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Being able to predict a patient's life expectancy can help doctors and patients prioritize treatments and supportive care. For predicting life expectancy, physicians have been shown to outperform traditional models that use only a few predictor variables. It is possible that a machine learning model that uses many predictor variables and diverse data sources from the electronic medical record can improve on physicians' performance. For patients with metastatic cancer, we [...]

The substantial expansion of secure messaging (SM) via the patient portal in the last decade suggests that it is becoming a standard of care, but few have examined SM use longitudinally. We examined SM patterns among a diverse cohort of patients with diabetes (N = 19 921) and the providers they exchanged messages with within a large, integrated health system over 10 years (2006-2015), linking patient demographics to SM use. We found a [...]

Cause of death is used as an important outcome of clinical research; however, access to cause-of-death data is limited. This study aimed to develop and validate a machine-learning model that predicts the cause of death from the patient's last medical checkup.

Reticular pseudodrusen (RPD), a key feature of age-related macular degeneration (AMD), are poorly detected by human experts on standard color fundus photography (CFP) and typically require advanced imaging modalities such as fundus autofluorescence (FAF). The objective was to develop and evaluate the performance of a novel multimodal, multitask, multiattention (M3) deep learning framework on RPD detection.

The study sought to review the different assessment items that have been used within existing health app evaluation frameworks aimed at individual, clinician, or organizational users, and to analyze the scoring and evaluation methods used in these frameworks.

Precision medicine can revolutionize health care by tailoring treatments to individual patient needs. Advancing precision medicine requires evidence development through research that combines needed data, including clinical data, at an unprecedented scale. Widespread adoption of health information technology (IT) has made digital clinical data broadly available. These data and information systems must evolve to support precision medicine research and delivery. Specifically, relevant health IT data, infrastructure, clinical integration, and policy [...]

To develop a computer model to predict patients with nonalcoholic steatohepatitis (NASH) using machine learning (ML).

Modern health care requires patients, staff, and equipment to navigate complex environments to deliver quality care efficiently. Real-time locating systems (RTLS) are local tracking systems that identify the physical locations of personnel and equipment in real time. Applications and analytic strategies to utilize RTLS-produced data are still under development. The objectives of this systematic review were to describe and analyze the key features of RTLS applications and demonstrate their potential [...]

The Office of National Coordinator for Health Information Technology final rule implementing the interoperability and information blocking provisions of the 21st Century Cures Act requires support for two SMART (Substitutable Medical Applications, Reusable Technologies) application programming interfaces (APIs) and instantiates Health Level Seven International (HL7) Fast Healthcare Interoperability Resources (FHIR) as a lingua franca for health data. We sought to assess the current state and near-term plans for the SMART/HL7 [...]

The Food & Drug Administration (FDA) is considering the permanent exemption of premarket notification requirements for several Class I and II medical device products, including several artificial Intelligence (AI)-driven devices. The exemption is based on the need to rapidly more quickly disseminate devices to the public, estimated cost-savings, a lack of documented adverse events reported to the FDA's database. However, this ignores emerging issues related to AI-based devices, including utility [...]