Individuals create and consume more diverse data about themselves today than any time in history. Sources of this data include wearable devices, images, social media, geo-spatial information and more. A tremendous opportunity rests within cross-modal data analysis that leverages existing domain knowledge methods to understand and guide human health. Especially in chronic diseases, current medical practice uses a combination of sparse hospital based biological metrics (blood tests, expensive imaging, etc.) to understand the evolving health status of an individual. Future health systems must integrate data created at the individual level to better understand health status perpetually, especially in a cybernetic framework. In this work we fuse multiple user created and open source data streams along with established biomedical domain knowledge to give two types of quantitative state estimates of cardiovascular health. First, we use wearable devices to calculate cardiorespiratory fitness (CRF), a known quantitative leading predictor of heart disease which is not routinely collected in clinical settings. Second, we estimate inherent genetic traits, living environmental risks, circadian rhythm, and biological metrics from a diverse dataset. Our experimental results on 24 subjects demonstrate how multi-modal data can provide personalized health insight. Understanding the dynamic nature of health status will pave the way for better health based recommendation engines, better clinical decision making and positive lifestyle changes.

Objectives To identify differences in letters of recommendation (LORs) of applicants to a single ophthalmology residency program by gender, race, academic performance, and match outcome. Design This was a retrospective analysis of LORS for 2,523 applicants (7,569 letters) to the University of California, Irvine ophthalmology residency program from 2011 to 2018. Methods Programming scripts were employed to determine the number of times 22 key words from four thematic categories (standout words, ability, grindstone, and compassion) appeared in LORs for each applicant. A chi-square test was performed to assess for possible differences in the presence of each key word by the following characteristics: gender, underrepresented minority (URM) status, Alpha Omega Alpha (AOA) membership, the United States Medical Licensing Exam (USMLE) Step 1 score, and match outcome. Linear regressions were created to determine the frequency at which words in each thematic category appeared according to the same baseline characteristics. Results In the LORs, females were more likely to be described as “empathetic” (p = 0.002), URMs were more likely to be described as “caring” (p = 0.002), high Step 1 scorers (≥240) were more likely to be described as “outstanding” (p = 0.002), and matched students were more likely to be described as “exceptional” (p = 0.001), “outstanding” (p < 0.001), and “superb” (p = 0.001). Standout words appeared more often in the LORs of AOA members, matched candidates, and high Step 1 scorers (p < 0.001 for all comparisons). “Competent” appeared more commonly in LORs for low Step 1 scorers (p < 0.001) and unmatched applicants (p = 0.001). Conclusion This study identifies differences in LORs by gender, URM status, and achievement including successful ophthalmology residency match. Females and URMs were more likely to be described as “empathetic” and “caring,” respectively; otherwise, we detected no gender or racial disparities in key word use in LORs. Candidates with high USMLE Step 1 scores or AOA membership had a higher frequency of standout words in their LORs. Whether they were truly more qualified in various dimensions or if they benefited from a halo effect bias warrants further investigation. There was a significant difference in the number of standout words in LORs between matched and unmatched applicants, suggesting that key word frequency may be a relevant metric for LOR appraisal.

Applying standard machine learning approaches for classification can produce unequal results across different demographic groups. When then used in real-world settings, these inequities can have negative societal impacts. This has motivated the development of various approaches to fair classification with machine learning models in recent years. In this paper, we consider the problem of modifying the predictions of a blackbox machine learning classifier in order to achieve fairness in a multiclass setting. To accomplish this, we extend the 'post-processing' approach in Hardt, Price, and Srebro (2016), which focuses on fairness for binary classification, to the setting of fair multiclass classification. We explore when our approach produces both fair and accurate predictions through systematic synthetic experiments and also evaluate discrimination-fairness tradeoffs on several publicly available real-world application datasets. We find that overall, our approach produces minor drops in accuracy and enforces fairness when the number of individuals in the dataset is high relative to the number of classes and protected groups.

High‐throughput single‐cell cytometry technologies have significantly improved our understanding of cellular phenotypes to support translational research and the clinical diagnosis of hematological and immunological diseases. However, subjective and ad hoc manual gating analysis does not adequately handle the increasing volume and heterogeneity of cytometry data for optimal diagnosis. Prior work has shown that machine learning can be applied to classify cytometry samples effectively. However, many of the machine learning classification results are either difficult to interpret without using characteristics of cell populations to make the classification, or suboptimal due to the use of inaccurate cell population characteristics derived from gating boundaries. To date, little has been done to optimize both the gating boundaries and the diagnostic accuracy simultaneously. In this work, we describe a fully discriminative machine learning approach that can simultaneously learn feature representations (e.g., combinations of coordinates of gating boundaries) and classifier parameters for optimizing clinical diagnosis from cytometry measurements. The approach starts from an initial gating position and then refines the position of the gating boundaries by gradient descent until a set of globally‐optimized gates across different samples are achieved. The learning procedure is constrained by regularization terms encoding domain knowledge that encourage the algorithm to seek interpretable results. We evaluate the proposed approach using both simulated and real data, producing classification results on par with those generated via human expertise, in terms of both the positions of the gating boundaries and the diagnostic accuracy. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

In data collection for predictive modeling, underrepresentation of certain groups, based on gender, race/ethnicity, or age, may yield less accurate predictions for these groups. Recently, this issue of fairness in predictions has attracted significant attention, as data-driven models are increasingly utilized to perform crucial decision-making tasks. Existing methods to achieve fairness in the machine learning literature typically build a single prediction model in a manner that encourages fair prediction performance for all groups. These approaches have two major limitations: (i) fairness is often achieved by compromising accuracy for some groups; (ii) the underlying relationship between dependent and independent variables may not be the same across groups. We propose a joint fairness model (JFM) approach for logistic regression models for binary outcomes that estimates group-specific classifiers using a joint modeling objective function that incorporates fairness criteria for prediction. We introduce an accelerated smoothing proximal gradient algorithm to solve the convex objective function, and present the key asymptotic properties of the JFM estimates. Through simulations, we demonstrate the efficacy of the JFM in achieving good prediction performance and across-group parity, in comparison with the single fairness model, group-separate model, and group-ignorant model, especially when the minority group's sample size is small. Finally, we demonstrate the utility of the JFM method in a real-world example to obtain fair risk predictions for underrepresented older patients diagnosed with coronavirus disease 2019 (COVID-19).