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1. Exploration of physiological sensors, features, and machine learning models for pain intensity estimation

   https://doi.org/10.1371/journal.pone.0254108  

   Pouromran, Fatemeh; Radhakrishnan, Srinivasan; Kamarthi, Sagar (July 2021, PLOS ONE)

   Le, Khanh N.Q. (Ed.)

   In current clinical settings, typically pain is measured by a patient’s self-reported information. This subjective pain assessment results in suboptimal treatment plans, over-prescription of opioids, and drug-seeking behavior among patients. In the present study, we explored automatic objective pain intensity estimation machine learning models using inputs from physiological sensors. This study uses BioVid Heat Pain Dataset. We extracted features from Electrodermal Activity (EDA), Electrocardiogram (ECG), Electromyogram (EMG) signals collected from study participants subjected to heat pain. We built different machine learning models, including Linear Regression, Support Vector Regression (SVR), Neural Networks and Extreme Gradient Boosting for continuous value pain intensity estimation. Then we identified the physiological sensor, feature set and machine learning model that give the best predictive performance. We found that EDA is the most information-rich sensor for continuous pain intensity prediction. A set of only 3 features from EDA signals using SVR model gave an average performance of 0.93 mean absolute error (MAE) and 1.16 root means square error (RMSE) for the subject-independent model and of 0.92 MAE and 1.13 RMSE for subject-dependent. The MAE achieved with signal-feature-model combination is less than 1 unit on 0 to 4 continues pain scale, which is smaller than the MAE achieved by the methods reported in the literature. These results demonstrate that it is possible to estimate pain intensity of a patient using a computationally inexpensive machine learning model with 3 statistical features from EDA signal which can be collected from a wrist biosensor. This method paves a way to developing a wearable pain measurement device. 

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2. Distress and self-care among chaplains working in palliative care

   https://doi.org/10.1017/S1478951518001062  

   White, Kelsey B.; Murphy, Patricia E.; Jeuland, Jane; Fitchett, George (October 2019, Palliative and Supportive Care)

   Abstract Background The prevalence of burnout and distress among palliative care professionals has received much attention since research suggests it negatively impacts the quality of care. Although limited, research suggests low levels of burnout or distress among healthcare chaplains; however, there has been no research among chaplains working in specific clinical contexts, including palliative care. Objective This study explored the distress, self-care, and debriefing practices of chaplains working in palliative care. Method Exploratory, cross-sectional survey of professional chaplains. Electronic surveys were sent to members of four professional chaplaincy organizations between February and April 2015. Primary measures of interest included Professional Distress, Distress from Theodicy, Informal Self-care, Formal Self-care, and debriefing practices. Result More than 60% of chaplains working in palliative care reported feeling worn out in the past 3 months because of their work as a helper; at least 33% practice Informal Self-care weekly. Bivariate analysis suggested significant associations between Informal Self-care and both Professional Distress and Distress from Theodicy. Multivariate analysis also identified that distress decreased as Informal and Formal Self-care increased. Significance of results Chaplains working in palliative care appear moderately distressed, possibly more so than chaplains working in other clinical areas. These chaplains also use debriefing, with non-chaplain palliative colleagues, to process clinical experiences. Further research is needed about the role of religious or spiritual beliefs and practices in protecting against stress associated with care for people at the end of life. 

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3. Assessing predictability of environmental time series with statistical and machine learning models

   https://doi.org/10.1002/env.2864  

   Bonas, Matthew; Datta, Abhirup; Wikle, Christopher_K; Boone, Edward_L; Alamri, Faten_S; Hari, Bhava_Vyasa; Kavila, Indulekha; Simmons, Susan_J; Jarvis, Shannon_M; Burr, Wesley_S; et al (July 2024, Environmetrics)

   Abstract The ever increasing popularity of machine learning methods in virtually all areas of science, engineering and beyond is poised to put established statistical modeling approaches into question. Environmental statistics is no exception, as popular constructs such as neural networks and decision trees are now routinely used to provide forecasts of physical processes ranging from air pollution to meteorology. This presents both challenges and opportunities to the statistical community, which could contribute to the machine learning literature with a model‐based approach with formal uncertainty quantification. Should, however, classical statistical methodologies be discarded altogether in environmental statistics, and should our contribution be focused on formalizing machine learning constructs? This work aims at providing some answers to this thought‐provoking question with two time series case studies where selected models from both the statistical and machine learning literature are compared in terms of forecasting skills, uncertainty quantification and computational time. Relative merits of both class of approaches are discussed, and broad open questions are formulated as a baseline for a discussion on the topic. 

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4. A New Perspective on Stress Detection: An Automated Approach for Detecting Eustress and Distress

   https://doi.org/10.1109/TAFFC.2023.3324910  

   Awada, Mohamad; Becerik-Gerber, Burcin; Lucas, Gale; Roll, Shawn; Liu, Ruying (October 2023, IEEE Transactions on Affective Computing)

   Previous studies have solely focused on establishing Machine Learning (ML) models for automated detection of stress arousal. However, these studies do not recognize stress appraisal and presume stress is a negative mental state. Yet, stress can be classified according to its influence on individuals; the way people perceive a stressor determines whether the stress reaction is considered as eustress (positive stress) or distress (negative stress). Thus, this study aims to assess the potential of using an ML approach to determine stress appraisal and identify eustress and distress instances using physiological and behavioral features. The results indicate that distress leads to higher perceived stress arousal compared to eustress. An XGBoost model that combined physiological and behavioral features using a 30 second time window had 83.38% and 78.79% F 1 -scores for predicting eustress and distress, respectively. Gender-based models resulted in an average increase of 2-4% in eustress and distress prediction accuracy. Finally, a model to predict the simultaneous assessment of eustress and distress, distinguishing between pure eustress, pure distress, eustress-distress coexistence, and the absence of stress achieved a moderate F 1 -score of 65.12%. The results of this study lay the foundation for work management interventions to maximize eustress and minimize distress in the workplace. 

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5. Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation

   Ruibo Tu, Kun Zhang (December 2019, Advances in neural information processing systems)

   Discovery of causal relations from observational data is essential for many disciplines of science and real-world applications. However, unlike other machine learning algorithms, whose development has been greatly fostered by a large amount of available benchmark datasets, causal discovery algorithms are notoriously difficult to be systematically evaluated because few datasets with known ground-truth causal relations are available. In this work, we handle the problem of evaluating causal discovery algorithms by building a flexible simulator in the medical setting. We develop a neuropathic pain diagnosis simulator, inspired by the fact that the biological processes of neuropathic pathophysiology are well studied with well-understood causal influences. Our simulator exploits the causal graph of theneuropathic pain pathology and its parameters in the generator are estimated from real-life patient cases. We show that the data generated from our simulator have similar statistics as real-world data. As a clear advantage, the simulator can produce infinite samples without jeopardizing the privacy of real-world patients. Our simulator provides a natural tool for evaluating various types of causal discovery algorithms, including those to deal with practical issues in causal discovery, such as unknown confounders, selection bias, and missing data. Using our simulator,we have evaluated extensively causal discovery algorithms under various settings. 

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