Using machine learning methods to determine a typology of patients with HIV-HCV infection to be treated with antivirals

Several European countries have established criteria for prioritising initiation of treatment in patients infected with the hepatitis C virus (HCV) by grouping patients according to clinical characteristics. Based on neural network techniques, our objective was to identify those factors for HIV/HCV co-infected patients (to which clinicians have given careful consideration before treatment uptake) that have not being included among the prioritisation criteria. This study was based on the Spanish HERACLES cohort (NCT02511496) (April-September 2015, 2940 patients) and involved application of different neural network models with different basis functions (product-unit, sigmoid unit and radial basis function neural networks) for automatic classification of patients for treatment. An evolutionary algorithm was used to determine the architecture and estimate the coefficients of the model. This machine learning methodology found that radial basis neural networks provided a very simple model in terms of the number of patient characteristics to be considered by the classifier (in this case, six), returning a good overall classification accuracy of 0.767 and a minimum sensitivity (for the classification of the minority class, untreated patients) of 0.550. Finally, the area under the ROC curve was 0.802, which proved to be exceptional. The parsimony of the model makes it especially attractive, using just eight connections. The independent variable "recent PWID" is compulsory due to its importance. The simplicity of the model means that it is possible to analyse the relationship between patient characteristics and the probability of belonging to the treated group

Predictive modeling of housing instability and homelessness in the Veterans Health Administration

OBJECTIVE: To develop and test predictive models of housing instability and homelessness based on responses to a brief screening instrument administered throughout the Veterans Health Administration (VHA). DATA SOURCES/STUDY SETTING: Electronic medical record data from 5.8 million Veterans who responded to the VHA's Homelessness Screening Clinical Reminder (HSCR) between October 2012 and September 2015. STUDY DESIGN: We randomly selected 80% of Veterans in our sample to develop predictive models. We evaluated the performance of both logistic regression and random forests—a machine learning algorithm—using the remaining 20% of cases. DATA COLLECTION/EXTRACTION METHODS: Data were extracted from two sources: VHA's Corporate Data Warehouse and National Homeless Registry. PRINCIPAL FINDINGS: Performance for all models was acceptable or better. Random forests models were more sensitive in predicting housing instability and homelessness than logistic regression, but less specific in predicting housing instability. Rates of positive screens for both outcomes were highest among Veterans in the top strata of model‐predicted risk. CONCLUSIONS: Predictive models based on medical record data can identify Veterans likely to report housing instability and homelessness, making the HSCR screening process more efficient and informing new engagement strategies. Our findings have implications for similar instruments in other health care systems.U.S. Department of Veterans Affairs (VA) Health Services Research and Development (HSR&D), Grant/Award Number: IIR 13-334 (IIR 13-334 - U.S. Department of Veterans Affairs (VA) Health Services Research and Development (HSRD))Accepted manuscrip

Breaking the Barriers to Specialty Care: Practical Ideas to Improve Health Equity and Reduce Cost - Helping Patients Engage in Specialty Care

Tremendous health outcome inequities remain in the U.S. across race and ethnicity, gender and sexual orientation, socio-economic status, and geography—particularly for those with serious conditions such as lung or skin cancer, HIV/AIDS, or cardiovascular disease.These inequities are driven by a complex set of factors—including distance to a specialist, insurance coverage, provider bias, and a patient's housing and healthy food access. These inequities not only harm patients, resulting in avoidable illness and death, they also drive unnecessary health systems costs.This 5-part series highlights the urgent need to address these issues, providing resources such as case studies, data, and recommendations to help the health care sector make meaningful strides toward achieving equity in specialty care.Top TakeawaysThere are vast inequalities in access to and outcomes from specialty health care in the U.S. These inequalities are worst for minority patients, low-income patients, patients with limited English language proficiency, and patients in rural areas.A number of solutions have emerged to improve health outcomes for minority and medically underserved patients. These solutions fall into three main categories: increasing specialty care availability, ensuring high-quality care, and helping patients engage in care.As these inequities are also significant drivers of health costs, payers, health care provider organizations, and policy makers have a strong incentive to invest in solutions that will both improve outcomes and reduce unnecessary costs. These actors play a critical role in ensuring that equity is embedded into core care delivery at scale.Part 4: "Helping Patients Engage in Specialty Care"Specialty care actors are increasingly addressing the social determinants of health with community outreach, patient navigation, and patient support services

Privacy-preserving model learning on a blockchain network-of-networks.

ObjectiveTo facilitate clinical/genomic/biomedical research, constructing generalizable predictive models using cross-institutional methods while protecting privacy is imperative. However, state-of-the-art methods assume a "flattened" topology, while real-world research networks may consist of "network-of-networks" which can imply practical issues including training on small data for rare diseases/conditions, prioritizing locally trained models, and maintaining models for each level of the hierarchy. In this study, we focus on developing a hierarchical approach to inherit the benefits of the privacy-preserving methods, retain the advantages of adopting blockchain, and address practical concerns on a research network-of-networks.Materials and methodsWe propose a framework to combine level-wise model learning, blockchain-based model dissemination, and a novel hierarchical consensus algorithm for model ensemble. We developed an example implementation HierarchicalChain (hierarchical privacy-preserving modeling on blockchain), evaluated it on 3 healthcare/genomic datasets, as well as compared its predictive correctness, learning iteration, and execution time with a state-of-the-art method designed for flattened network topology.ResultsHierarchicalChain improves the predictive correctness for small training datasets and provides comparable correctness results with the competing method with higher learning iteration and similar per-iteration execution time, inherits the benefits of the privacy-preserving learning and advantages of blockchain technology, and immutable records models for each level.DiscussionHierarchicalChain is independent of the core privacy-preserving learning method, as well as of the underlying blockchain platform. Further studies are warranted for various types of network topology, complex data, and privacy concerns.ConclusionWe demonstrated the potential of utilizing the information from the hierarchical network-of-networks topology to improve prediction

The place of volunteering in palliative care

This chapter discusses the place and development of volunteering in palliative care in the context of hospice service provision in the UK. It draws on recent qualitative research undertaken in a large hospice in England. The research explored a range of issues connected to the process and experience of voluntary work in this setting including who volunteers, what roles volunteers take up, how they are trained and supported and the ways in which role boundaries are established and maintained. The research revealed that hospice volunteering is rewarding but often emotionally challenging and is now highly routinised and closely monitored in ways paralleling practices in the paid labour market. Although volunteers freely give their time to the work of hospice, their activities are subject to significant management prescription, with hospices increasingly adopting sophisticated business models to underpin their operation and, in many cases, their expansion (Watts, 2010)

Is the Health Care System Working for Adolescents? Perspectives From Providers in Boston, Denver, Houston, and San Francisco

Assesses healthcare system services for adolescents in four urban areas. Includes provider perspectives on how health insurance, managed care, and other factors facilitate or impede access. Discusses innovative programs, and offers recommendations