This chapter addresses fundamental issues of change in psychotherapy: how to measure,
monitor, predict change, and provide feedback on treatment outcome. The chapter starts …

Background: The use of machine learning (ML) and artificial intelligence (AI) in medical
research continues to grow as the amount and availability of clinical data expands. These …

Background: Studies with heterogeneous samples in naturalistic treatment settings suggest
that movement synchrony (MS) between therapists and patients correlates with therapeutic …

Abstract Machine learning (ML) has demonstrated promise in predicting mortality; however,
understanding spatial variation in risk factor contributions to mortality rate requires …

Objective: Optimizing treatment selection may improve treatment outcomes in depression. A
promising approach is the Personalized Advantage Index (PAI), which predicts the optimal …

To improve understanding of Alzheimer's disease, large observational studies are needed to
increase power for more nuanced analyses. Combining data across existing observational …

Premature termination is a problem in psychotherapy. In addition to the examination of
demographic and clinical variables as predictors of dropout, research indicates the …

Missing data imputation forms the first critical step of many data analysis pipelines. The
challenge is greatest for mixed data sets, including real, Boolean, and ordinal data, where …

Statistical imputation techniques were evaluated for infilling missing records in daily rainfall
data within the Pra and Densu river basins in Ghana. The imputation techniques considered …

Missing data is an expected issue when large amounts of data is collected, and several
imputation techniques have been proposed to tackle this problem. Beneath classical …