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Background: For the provision of optimal care in a suicide prevention helpline, it is important to know what contributes to positive or negative effects on help seekers. Helplines can often be contacted through text-based chat services, which produce large amounts of text data for use in large-scale analysis. Objective: We trained a machine learning classification model to predict chat outcomes based on the content of the chat conversations in suicide helplines and identified the counsellor utterances that had the most impact on its outputs. Methods: From August 2021 until January 2023, help seekers (N=6903) scored themselves on factors known to be associated with suicidality (eg, hopelessness, feeling entrapped, will to live) before and after a chat conversation with the suicide prevention helpline in the Netherlands (113 Suicide Prevention). Machine learning text analysis was used to predict help seeker scores on these factors. Using 2 approaches for interpreting machine learning models, we identified text messages from helpers in a chat that contributed the most to the prediction of the model. Results: According to the machine learning model, helpers' positive affirmations and expressing involvement contributed to improved scores of the help seekers. Use of macros and ending the chat prematurely due to the help seeker being in an unsafe situation had negative effects on help seekers. Conclusions: This study reveals insights for improving helpline chats, emphasizing the value of an evocative style with questions, positive affirmations, and practical advice. It also underscores the potential of machine learning in helpline chat analysis. [ABSTRACT FROM AUTHOR]

Measuring and quantifying dependencies between random variables (RVs) can give critical insights into a dataset. Typical questions are: 'Do underlying relationships exist?', 'Are some variables redundant?', and 'Is some target variable Y highly or weakly dependent on variable X ?' Interestingly, despite the evident need for a general-purpose measure of dependency between RVs, common practice is that most data analysts use the Pearson correlation coefficient to quantify dependence between RVs, while it is recognized that the correlation coefficient is essentially a measure for linear dependency only. Although many attempts have been made to define more generic dependency measures, there is no consensus yet on a standard, general-purpose dependency function. In fact, several ideal properties of a dependency function have been proposed, but without much argumentation. Motivated by this, we discuss and revise the list of desired properties and propose a new dependency function that meets all these requirements. This general-purpose dependency function provides data analysts with a powerful means to quantify the level of dependence between variables. To this end, we also provide Python code to determine the dependency function for use in practice. [ABSTRACT FROM AUTHOR]

Before any binary classification model is taken into practice, it is important to validate its performance on a proper test set. Without a frame of reference given by a baseline method, it is impossible to determine if a score is "good" or "bad." The goal of this paper is to examine all baseline methods that are independent of feature values and determine which model is the "best" and why. By identifying which baseline models are optimal, a crucial selection decision in the evaluation process is simplified. We prove that the recently proposed Dutch Draw baseline is the best input‐independent classifier (independent of feature values) for all order‐invariant measures (independent of sequence order) assuming that the samples are randomly shuffled. This means that the Dutch Draw baseline is the optimal baseline under these intuitive requirements and should therefore be used in practice. [ABSTRACT FROM AUTHOR]

A novel mathematical model is introduced that allows solving real-life scheduling problems in complex multi-stage machine environment with (i) non-triangular sequence-dependent setup times and (ii) shifting production bottlenecks, both of which are important aspects appearing in varying manufacturing industries. The primary goal is to minimise the tardiness of customer orders, which may consist of multiple production orders each in turn composed of several batches. A secondary objective is to maximise the production capacity utilization as measured by the makespan. The model is elaborated for general animal-feed plants which have to deal with the particular production scheduling problem on a daily basis. Dispatching rules are introduced to enhance the optimization progress. Numerical experiments show that optimising the model leads to schedules that meet the due dates. Moreover, by reducing the mean idle time of production lines with 35.6%, the optimization leads to a makespan reduction of 6.5% on average compared to real-life applied schedules. [ABSTRACT FROM AUTHOR]