Background and Objectives: Acute respiratory distress syndrome (ARDS) carries high mortality, with cardiovascular complications frequently contributing to adverse outcomes. This study investigated the relationship between cardiac auscultation using electronic stethoscopy and echocardiographic findings and evaluated their prognostic significance in mechanically ventilated ARDS patients. Materials and Methods: This prospective observational study enrolled 173 consecutive adults with ARDS requiring mechanical ventilation (June 2020-June 2021). Cardiac auscultation was performed using an electronic stethoscope at four standard valvular positions. Bedside echocardiography assessed ventricular function, valvular regurgitation, right ventricular systolic pressure (RVSP), and inferior vena cava dimensions. Primary outcomes were ICU and 90-day mortality; the secondary outcome was ICU length of stay. Results: ICU mortality was 42.2% and 90-day mortality 46.8%. Auscultation findings correlated significantly with echocardiographic parameters: aortic stenosis murmur with an elevated aortic valve velocity (p = 0.009), and mitral/tricuspid regurgitation murmurs with corresponding color Doppler findings (p < 0.001). In multivariate analysis, the mean daily SOFA score (OR 2.39, 95% CI 1.57-3.64, p < 0.001) and RVSP (OR 1.07, 95% CI 1.02-1.11, p = 0.006) independently predicted ICU mortality. For 90-day mortality, the APACHE II score (OR 1.25, p = 0.006), mean daily SOFA score (OR 1.54, p = 0.039), RVSP (OR 1.07, p = 0.020), and mitral regurgitation severity (OR 2.98, p = 0.031) were independent predictors. ICU length of stay was predicted by the mean daily SOFA score (r = 0.35, p < 0.001) and tricuspid regurgitation severity (r = 0.25, p = 0.012). Conclusions: Electronic stethoscope auscultation correlates with the echocardiographic findings in ARDS patients. The RVSP and SOFA scores independently predict mortality, while valvular regurgitation severity provides additional prognostic information for long-term survival and ICU resource utilization.

INTRODUCTION:Atrial fibrillation (AFib) is the most prevalent abnormal heart rhythm, significantly increasing the risk of stroke and heart failure. Accurate and timely detection remains challenging, particularly due to the complexity of 12‑lead electrocardiogram (ECG) interpretation. While machine learning (ML) and deep learning (DL) models have demonstrated high accuracy in AFib detection, selecting the optimal input features is often non-trivial. This study aims to develop a hybrid feature selection methodology that objectively identifies the most discriminative ECG-based features for distinguishing AFib from normal sinus rhythm (NSR). MATERIAL & METHODS:We propose a hybrid framework that combines Extremely Randomized Trees (Extra-Trees) with statistical association measures to identify physiologically meaningful ECG features. Our analysis evaluates morphological, entropy-based and spectral hand-crafted features extracted from 12‑lead ECG recordings of patients who underwent catheter ablation for AFib. Two novel metrics, the feature importance score (FIS) and overall feature importance score (OFIS), are introduced to quantify feature relevance. RESULTS:The proposed approach ranked 97 extracted features and identified the 10 most important per ECG lead and 20 most relevant overall, with high consistency across leads. The interquartile range of RR-intervals achieved the highest normalized OFIS value (0.064), followed by other rhythm-related and entropy-based measures, confirming their strong discriminative power. The dimensionality of the feature space was thus reduced by nearly 80% while preserving interpretability and physiological meaning. CONCLUSIONS:This methodology provides a reproducible, interpretable and statistically grounded framework for ECG-based feature discovery, offering a preprocessing step for ML/DL models and aiding clinicians in real-time AFib detection.

