A Confidence-Based Approach for Balancing Fairness and Accuracy

We study three classical machine learning algorithms in the context of algorithmic fairness: adaptive boosting, support vector machines, and logistic regression. Our goal is to maintain the high accuracy of these learning algorithms while reducing the degree to which they discriminate against individuals because of their membership in a protected group. Our first contribution is a method for achieving fairness by shifting the decision boundary for the protected group. The method is based on the theory of margins for boosting. Our method performs comparably to or outperforms previous algorithms in the fairness literature in terms of accuracy and low discrimination, while simultaneously allowing for a fast and transparent quantification of the trade-off between bias and error. Our second contribution addresses the shortcomings of the bias-error trade-off studied in most of the algorithmic fairness literature. We demonstrate that even hopelessly naive modifications of a biased algorithm, which cannot be reasonably said to be fair, can still achieve low bias and high accuracy. To help to distinguish between these naive algorithms and more sensible algorithms we propose a new measure of fairness, called resilience to random bias (RRB). We demonstrate that RRB distinguishes well between our naive and sensible fairness algorithms. RRB together with bias and accuracy provides a more complete picture of the fairness of an algorithm

On Designing of a Low Leakage Patient-Centric Provider Network

When a patient in a provider network seeks services outside of their community, the community experiences a leakage. Leakage is undesirable as it typically leads to higher out-of-network cost for patient and increases barrier for care coordination, which is particularly problematic for Accountable Care Organization (ACO) as the in-network providers are financially responsible for patient quality and outcome. We aim to design a data-driven method to identify naturally occurring provider networks driven by diabetic patient choices, and understand the relationship among provider composition, patient composition, and service leakage pattern. We construct a healthcare provider network based on patients' historical medical insurance claims. A community detection algorithm is used to identify naturally occurring communities of collaborating providers. Finally, import-export analysis is conducted to benchmark their leakage pattern and identify further leakage reduction opportunity. The design yields six major provider communities with diverse profiles. Some communities are geographically concentrated, while others tend to draw patients with certain diabetic co-morbidities. Providers from the same healthcare institution are likely to be assigned to the same community. While most communities have high within-community utilization and spending, at 85% and 86% respectively, leakage still persists. Hence, we utilize a metric from import-export analysis to detect leakage, gaining insight on how to minimizing leakage. In conclusion, we identify patient-driven provider organization by surfacing providers who share a large number of patients. By analyzing the import-export behavior of each identified community using a novel approach and profiling community patient and provider composition we understand the key features of having a balanced number of PCP and specialists and provider heterogeneity

Connecting the Dots: A Road Map for Better Integration in Latin America and the Caribbean

What can be said of Latin America and the Caribbean's experiment with regional integration? Did it live up to the expectations? What does this experience say about the regional integration agenda moving forward? Do the tectonic changes undergone by the world economy in the last quarter of a century matter for policy design? This report offers answers to these pressing questions. It argues that while the "new regionalism" was in general effective to promote international trade, it failed to boost the region's competitiveness abroad. Fragmentation is seen as the original sin, and convergence the path to redemption. The policy recommendations offer different routes to convergence, from a cautious, cumulation of rules or origin approach to a non-stop sprint to a LAC-FTA. But they all come with a warning: in the current challenging trade environment, the benefits of caution might be too little, too late

Predicting Survival of NSCLC Patients Treated with Immune Checkpoint Inhibitors: Impact and Timing of Immune-related Adverse Events and Prior Tyrosine Kinase Inhibitor Therapy

Introduction: Immune checkpoint inhibitors (ICIs) produce a broad spectrum of immune-related adverse events (irAEs) affecting various organ systems. While ICIs are established as a therapeutic option in non-small cell lung cancer (NSCLC) treatment, most patients receiving ICI relapse. Additionally, the role of ICIs on survival in patients receiving prior targeted tyrosine kinase inhibitor (TKI) therapy has not been well-defined. Objective: To investigate the impact of irAEs, the relative time of occurrence, and prior TKI therapy to predict clinical outcomes in NSCLC patients treated with ICIs. Methods: A single center retrospective cohort study identified 354 adult patients with NSCLC receiving ICI therapy between 2014 and 2018. Survival analysis utilized overall survival (OS) and real-world progression free survival (rwPFS) outcomes. Model performance matrices for predicting 1-year OS and 6-month rwPFS using linear regression baseline, optimal, and machine learning modeling approaches. Results: Patients experiencing an irAE were found to have a significantly longer OS and rwPFS compared to patients who did not (median OS 25.1 vs. 11.1 months; hazard ratio [HR] 0.51, confidence interval [CI] 0.39- 0.68, P-value \u3c0.001, median rwPFS 5.7 months vs. 2.3; HR 0.52, CI 0.41- 0.66, P-value \u3c0.001, respectively). Patients who received TKI therapy before initiation of ICI experienced significantly shorter OS than patients without prior TKI therapy (median OS 7.6 months vs. 18.5 months; P-value \u3c 0.01). After adjusting for other variables, irAEs and prior TKI therapy significantly impacted OS and rwPFS. Lastly, the performances of models implementing logistic regression and machine learning approaches were comparable in predicting 1-year OS and 6-month rwPFS. Conclusion: The occurrence of irAEs, the timing of the events, and prior TKI therapy were significant predictors of survival in NSCLC patients on ICI therapy. Therefore, our study supports future prospective studies to investigate the impact of irAEs, and sequence of therapy on the survival of NSCLC patients taking ICIs

Structure-Based Optimization of a Novel Class of Aldehyde Dehydrogenase 1A (ALDH1A) Subfamily-Selective Inhibitors as Potential Adjuncts to Ovarian Cancer Chemotherapy

Aldehyde dehydrogenase (ALDH) activity is commonly used as a marker to identify cancer stem-like cells. The three ALDH1A isoforms have all been individually implicated in cancer stem-like cells and in chemoresistance; however, which isoform is preferentially expressed varies between cell lines. We sought to explore the structural determinants of ALDH1A isoform selectivity in a series of small-molecule inhibitors in support of research into the role of ALDH1A in cancer stem cells. An SAR campaign guided by a cocrystal structure of the HTS hit CM39 (7) with ALDH1A1 afforded first-in-class inhibitors of the ALDH1A subfamily with excellent selectivity over the homologous ALDH2 isoform. We also discovered the first reported modestly selective single isoform 1A2 and 1A3 inhibitors. Two compounds, 13g and 13h, depleted the CD133+ putative cancer stem cell pool, synergized with cisplatin, and achieved efficacious concentrations in vivo following IP administration. Compound 13h additionally synergized with cisplatin in a patient-derived ovarian cancer spheroid model