Longitudinal machine learning uncouples healthy aging factors from chronic disease risks.

To understand human longevity, inherent aging processes must be distinguished from known etiologies leading to age-related chronic diseases. Such deconvolution is difficult to achieve because it requires tracking patients throughout their entire lives. Here, we used machine learning to infer health trajectories over the entire adulthood age range using extrapolation from electronic medical records with partial longitudinal coverage.

Personalized lab test models to quantify disease potentials in healthy individuals.

Standardized lab tests are central for patient evaluation, differential diagnosis and treatment. Interpretation of these data is nevertheless lacking quantitative and personalized metrics. Here we report on the modeling of 2.

Hospital Revisits Within 30 Days After Conventional and Robotically Assisted Hysterectomy.

Objectives: To compare the rates of hospital readmissions, emergency department, and outpatient clinic visits after discharge for robotically assisted (RA) versus nonrobotic hysterectomy in women age 30 or more with nonmalignant conditions.

Data Sources: Discharges for 2011 for 8 states (CA, FL, GA, IA, MO, NE, NY, TN) (>86,000 inpatient hysterectomies) were drawn from the statewide databases of the Healthcare Cost and Utilization Project. Data from 4 of these states were used to study revisits after 29,000 outpatient hysterectomies.

Factors associated with adoption of robotic surgical technology in US hospitals and relationship to radical prostatectomy procedure volume.

Objective: Robotic technology has diffused rapidly despite high costs and limited additive reimbursement by major payers. We aimed to identify the factors associated with hospitals' decisions to adopt robotic technology and the consequences of these decisions.

Methods: This observational study used data on hospitals and market areas from 2005 to 2009.