Posts classified under: Knowledge Discovery

Young girl using inhaler

Advancing mHealth Informatics

The Los Angeles (LA) PRISMS Center aims to be the leader in the development and application of mobile health (mHealth) technologies that deepen our scientific understanding and clinical management of diseases. Bringing together leading experts from UCLA and the University of Southern California (USC) in biomedical informatics, computer science, wireless health, environmental health science, and pediatrics, this Center supports innovative end-to-end software infrastructure for sensor-based health monitoring.

Focusing on pediatric asthma, our Center’s vision and research is motivated by the following question: what if you could predict ahead of time, for a specific asthma patient, the potential for exacerbation and thus mitigate – if not prevent – the event? Any system with this ability must integrate the growing array of available physiologic and environmental data from sensors, and place such data into context to elucidate the patient’s state and specific situation. The system must be able to act sufficiently quickly on sensed data to make timely recommendations, and end user compliance with system usage must be high to effect change. Our solution, the <strong>Biomedical REAl-Time Health Evaluation for Pediatric Asthma (BREATHE)</strong> platform, provides an extensible framework for the deployment of data collection protocols; secure data collection from sensors to a mobile device; integration of additional contextual information; and real-time analysis. Importantly, usability is a central consideration in the design of BREATHE, reflected in an iterative design/evaluate/refine process. To build and assess BREATHE, the Center comprises three closely coordinated efforts: Project 1 – Integrated Sensing from the Device to the Cloud, which establishes APIs for automatically gathering information from a device and local sensors, communicating with commercial and PRISMS U01 sensors and U24 coordinating data center; Project 2 – Integrating &amp; Visualizing Clinical, Environmental, and Sensor Data, which focuses on combining data acquired from the U24 data center with contextual information (e.g., regional air quality, clinical elements from the patient’s electronic health record, etc.) with real-time processing and analysis infrastructure; and Project 3 – Real-time Asthma and Air Pollution Project (Asthma APP), which develops a framework for evaluating system performance and real-world field testing of the platform for self-management and early interventions. Collectively, these Projects’ efforts realize BREATHE, changing how we interact with pediatric asthma patients and their caregivers to actuate a better understanding of the disease and improve adherence, and to achieve more personalized medicine through more detailed, objective measurements of an individual’s daily activities and surroundings.

science formula and math equation abstract background. concept of machine learning and artificial

Making Biomedical ML Reproducible

The confluence of machine learning (ML) data-driven approaches and increased computational power, alongside access to the wealth of electronic health records (EHRs) and other emergent types of data (e.g., omics, imaging, mHealth), are accelerating the development of biomedical predictive models. Such models range from traditional statistical approaches (e.g., regression) through to more advanced deep learning techniques (e.g., convolutional neural networks, CNNs), and span different tasks (e.g., biomarker/pathway discovery, diagnostic, prognostic, etc.). Two issues have become evident: 1) as there are no comprehensive standards to support the dissemination of these models, scientific reproducibility (vs. replicability) is problematic, given challenges in interpretation and implementation; and 2) as new models are put forth, methods to assess differences in performance, as well as insights into external validity (i.e., transportability), are necessary. Tools moving beyond data sharing and model “executables” are needed, capturing the information needed to fully reproduce a model and its evaluation. The objective of this R01 is the development of <strong>PREMIERE (PREdictive Model Index and Exchange REpository)</strong>, an informatics standard supporting the requisite information for scientific reproducibility for statistical and ML-based biomedical predictive models.

Human kidney cross section

Understanding Chronic Kidney Disease

Significant health disparities exist in chronic kidney disease (CKD), CKD progression, and end stage renal disease (ESRD) in ethnically diverse populations. African Americans (AAs) have approximately 25% higher prevalence of CKD, and 3x higher rates of ESRD compared to their counterparts. Moreover, a portion of these individuals exhibit rapid decline, progressing much faster than other comparable patients. Using a unique repository build from the electronic health records of &gt;10 million individuals seen at UCLA and Providence Health System, we are building new predictive models to better understand CKD progression and issues related to AAs. Using machine learning (ML)-based methods, we are developing CKD and eGFR trajectory models over time, stratified across age, gender, and different racial/ethnic groups. These models will provide insight into the factors that influence CKD, resulting in worsening outcomes. To ensure that these models translate into effective interventions, we are also conducting focus groups with primary care physicians to elicit their perspectives on existing and designed models to reduce CKD health disparities.