eg, age and race) about a subject. Yet, worst-case adversarial modeling can overestimate risk and induce heavy editing of shared data. The objective of this study is to introduce a framework for assessing the risk considering the attacker's resources and capabilities.

The development and implementation of clinical decision support (CDS) that trains itself and adapts its algorithms based on new data-here referred to as Adaptive CDS-present unique challenges and considerations. Although Adaptive CDS represents an expected progression from earlier work, the activities needed to appropriately manage and support the establishment and evolution of Adaptive CDS require new, coordinated initiatives and oversight that do not currently exist. In this AMIA position paper [...]