Background There is an increasing interest in clinical prediction tools that can achieve high prediction accuracy and provide explanations of the factors leading to increased risk of adverse outcomes. However, approaches to explaining complex machine learning (ML) models are rarely informed by end-user needs and user evaluations of model interpretability are lacking in the healthcare domain. We used extended revisions of previously-published theoretical frameworks to propose a framework for the design of user-centered displays of explanations. This new framework served as the basis for qualitative inquiries and design review sessions with critical care nurses and physicians that informed the design of a user-centered explanation display for an ML-based prediction tool. Methods We used our framework to propose explanation displays for predictions from a pediatric intensive care unit (PICU) in-hospital mortality risk model. Proposed displays were based on a model-agnostic, instance-level explanation approach based on feature influence, as determined by Shapley values. Focus group sessions solicited critical care provider feedback on the proposed displays, which were then revised accordingly. Results The proposed displays were perceived as useful tools in assessing model predictions. However, specific explanation goals and information needs varied by clinical role and level of predictive modeling knowledge. Providers preferred explanation displays that required less information processing effort and could support the information needs of a variety of users. Providing supporting information to assist in interpretation was seen as critical for fostering provider understanding and acceptance of the predictions and explanations. The user-centered explanation display for the PICU in-hospital mortality risk model incorporated elements from the initial displays along with enhancements suggested by providers. Conclusions We proposed a framework for the design of user-centered displays of explanations for ML models. We used the proposed framework to motivate the design of a user-centered display of an explanation for predictions from a PICU in-hospital mortality risk model. Positive feedback from focus group participants provides preliminary support for the use of model-agnostic, instance-level explanations of feature influence as an approach to understand ML model predictions in healthcare and advances the discussion on how to effectively communicate ML model information to healthcare providers.

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Esophageal cancer is the seventh most common type of cancer in the world, the sixth leading cause of cancer-related death and its incidence is expected to rise 140% in the world in a period of 10 years until 2025. The overall incidence is higher in males, while data about prognosis and survival are not well established yet. The goal of this study was to carry out a comprehensive analysis of differences between sexes and other covariates in patients diagnosed with primary esophageal cancer. Data from 2005 to 2020 were obtained from the University Hospitals (UH) Seidman Cancer Center and from 2005 to 2018 from SEER. Patients were categorized according to histological subtype and divided according to sex. Pearson Chi-square test was used to compare variables of interest by sex and the influence of sex on survival was assessed by Kaplan Meier, log rank tests and Cox proportional hazards regression models. A total of 1205 patients were used for analysis. Sex differences in all types were found for age at diagnosis, histology, smoking status and prescriptions of NSAIDs and in SCC for age at diagnosis and alcoholism. Survival analysis didn’t showed differences between males and females on univariable and multivariable models. Males have a higher incidence of Esophageal Cancer and its two main subtypes but none of the comprehensive set of variables analyzed showed to be strongly or unique correlated with this sex difference in incidence nor are they associated with a sex difference in survival.