OBJECTIVE: To present clinicians at the point-of-care with real-world data on the effectiveness of various treatment options in a precision cohort of patients closely matched to the index patient. MATERIALS AND METHODS: We developed disease-specific, machine-learning, patient-similarity models for hypertension (HTN), type II diabetes mellitus (T2DM), and hyperlipidemia (HL) using data on approximately 2.5 million patients in a large medical group practice. For each identified decision point, an encounter during which the patient's condition was not controlled, we compared the actual outcome of the treatment decision administered to that of the best-achieved outcome for similar patients in similar clinical situations. RESULTS: For the majority of decision points (66.8%, 59.0%, and 83.5% for HTN, T2DM, and HL, respectively), there were alternative treatment options administered to patients in the precision cohort that resulted in a significantly increased proportion of patients under control than the treatment option chosen for the index patient. The expected percentage of patients whose condition would have been controlled if the best-practice treatment option had been chosen would have been better than the actual percentage by: 36% (65.1% vs 48.0%, HTN), 68% (37.7% vs 22.5%, T2DM), and 138% (75.3% vs 31.7%, HL). CONCLUSION: Clinical guidelines are primarily based on the results of randomized controlled trials, which apply to a homogeneous subject population. Providing the effectiveness of various treatment options used in a precision cohort of patients similar to the index patient can provide complementary information to tailor guideline recommendations for individual patients and potentially improve outcomes.

Many researchers across diverse disciplines aim to analyze the behavior of cohorts whose behaviors are recorded in large event databases. However, extracting cohorts from databases is a difficult yet important step, often overlooked in many analytical solutions. This is especially true when researchers wish to restrict their cohorts to exhibit a particular temporal pattern of interest. In order to fill this gap, we designed COQUITO, a visual interface that assists users defining cohorts with temporal constraints. COQUITO was designed to be comprehensible to domain experts with no preknowledge of database queries and also to encourage exploration. We then demonstrate the utility of COQUITO via two case studies, involving medical and social media researchers.

Temporal event sequence data is increasingly commonplace, with applications ranging from electronic medical records to financial transactions to social media activity. Previously developed techniques have focused on low-dimensional datasets (e.g., with less than 20 distinct event types). Real-world datasets are often far more complex. This paper describes DecisionFlow, a visual analysis technique designed to support the analysis of high-dimensional temporal event sequence data (e.g., thousands of event types). DecisionFlow combines a scalable and dynamic temporal event data structure with interactive multi-view visualizations and ad hoc statistical analytics. We provide a detailed review of our methods, and present the results from a 12-person user study. The study results demonstrate that DecisionFlow enables the quick and accurate completion of a range of sequence analysis tasks for datasets containing thousands of event types and millions of individual events.

Consumers are increasingly relying on Web-based social content, such as product reviews, prior to making to a purchase. Recent surveys in the retail industry confirm that social content is indeed the #1 aid in a buying decision. Currently, accessing or adding to this valuable Web-based social content repository is mostly limited to computers far removed from the site of the shopping experience itself. We present a mobile augmented reality application, which extends such social content from the computer monitor into the physical world through mobile phones, providing consumers with in situ information on products right when and where they need to make buying decisions.

Rapid advances in mobile computing devices and sensor technologies are enabling the capture of unprecedented volumes of data by individuals involved in field operations in a variety of applications. As capture becomes ever more rich and pervasive the biggest challenge is in developing information processing and representation tools that maximize the utility of the captured multi-sensory data. The right tools hold the promise of converting captured data into actionable intelligence resulting in improved memory, enhanced situational understanding, and more efficient execution of operations. These tools need to be at least as rich and diverse as the sensors used for capture, and need to be unified within an effective system architecture. This paper presents our initial attempt at such a system and architecture that combines several emerging sensor technologies, state of the art analytic engines, and multi-dimensional navigation tools, into an end-to-end electronic chronicling solution for mobile surveillance by humans.