Heart failure with reduced ejection fraction (HFrEF) presents a significant diagnostic challenge, particularly in advanced stages. Deep learning (DL) models offer promising potential for automated HFrEF detection, but their effectiveness is often limited in small and imbalanced clinical datasets. In this study, we propose a novel approach to enhance HFrEF detection from echocardiographic videos. We adapt the TimeSformer architecture-a Transformer-based model optimized for spatiotemporal feature extraction in video data-and apply it for the first time in echocardiography. In addition, we introduce a domain-informed left ventricle (LV) masking method using image segmentation to guide model attention toward diagnostically critical regions, boosting overall performance. Our methodology is evaluated both directly on a large-scale benchmark dataset and on a smaller, specialized clinical dataset from our cardiology department after fine-tuning. Experimental results show that the proposed framework yields substantial performance gains: on the benchmark dataset, we observe a 3% improvement in both model accuracy and area under the curve (AUC), while on the specialized dataset, improvements reach 7% in accuracy and 30% in AUC values. Moreover, TimeSformer consistently outperforms conventional approaches after the implementation of fine-tuning and LV masking. These findings highlight a practical and generalizable strategy for improving automated HFrEF diagnosis, with strong implications for clinical decision support in data-scarce healthcare settings.

Several studies introduced complex deep learning (DL) models to predict the progression of the COVID-19 pandemic, aiming to effectively manage high-dimensional sequential data. In contrast, the objective of the present analysis is the investigation of the impact of integrating additional features into recurrent neural network (RNN)-type models to enhance epidemic predictive performance. Specifically, a set of 31 features is examined, encompassing environmental factors, epidemiological indicators, infection phases, and parameters estimated using particle filtering, constituting the most complete training dataset to date. These features were incorporated into six low-complexity RNN-type models, including standard, bidirectional, and time-distributed variants of gated recurrent unit (GRU) and long short-term memory (LSTM) networks. Additionally, both linear and nonlinear statistical measures, such as Granger causality and transfer entropy, were employed to assess the importance of these features in predicting new COVID-19 cases and deaths. Feature importance scores derived from these model-free measures played a critical role in guiding feature-ranking and integration into the neural networks (NNs). The findings validate the importance of incorporating additional features and quantify their contribution to forecasting accuracy, thereby enabling optimal feature selection across all six RNN-type models. Moreover, the effectiveness of transfer entropy as a feature-ranking method is underscored, with models that excluded additional features exhibiting substantial degradation in predictive performance, often reflected in RMSE increases of over 100 to 200%. Ultimately, the proposed methodology enhances the understanding of epidemic dynamics and supports more accurate and timely decision-making.

Sleep constitutes a third of human life, underscoring its importance in health-related and psychophysiological research. Monitoring sleep stage evolution is critical for understanding sleep-related issues and diagnosing disorders. This study aims to classify sleep stages using a Hidden Markov Model (HMM) based on spectral statistical measures derived from raw electroencephalography (EEG) signals. It explores effective feature combinations to enhance classification accuracy while maintaining a practical approach requiring minimal inputs. We utilized raw EEG signals to extract various statistical features in the frequency domain, identifying combinations that maximize predictive performance. The proposed HMM was employed to classify sleep stages, leveraging these spectral features. Unlike many prior studies that focus solely on machine learning (ML) techniques, our analysis emphasizes feature significance and model interpretability. Our approach achieved a multiclass classification accuracy of 76.76

The COVID-19 pandemic has introduced significant cardiovascular challenges, particularly among patients who require intensive care. Although heart sound analysis enables non-invasive cardiac monitoring, its application in predicting clinical outcomes remains insufficiently investigated. This study introduces a novel machine learning (ML)-based framework for classifying heart sounds to assess mortality risk in intensive care unit (ICU) patients with COVID-19. A comprehensive preprocessing pipeline is implemented, incorporating bandpass filtering and wavelet decomposition for signal denoising. From segmented heart sound recordings, a set of twenty-two characteristics, entropy measures, frequency domain characteristics, and Mel frequency cepstral coefficients (MFCC) were extracted. These features were then used to train a range of ML models. Among them, the Random Forest (RF) classifier achieved the best performance, reaching 69.07% accuracy at the segment-level. By leveraging heart sound recordings for mortality prediction in ICU COVID-19 patients, this study bridges a critical gap between non-invasive monitoring and clinical risk stratification.

