Using Similarity Metrics on Real World Data and Patient Treatment Pathways to Recommend the Next Treatment

Non-small-cell lung cancer (NSCLC) is one of the most prevalent types of lung cancer and continues to have an ominous five year survival rate. Considerable work has been accomplished in analyzing the viability of the treatments offered to NSCLC patients; however, while many of these treatments have performed better over populations of diagnosed NSCLC patients, a specific treatment may not be the most effective therapy for a given patient. Coupling both patient similarity metrics using the Gower similarity metric and prior treatment knowledge, we were able to demonstrate how patient analytics can complement clinical efforts in recommending the next best treatment. Our retrospective and exploratory results indicate that a majority of patients are not recommended the best surviving therapy once they require a new therapy. This investigation lays the groundwork for treatment recommendation using analytics, but more investigation is required to analyze patient outcomes beyond survival

Putting Community First: A Promising Approach to Federal Collaboration for Environmental Improvement: An Evaluation of the Community Action for a Renewed Environment (CARE) Demonstration Program

This report is an independent evaluation of the Environmental Protection Agency's (EPA) Community Action for a Renewed Environment (CARE) Demonstration Program, a community-driven process that uses the best available data to help communities set priorities and take action on their greatest environmental risks. CARE fosters local partnerships that seek participation from business, government, organizations, residents and EPA staff. It also supports a public, transparent planning and implementation process based on collaborative decision-making and shared action.Key FindingsThe National Academy Panel overseeing this effort was impressed by the dedication of the EPA staff to this unique initiative and commended the EPA for its efforts to partner with communities in achieving important long-term and sustainable environmental improvements at the local level. Recommended actions for the CARE Program include: (1) develop and implement a multifaceted information sharing approach; (2) coordinate and refine internal program management activities; and (3) develop a strategic plan and a business plan for CARE

Recurrent Latent Variable Networks for Session-Based Recommendation

In this work, we attempt to ameliorate the impact of data sparsity in the context of session-based recommendation. Specifically, we seek to devise a machine learning mechanism capable of extracting subtle and complex underlying temporal dynamics in the observed session data, so as to inform the recommendation algorithm. To this end, we improve upon systems that utilize deep learning techniques with recurrently connected units; we do so by adopting concepts from the field of Bayesian statistics, namely variational inference. Our proposed approach consists in treating the network recurrent units as stochastic latent variables with a prior distribution imposed over them. On this basis, we proceed to infer corresponding posteriors; these can be used for prediction and recommendation generation, in a way that accounts for the uncertainty in the available sparse training data. To allow for our approach to easily scale to large real-world datasets, we perform inference under an approximate amortized variational inference (AVI) setup, whereby the learned posteriors are parameterized via (conventional) neural networks. We perform an extensive experimental evaluation of our approach using challenging benchmark datasets, and illustrate its superiority over existing state-of-the-art techniques

A Tripartite Framework for Leadership Evaluation

The Tripartite Framework for Leadership Evaluation provides a comprehensive examination of the leadership evaluation landscape and makes key recommendations about how the field of leadership evaluation should proceed. The chief concern addressed by this working paper is the use of student outcome data as a measurement of leadership effectiveness. A second concern in our work with urban leaders is the absence or surface treatment of race and equity in nearly all evaluation instruments or processes. Finally, we call for an overhaul of the conventional cycle of inquiry, which is based largely on needs analysis and leader deficits, and incomplete use of evidence to support recurring short cycles within the larger yearly cycle of inquiry

A conceptual analytics model for an outcome-driven quality management framework as part of professional healthcare education

BACKGROUND: Preparing the future health care professional workforce in a changing world is a significant undertaking. Educators and other decision makers look to evidence-based knowledge to improve quality of education. Analytics, the use of data to generate insights and support decisions, have been applied successfully across numerous application domains. Health care professional education is one area where great potential is yet to be realized. Previous research of Academic and Learning analytics has mainly focused on technical issues. The focus of this study relates to its practical implementation in the setting of health care education. OBJECTIVE: The aim of this study is to create a conceptual model for a deeper understanding of the synthesizing process, and transforming data into information to support educators’ decision making. METHODS: A deductive case study approach was applied to develop the conceptual model. RESULTS: The analytics loop works both in theory and in practice. The conceptual model encompasses the underlying data, the quality indicators, and decision support for educators. CONCLUSIONS: The model illustrates how a theory can be applied to a traditional data-driven analytics approach, and alongside the context- or need-driven analytics approach

Preparing for a Pay for Success Opportunity

Massachusetts, like many other states and municipalities across the US, is grappling with a tremendous challenge: With increasingly strained budgets and growing social need, how can government funding be directed towards evidence-based programs that offer demonstrated cost-savings? Pay for Success and Social Impact Bonds (PFS/SIB) have emerged as potential mechanisms for making smart investments in effective social interventions by changing the way Government allocates and invests its resources -- focusing on results and outcomes. In short, funding what works.In May 2011, Massachusetts issued a Request for Information (RFI) with the goal of helping the Commonwealth determine "the areas of government activity where success-based-contracting strategies have the potential to improve outcomes and/or reduce costs." The Justice System is one area of government activity with the potential to illustrate both improved outcomes and well-defined cost savings for the Commonwealth. In addition, Massachusetts has a variety of established service providers that offer preventative initiatives to populations involved with the Justice System, particularly young people. One of these successful and well-known providers, Roca, had worked with Third Sector Capital Partners (Third Sector) for a year and a half to prepare itself to be a successful candidate for this PFS/SIB opportunity