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Background This study sought to predict postsurgical seizure freedom from pre-operative diagnostic test results and clinical information using a rapid automated approach, based on supervised learning methods in patients with drug-resistant focal seizures suspected to begin in temporal lobe. Method We applied machine learning, specifically a combination of mutual information-based feature selection and supervised learning classifiers on multimodal data, to predict surgery outcome retrospectively in 20 presurgical patients (13 female; mean age±SD, in years 33±9.7 for females, and 35.3±9.4 for males) who were diagnosed with mesial temporal lobe epilepsy (MTLE) and subsequently underwent standard anteromesial temporal lobectomy. The main advantage of the present work over previous studies is the inclusion of the extent of ipsilateral neocortical gray matter atrophy and spatiotemporal properties of depth electrode-recorded seizures as training features for individual patient surgery planning. Results A maximum relevance minimum redundancy (mRMR) feature selector identified the following features as the most informative predictors of postsurgical seizure freedom in this study's sample of patients: family history of epilepsy, ictal EEG onset pattern (positive correlation with seizure freedom), MRI-based gray matter thickness reduction in the hemisphere ipsilateral to seizure onset, proportion of seizures that first appeared in ipsilateral amygdala to total seizures, age, epilepsy duration, delay in the spread of ipsilateral ictal discharges from site of onset, gender, and number of electrode contacts at seizure onset (negative correlation with seizure freedom). Using these features in combination with a least square support vector machine (LS-SVM) classifier compared to other commonly used classifiers resulted in very high surgical outcome prediction accuracy (95%). Conclusions Supervised machine learning using multimodal compared to unimodal data accurately predicted postsurgical outcome in patients with atypical MTLE.

Purpose – This paper aims to examine the effect of different relational bonding strategies on franchisees’ perceptions of benefits. The duration of the relationship is framed as a moderator between three types of relational bonds and the perceived benefits. Design/methodology/approach – The data are collected via a survey from foodservice franchisees in South Korea. To test the study’s hypotheses, the research model was estimated with two-stage least squares. Findings – The result shows that social and structural bonds have a significant impact on franchisees’ perceptions of benefits. There are some significant interactions between different types of relational bonds and the duration of the relationship. Perceptions of benefits are found to influence satisfaction, intentions to recommend, intentions to renew the contract and long-term orientation. Practical implications – The study suggests that franchisors may want to focus on developing and strengthening social bonds, and also customize their relational approaches based on the duration of the relationship with the franchisees. Originality/value – This research illustrates the impact of three types of relational bonding strategies on franchisees’ perceptions of the benefits and also examines the significant moderating role of the duration of the relationship.