With the recent expansion of patient data availability and storage capabilities, healthcare entities tend to store an increasing amount of medical data locally. In addition, ongoing advances in the era of healthcare industry 5.0 and Artificial Intelligence of Things can lead to more efficient use of medical data, resulting in better health monitoring at lower costs. However, due to strict privacy restrictions related to the sensitive nature of medical data, it is often only used locally and ultimately underutilized. To this end, federated learning (FL) offers a promising solution for the efficient use of medical data, facilitating the development of reliable and robust healthcare tools. This can be achieved thanks to its decentralized nature, which allows participating entities to collaborate and thus develop and train a centralized shared model without requiring data sharing. Taking this into account, we present a Pareto-front optimization framework for FL-based health monitoring that is able to mitigate false negative predictions for the required level of false positives. By applying the proposed framework to four different medical applications, it is shown that the risk of misdiagnosis is significantly reduced, providing an additional tool for medical professionals.

Heart failure (HF) remains a significant public health challenge with high mortality rates. Machine learning (ML) techniques offer a promising approach to predict HF mortality, potentially improving clinical outcomes. However, the effectiveness of these techniques heavily depends on the quality and relevance of the features used. This study introduces a novel hybrid feature selection methodology that combines Extremely Randomized Trees (Extra-Trees) and non-linear correlation measures to enhance 1-year all-cause mortality prediction in HF patients using echocardiographic and key demographic data. Unlike existing feature selection methods that are often tied to specific ML models and produce inconsistent feature sets across different algorithms, our proposed approach is model-independent, ensuring robustness and generalizability. Moreover, the optimal number of predictive features is identified through loss graph inspection, leading to a compact and highly informative subset of seven features. We trained and evaluated seven widely-used ML models on both the full feature set and the selected subset, finding that most models maintained or improved their predictive performance despite an 80% reduction in features. Model interpretability was enhanced using SHapley Additive exPlanations (SHAP), allowing for a detailed examination of how individual features influence predictions. To further assess its effectiveness, we compared our methodology against widely known feature selection techniques across all seven ML models. The results underscore the superiority of our proposed feature set in accurately predicting HF mortality over conventional methods, offering new opportunities for personalized management strategies based on a streamlined and explainable feature subset.

With the advent of the COVID-19 pandemic, ultrasound imaging has emerged as a promising technique for COVID-19 detection, due to its non-invasive nature, affordability, and portability. In response, researchers have focused on developing AI-based scoring systems to provide real-time diagnostic support. However, the limited size and lack of proper annotation in publicly available ultrasound datasets pose significant challenges for training a robust AI model. This paper proposes MeDiVLAD, a novel pipeline to address the above issue for multi-level lung-ultrasound (LUS) severity scoring. In particular, we leverage self-knowledge distillation to pretrain a vision transformer (ViT) without label and aggregate frame-level features via dual-level VLAD aggregation. We show that with minimal finetuning, MeDiVLAD outperforms conventional fully-supervised methods in both frame- and video-level scoring, while offering classification reasoning with exceptional quality. This superior performance enables key applications such as the automatic identification of critical lung pathology areas and provides a robust solution for broader medical video classification tasks.

