Towards more Accessible Precision Medicine: Building a more Transferable Machine Learning Model to Support Prognostic Decisions for Micro- and Macrovascular Complications of Type 2 Diabetes Mellitus.

Although machine learning models are increasingly being developed for clinical decision support for patients with type 2 diabetes, the adoption of these models into clinical practice remains limited. Currently, machine learning (ML) models are being constructed on local healthcare systems and are validated internally with no expectation that they would validate externally and thus, are rarely transferrable to a different healthcare system. In this work, we aim to demonstrate that (1) even a complex ML model built on a national cohort can be transferred to two local healthcare systems, (2) while a model constructed on a local healthcare system's cohort is difficult to transfer; (3) we examine the impact of training cohort size on the transferability; and (4) we discuss criteria for external validity.

An Insurer's Program To Incentivize Generic Oncology Drugs Did Not Alter Treatment Patterns Or Spending On Care.

The high and rising costs of anticancer drugs have received national attention. The prices of brand-name anticancer drugs often dwarf those of established generic drugs with similar efficacy. In 2007-16 UnitedHealthcare sought to encourage the use of several common low-cost generic anticancer drugs by offering providers a voluntary incentivized fee schedule with substantially higher generic drug payments (and profit margins), thereby increasing financial equivalence for providers in the choice between generic and brand-name drugs and regimens.

Multi-Task Learning to Identify Outcome-Specific Risk Factors that Distinguish Individual Micro and Macrovascular Complications of Type 2 Diabetes.

Because deterioration in overall metabolic health underlies multiple complications of Type 2 Diabetes Mellitus, a substantial overlap among risk factors for the complications exists, and this makes the outcomes difficult to distinguish. We hypothesized each risk factor had two roles: describing the extent of deteriorating overall metabolic health and signaling a particular complication the patient is progressing towards. We aimed to examine feasibility of our proposed methodology that separates these two roles, thereby, improving interpretation of predictions and helping prioritize which complication to target first.

Discovering Public Health Nurse-Specific Family Home Visiting Intervention Patterns Using Visualization Techniques.

Visualization is a Big Data method for detecting and validating previously unknown and hidden patterns within large data sets. This study used visualization techniques to discover and test novel patterns in public health nurse (PHN)-client-risk-intervention-outcome relationships. To understand the mechanism underlying risk reduction among high risk mothers, data representing complex social interventions were visualized in a series of three steps, and analyzed with other important contextual factors using standard descriptive and inferential statistics.

Type 2 Diabetes Mellitus Trajectories and Associated Risks.

Disease progression models, statistical models that assess a patient's risk of diabetes progression, are popular tools in clinical practice for prevention and management of chronic conditions. Most, if not all, models currently in use are based on gold standard clinical trial data. The relatively small sample size available from clinical trial limits these models only considering the patient's state at the time of the assessment and ignoring the trajectory, the sequence of events, that led up to the state.

Public health nurse perceptions of Omaha System data visualization.

Background: Electronic health records (EHRs) provide many benefits related to the storage, deployment, and retrieval of large amounts of patient data. However, EHRs have not fully met the need to reuse data for decision making on follow-up care plans. Visualization offers new ways to present health data, especially in EHRs.

Divisive Hierarchical Clustering towards Identifying Clinically Significant Pre-Diabetes Subpopulations.

Type 2 Diabetes Mellitus is a progressive disease with increased risk of developing serious complications. Identifying subpopulations and their relevant risk factors can contribute to the prevention and effective management of diabetes. We use a novel divisive hierarchical clustering technique to identify clinically interesting subpopulations in a large cohort of Olmsted County, MN residents.