PurposeThis paper aims to conceptualize and test an integrated model of online grocery buying intention by extending technology acceptance model by adding several antecedents of online grocery shopping behaviour such as physical effort, time pressure, entertainment value, product assortment, economic values, website design aesthetics, etc. The ultimate dependent variable was consumer’s satisfaction with buying process of grocery product via online platform. Design/methodology/approachThe model was tested over online grocery shoppers using structural equation modelling approach. To enhance the validity of the finding, common method bias and social desirability bias were also assessed. FindingsAs product assortment was found to have a significant impact on both perceived ease of use and perceived usefulness, it supports the notion of one-stop solution as a major driver to attract buyers to buy groceries online. Findings also highlight the importance of entertainment value and economic value as key variables which shape the buyer’s satisfaction and purchase loyalty behaviour. Overall, the results support the proposed model. Practical/implicationsThe findings of this study would be helpful for online marketers to get more website visits and to increase conversion rates, i.e. getting their visitors to spend more time on the website and to make purchase. Originality/valueThis integrated framework tested here is quite comprehensive in nature, as it includes the influence of time pressure, physical effort and product assortment on online buying behaviour. These basic yet important variables to study, especially when the industry (online grocery shopping) is still in its nascent stage, are missing from the literature. The present study also involves a rigorous data analysis process followed by assessment of common method bias and psychometric property test. Such approach is rare in existing body of knowledge. The study uses S-O-R framework for hypothesis and model development, which is also rare in context of online grocery shopping.

INTRODUCTION:The pharmacological treatment of Major Depressive Disorder (MDD) continues to rely predominantly on a trial-and-error approach. Here, we introduce an artificial intelligence (AI) model aiming to personalize treatment and improve outcomes, which was deployed in the Artificial Intelligence in Depression -Medication Enhancement (AID-ME) Study.OBJECTIVES: 1) Develop a model capable of predicting probabilities of remission across multiple pharmacological treatments for adults with at least moderate major depression. 2) Validate model predictions and examine them for amplification of harmful biases. METHODS: Data from previous clinical trials of antidepressant medications were sourced from the NIMH, collaborating researchers, and pharmaceutical open science platforms, and standardized into a common framework. Our analysis included 9,042 adults with moderate to severe major depression from 22 studies, each lasting 8-14 weeks, and covering 10 different treatments. The data was divided into training, validation, and held-out test sets. Feature selection selected 25 clinical and demographic variables. Using Bayesian optimization, a deep learning model was trained on the training set and refined using the validation set before being tested once on the held-out test set. Pre-specified post-hoc testing was performed to assess for potential clinical utility as well as risk of the amplification of harmful biases and biased subgroup performance.RESULTS: In the evaluation on the held-out test set, the model demonstrated a performance with an AUC (Area Under the Curve) score of 0.65. The model outperformed a null model on the test set (p = 0.01). The model demonstrated notable clinical utility, achieving an absolute improvement in population remission rate from 43.15% to 53.99% under hypothetical "naive" analysis and an improvement from 43.21 to 55.08% improvement under actual improvement "conservative" analysis on the testing data. While the model did identify one drug (escitalopram)

Introduction: Suicidal ideation (SI) is prevalent in the general population, and is a prominent risk factor for suicide. However, predicting which patients are likely to have SI remains a challenge. Deep Learning (DL) may be a useful tool in this context, as it can be used to find patterns in complex, heterogeneous, and incomplete psychiatric datasets. An automated screening system for SI could help prompt clinicians to be more attentive to patients at risk for suicide. Methods: Using the Canadian Community Health Survey - Mental Health Component, we trained a DL model based on 23,859 survey responses to predict lifetime SI on an individual patient basis. Models were created to predict both lifetime and last 12 month SI. We reduced 582 possible model parameters captured by the survey to 96 and 21 feature versions of the models. Models were trained using an undersampling procedure that balanced the training set between SI and non-SI respondents; validation was done on held-out data. Results: AUC was used as the main model metric. For lifetime SI, the 96 feature model had an AUC of 0.79 and the 21 feature model had an AUC of 0.75. For SI in the last 12 months the 96 feature model had an AUC of 0.76 and the 21 feature model had an AUC of 0.69. DL outperformed random forest classifiers. Discussion: Although requiring further study to ensure clinical relevance and sample generalizability, this study is a proof-of-concept for the use of DL to improve prediction of SI. This kind of model would help start conversations with patients which could lead to improved care and, it is hoped, a reduction in suicidal behavior.

Introduction: Suicidal ideation (SI) is prevalent in the general population, and is a risk factor for suicide. Predicting which patients are likely to have SI remains challenging. Deep Learning (DL) may be a useful tool in this context, as it can be used to find patterns in complex, heterogeneous, and incomplete datasets. An automated screening system for SI could help prompt clinicians to be more attentive to patients at risk for suicide.Methods: Using the Canadian Community Health Survey—Mental Health Component, we trained a DL model based on 23,859 survey responses to classify patients with and without SI. Models were created to classify both lifetime SI and SI over the last 12 months. From 582 possible parameters we produced 96- and 21-feature versions of the models. Models were trained using an undersampling procedure that balanced the training set between SI and non-SI; validation was done on held-out data.Results: For lifetime SI, the 96 feature model had an Area under the receiver operating curve (AUC) of 0.79 and the 21 feature model had an AUC of 0.77. For SI in the last 12 months the 96 feature model had an AUC of 0.71 and the 21 feature model had an AUC of 0.68. In addition, sensitivity analyses demonstrated feature relationships in line with existing literature.Discussion: Although further study is required to ensure clinical relevance and sample generalizability, this study is an initial proof of concept for the use of DL to improve identification of SI. Sensitivity analyses can help improve the interpretability of DL models. This kind of model would help start conversations with patients which could lead to improved care and a reduction in suicidal behavior.