Let $ \left(u_n\right)_{n\geq0} $ be the special Lucas $ u $-sequence defined by \begin{document}$ u_{n+2} = Au_{n+1}-Bu_n,\quad u_0 = 0,\, u_1 = 1, $\end{document} where $ n\geq0 $, $ B = \pm1 $, and $ A $ is an integer such that $ A^2-4B > 0 $. Let \begin{document}$ a_k = \frac{1}{u_{mk}^s},\,\frac{1}{u_{mk}+u_{mk+l}},\,\frac{1}{\sum\nolimits_{i = 0}^l u_{mk+i}},\,\frac{1}{u_{mk}u_{mk+2l}},\,\frac{1}{u_{mk}u_{mk+2l-1}},\,\frac{1}{u_{mk}+C}, $\end{document} where $ m, \, l $ are positive integers, $ s = 1, 2, 3, 4 $, and $ C $ is any constant. The aim of this paper is to find a form $ g_n $ such that \begin{document}$ \underset{n\to\infty}{\lim}\left(\left(\sum\limits_{k = n}^\infty a_k\right)^{-1}-g_n\right) = 0. $\end{document} For example, we show that \begin{document}$ \underset{n\to\infty}{\lim}\left(\left( \sum\limits_{k = n}^\infty \frac{1}{u_{mk}}\right)^{-1}-\left(u_{mn}-u_{m(n-1)}\right)\right) = 0. $\end{document}

In the past few decades, technology has played a considerable role in medical research. For example, advances in machine learning algorithms have allowed for automatic diagnoses of medical conditions, through techniques involving neuroimaging data, electronic health records, and physiological biomarkers. Research in the past few decades has also resulted in a greater understanding of the causes of medical conditions, such as through biophysical models of the body and computational genetics. In parallel with other medical fields, computational models in psychology have opened new avenues of diagnosis and prognosis of mental health conditions. In this article we describe common mental health conditions and discuss computational approaches in mental health, including drift diffusion modeling, reinforcement learning, and machine learning approaches.

Chronic respiratory diseases are leading causes of morbidity and mortality in EU and worldwide. The current management of respiratory diseases allows only a momentary patient assessment at the time point of outpatient department visit or hospitalization. The incorporation of novel low-cost electronics in garments presents great potential for making accurate and effective continuous monitoring of lung diseases feasible. The EU WELMO project (Wearable Electronics for Effective Lung Monitoring) developed novel miniaturized sensors, integrated to a comfortable vest, enabling the accurate and continuous monitoring of the lungs, through the collection of lung sounds and EIT signals, that can be combined, processed and linked with specific clinical outcome, rendering the systematic, accurate and real-time evaluation of respiratory conditions possible. The data and features derived from lung sounds, EIT and medical sensors are best exploited in an integrated manner, towards identifying patterns of diagnostic value and patient coaching/adherence value. The proposed WELMO also offers novel algorithms for processing the collected data, and applications for presentation of the processing outcomes. Two user applications the Patient app and the HCP app were developed. This paper presents the design of WELMO Patient App. The description of software design yields the systematic process to consider every aspect and challenge of the proposed system. The presentation focuses on the functionality of Patient Application and the ease of use by health experts. The design led to a functional software which complements the hardware and is an essential and novel part of the innovative lung monitoring system of WELMO.

Background The Covid-19 pandemic has caused immense pressure on Intensive Care Units (ICU). In patients with severe ARDS due to Covid-19, respiratory mechanics are important for determining the severity of lung damage. Lung auscultation could not be used during the pandemic despite its merit. The main objective of this study was to investigate associations between lung auscultatory sound features and lung mechanical properties, length of stay (LOS) and survival, in adults with severe Covid-19 ARDS.Methods Consecutive patients admitted to a large ICU between 2020 and 2021 (n = 173) were included. Digital stethoscopes obtained auscultatory sounds and stored them in an on-line database for replay and further processing using advanced AI techniques. Correlation and regression analysis explored relationships between digital auscultation findings and lung mechanics or the ICU outcome. The resulting annotated lung sounds database is also publicly available as supplementary material.Results The presence of squawks was associated with the ICU LOS, outcome and 90-day mortality. Other features (age, SOFA score & oxygenation index upon admission, minimum crackle entropy) had significant impact on outcome. Additional features affecting the 90-d survival were age and mean crackle entropy. Multivariate logistic regression showed that survival was affected by age, baseline SOFA, baseline oxygenation index and minimum crackle entropy.Conclusions Respiratory mechanics were associated with various adventitious sounds, whereas the lung sound analytics and the presence of certain adventitious sounds correlated with the ICU outcome and the 90-d survival. Spectral features of crackles sounds can serve as prognostic factors for survival, highlighting the importance of digital auscultation.

