Interpretable machine learning for psychological research: Opportunities and pitfalls

In recent years, machine learning methods have become increasingly popular prediction methods in psychology. At the same time, psychological researchers are typically not only interested in making predictions about the dependent variable, but also in learning which predictor variables are relevant, how they influence the dependent variable, and which predictors interact with each other. However, most machine learning methods are not directly interpretable. Interpretation techniques that support researchers in describing how the machine learning technique came to its prediction may be a means to this end. We present a variety of interpretation techniques and illustrate the opportunities they provide for interpreting the results of two widely used black box machine learning methods that serve as our examples: random forests and neural networks. At the same time, we illustrate potential pitfalls and risks of misinterpretation that may occur in certain data settings. We show in which way correlated predictors impact interpretations with regard to the relevance or shape of predictor effects and in which situations interaction effects may or may not be detected. We use simulated didactic examples throughout the article, as well as an empirical data set for illustrating an approach to objectify the interpretation of visualizations. We conclude that, when critically reflected, interpretable machine learning techniques may provide useful tools when describing complex psychological relationships

A study of unplanned 30-day hospital readmissions in the United States : early prediction and potentially modifiable risk factor identification

Unplanned hospital readmissions greatly impair patients' quality of life and have imposed a significant economic burden on American society. The pressure to reduce costs and improve healthcare quality has triggered the development of readmission reduction interventions. However, existing solutions focus on complementing inpatient care with enhanced care transition and post-discharge interventions, which are initiated near or after discharge when clinicians' impact on inpatient care is ending. Preventive intervention during hospitalization is an under-explored area, which holds the potential for reducing readmission risk. Nevertheless, it is challenging for clinicians to predict readmission risk at the early stage of inpatient care because little data is available. Existing readmission predictive models tend to incorporate variables whose values are only available near or after discharge. As a result, these models cannot be used for the early prediction of readmission. Another challenge is that there is no universal solution to reduce readmissions during hospitalization. Patients can be readmitted for any reason, and their heterogeneous social and clinical factors can further complicate the planning of interventions. The objective of this project was to improve the timeliness of readmission preventive intervention through a data-driven approach. A systematic review of the literature was performed to collect reported risk factors for unplanned 30-day hospital readmission. Using various predictive modeling and exploratory analysis methods, we have developed an early prediction model of readmission and have identified potentially modifiable readmission risk factors, which may be used to guide the development of readmission preventive interventions during hospitalization for different patients

The Historic Urban Landscape approach to urban management: a systematic review

In 2011, UNESCO adopted the Historic Urban Landscape (HUL) recommendation and called for the application of a landscape approach to ensure the integration of cultural heritage policies and management concerns in the wider goals of sustainable urban development. This paper tracks the genesis of a landscape approach to heritage conservation, and then presents a systematic review of the literature on the HUL. More than 100 publications from 2010 to early 2018 were analysed. The applied methodology combined an inductive categorization method with a deductive data mining method. The objective is to determine whether the academic discussion is addressing the different dimensions of the HUL approach, including the holistic, integrated, and value-based dimensions, and whether it is progressing through time to move from a conceptual to an operational level. Results show that while the discussion is heavily focused on values, the operationalization of a value-based approach is still lacking, as it is not fully contextualized in relation to local heritage discourses and the dynamics of heritage governance. Results also show that many case studies applications are in “non-Western” cities, thus opening the debate about the accountability of a value-based approach in contexts that tend to be dominated by groups with the most political power, and where conservation practices mainly focus on the mobilization of material heritage to foster its economic value. Nevertheless, the transition from international guidelines to contextualized local endeavours and policies remains a challenge to be solved.Evaluating the role of Historic Urban Landscapes in urban regeneration projects: An integrated approach based on the use of social media data