BACKGROUND:Despite COVID-19 vaccine mandates, many chose to forgo vaccination, raising questions about the psychology underlying how judgment affects these choices. Research shows that reward and aversion judgments are important for vaccination choice; however, no studies have integrated such cognitive science with machine learning to predict COVID-19 vaccine uptake. OBJECTIVE:This study aims to determine the predictive power of a small but interpretable set of judgment variables using 3 machine learning algorithms to predict COVID-19 vaccine uptake and interpret what profile of judgment variables was important for prediction. METHODS:We surveyed 3476 adults across the United States in December 2021. Participants answered demographic, COVID-19 vaccine uptake (ie, whether participants were fully vaccinated), and COVID-19 precaution questions. Participants also completed a picture-rating task using images from the International Affective Picture System. Images were rated on a Likert-type scale to calibrate the degree of liking and disliking. Ratings were computationally modeled using relative preference theory to produce a set of graphs for each participant (minimum R2>0.8). In total, 15 judgment features were extracted from these graphs, 2 being analogous to risk and loss aversion from behavioral economics. These judgment variables, along with demographics, were compared between those who were fully vaccinated and those who were not. In total, 3 machine learning approaches (random forest, balanced random forest [BRF], and logistic regression) were used to test how well judgment, demographic, and COVID-19 precaution variables predicted vaccine uptake. Mediation and moderation were implemented to assess statistical mechanisms underlying successful prediction. RESULTS:Age, income, marital status, employment status, ethnicity, educational level, and sex differed by vaccine uptake (Wilcoxon rank sum and chi-square P<.001). Most judgment variables also differed by vaccine uptake (Wilcoxon rank sum P<.05). A similar area under the receiver operating characteristic curve (AUROC) was achieved by the 3 machine learning frameworks, although random forest and logistic regression produced specificities between 30% and 38% (vs 74.2% for BRF), indicating a lower performance in predicting unvaccinated participants. BRF achieved high precision (87.8%) and AUROC (79%) with moderate to high accuracy (70.8%) and balanced recall (69.6%) and specificity (74.2%). It should be noted that, for BRF, the negative predictive value was <50% despite good specificity. For BRF and random forest, 63% to 75% of the feature importance came from the 15 judgment variables. Furthermore, age, income, and educational level mediated relationships between judgment variables and vaccine uptake. CONCLUSIONS:The findings demonstrate the underlying importance of judgment variables for vaccine choice and uptake, suggesting that vaccine education and messaging might target varying judgment profiles to improve uptake. These methods could also be used to aid vaccine rollouts and health care preparedness by providing location-specific details (eg, identifying areas that may experience low vaccination and high hospitalization).

In recent years, computerized methods for analyzing respiratory function have gained increased attention within the scientific community. This study proposes a deep-learning model to estimate the dimensionless respiratory airflow using only respiratory sound without prior calibration. We developed hybrid deep learning models (CNN + LSTM) to extract features from the respiratory sound and model their temporal dependencies. Then, we used an ensemble approach to combine multiple outputs of our models and obtain the respiratory airflow waveform for entire respiratory audio signals as the final output. We conducted a comprehensive set of experiments and evaluated the models using several regression evaluation metrics to assess how the models would perform in various circumstances of different complexity. The methods were developed and evaluated considering respiratory sound and electrical impedance tomography (EIT) data from 50 respiratory patients (15 female and 35 male with an average age of 67.4 ± 8.9 years and body mass index of 27.8 ± 5.6 kg/m2). An external assessment was conducted using an external database, the Respiratory Sound Database (RSD). This was an indirect evaluation because the RSD does not provide the ground truth values of the dimensionless respiratory airflow. In the most complex evaluation task (Task II), we achieved the following results for the estimation of the normalized dimensionless respiratory airflow curve: mean absolute error = 0.134 ± 0.061; root mean squared error = 0.170 ± 0.075; dynamic time warping similarity = 3.282 ± 1.514; Pearson correlation coefficient = 0.770 ± 0.235. External assessment with the RSD showed that the performance of our model decreased when devices different from the ones used for their training were considered. Our study demonstrated that deep learning models could reliably estimate the dimensionless respiratory airflow.

The prediction of suicidal thought and behavior has met with mixed results. This study of 3,476 de-identified participants (4,019 before data exclusion) quantified the prediction of four suicidal thought and behavior (STB) variables using a short reward/aversion judgment task and a limited set of demographic and mental health surveys. The focus was to produce a simple, quick and objective framework for assessing STB that might be automatable, without the use of big data. A balanced random forest classifier performed better than a Gaussian mixture model and four standard machine learning classifiers for predicting passive suicide ideation, active suicide ideation, suicide planning and planning for safety. Accuracies ranged from 78% to 92% (optimal area under the curve between 0.80 and 0.95) without overfitting, and peak performance was observed for predicting suicide planning. The relative importance of features for prediction showed distinct weighting across judgment variables, contributing between 40% and 64% to prediction per Gini scores. Mediation/moderation analyses showed that depression, anxiety, loneliness and age variables moderated the judgment variables, indicating that the interaction of judgment with mental health and demographic indices is fundamental for the high-accuracy prediction of STB. These findings suggest the feasibility of an efficient and highly scalable system for suicide assessment, without requiring psychiatric records or neural measures. The findings suggest that STB might be understood within a cognitive framework for judgment with quantitative variables whose unique constellation separates passive and active suicidal thought (ideation) from suicide planning and planning for safety.

BackgroundHeart failure (HF), a global health challenge, requires innovative diagnostic and management approaches. The rapid evolution of deep learning (DL) in healthcare necessitates a comprehensive review to evaluate these developments and their potential to enhance HF evaluation, aligning clinical practices with technological advancements.ObjectiveThis review aims to systematically explore the contributions of DL technologies in the assessment of HF, focusing on their potential to improve diagnostic accuracy, personalize treatment strategies, and address the impact of comorbidities.MethodsA thorough literature search was conducted across four major electronic databases: PubMed, Scopus, Web of Science and IEEE Xplore, yielding 137 articles that were subsequently categorized into five primary application areas: cardiovascular disease (CVD) classification, HF detection, image analysis, risk assessment, and other clinical analyses. The selection criteria focused on studies utilizing DL algorithms for HF assessment, not limited to HF detection but extending to any attempt in analyzing and interpreting HF-related data.ResultsThe analysis revealed a notable emphasis on CVD classification and HF detection, with DL algorithms showing significant promise in distinguishing between affected individuals and healthy subjects. Furthermore, the review highlights DL's capacity to identify underlying cardiomyopathies and other comorbidities, underscoring its utility in refining diagnostic processes and tailoring treatment plans to individual patient needs.ConclusionsThis review establishes DL as a key innovation in HF management, highlighting its role in advancing diagnostic accuracy and personalized care. The insights provided advocate for the integration of DL in clinical settings and suggest directions for future research to enhance patient outcomes in HF care.

Assessing the health status of critically ill patients with COVID-19 and predicting their outcome are highly challenging problems and one of the reasons for poor management of ICU resources worldwide. A better pathophysiological understanding of patients’ state evolution in the ICU can enhance effective medical interventions. Therefore, there is a need to monitor and analyze the pulmonary function of a ICU patient with COVID-19 and its impact on cardiovascular and other systems. To achieve this, chest X-rays (CXRs), respiratory sounds and all the routinely monitored parameters, scores and metrics in the COVID-19 ICU were recorded from 171 ICU patients with COVID-19 from June 2020 until December 2021. Features were extracted from respiratory sounds, deep learning analysis was conducted on CXRs, and logistic regression analysis was performed on routine ICU clinical variables. Deep learning pipelines were established to classify patients’ outcomes (survival or death) at two time points (ICU mortality or 90-day mortality) using three input configurations: (a) CXRs, (b) a fusion of CXRs and respiratory sounds features, or (c) a fusion of CXRs, respiratory sounds features, and principal features of the ICU clinical measurements. The performance of the latter approach was promising, achieving, for ICU mortality, an accuracy of 0.761 and an AUC of 0.759, and for 90-day mortality, an accuracy of 0.743 and an AUC of 0.752, while the performance of approaches (a) and (b) was worse. Therefore, using multi-source data and longitudinal COVID-19 ICU data offers a better prediction of the outcome in the ICU, thereby optimizing medical decisions and interventions. Furthermore, we show that adding the adventitious respiratory sounds features significantly increased AUC and accuracy for mortality prediction of ICU patients with COVID-19.

Anxiety, a condition characterized by intense fear and persistent worry, affects millions each year and, when severe, is distressing and functionally impairing. Numerous machine learning frameworks have been developed and tested to predict features of anxiety and anxiety traits. This study extended these approaches by using a small set of interpretable judgment variables (n = 15) and contextual variables (demographics, perceived loneliness, COVID-19 history) to (1) understand the relationships between these variables and (2) develop a framework to predict anxiety levels [derived from the State Trait Anxiety Inventory (STAI)]. This set of 15 judgment variables, including loss aversion and risk aversion, models biases in reward/aversion judgments extracted from an unsupervised, short (2–3 min) picture rating task (using the International Affective Picture System) that can be completed on a smartphone. The study cohort consisted of 3476 de-identified adult participants from across the United States who were recruited using an email survey database. Using a balanced Random Forest approach with these judgment and contextual variables, STAI-derived anxiety levels were predicted with up to 81% accuracy and 0.71 AUC ROC. Normalized Gini scores showed that the most important predictors (age, loneliness, household income, employment status) contributed a total of 29–31% of the cumulative relative importance and up to 61% was contributed by judgment variables. Mediation/moderation statistics revealed that the interactions between judgment and contextual variables appears to be important for accurately predicting anxiety levels. Median shifts in judgment variables described a behavioral profile for individuals with higher anxiety levels that was characterized by less resilience, more avoidance, and more indifference behavior. This study supports the hypothesis that distinct constellations of 15 interpretable judgment variables, along with contextual variables, could yield an efficient and highly scalable system for mental health assessment. These results contribute to our understanding of underlying psychological processes that are necessary to characterize what causes variance in anxiety conditions and its behaviors, which can impact treatment development and efficacy.

Operant keypress tasks, where each action has a consequence, have been analogized to the construct of "wanting" and produce lawful relationships in humans that quantify preferences for approach and avoidance behavior. It is unknown if rating tasks without an operant framework, which can be analogized to "liking", show similar lawful relationships. We studied three independent cohorts of participants (N = 501, 506, and 4,019 participants) collected by two distinct organizations, using the same 7-point Likert scale to rate negative to positive preferences for pictures from the International Affective Picture Set. Picture ratings without an operant framework produced similar value functions, limit functions, and trade-off functions to those reported in the literature for operant keypress tasks, all with goodness of fits above 0.75. These value, limit, and trade-off functions were discrete in their mathematical formulation, recurrent across all three independent cohorts, and demonstrated scaling between individual and group curves. In all three experiments, the computation of loss aversion showed 95% confidence intervals below the value of 2, arguing against a strong overweighting of losses relative to gains, as has previously been reported for keypress tasks or games of chance with calibrated uncertainty. Graphed features from the three cohorts were similar and argue that preference assessments meet three of four criteria for lawfulness, providing a simple, short, and low-cost method for the quantitative assessment of preference without forced choice decisions, games of chance, or operant keypressing. This approach can easily be implemented on any digital device with a screen (e.g., cellphones).