Background: While psychological treatments are effective, a substantial portion of patients do not benefit enough. Early identification of those may allow for adaptive treatment strategies and improved outcomes. We aimed to evaluate the clinical usefulness of machine-learning (ML) models predicting outcomes in Internet-based Cognitive Behavioural Therapy, to compare ML-related methodological choices, and guide future use of these. Methods: Eighty main models were compared. Baseline variables, weekly symptoms, and treatment activity were used to predict treatment outcomes in a dataset of 6695 patients from regular care. Results: We show that the best models use handpicked predictors and impute missing data. No ML algorithm shows clear superiority. They have a mean balanced accuracy of 78.1% at treatment week four, closely matched by regression (77.8%). Conclusions: ML surpasses the benchmark for clinical usefulness (67%). Advanced and simple models perform equally, indicating a need for more data or smarter methodological designs to confirm advantages of ML. 

Background: Optimising maintenance drug treatment selection for people with bipolar disorder is challenging. There is some evidence that clinical and demographic features may predict response to lithium. However, attempts to personalise treatment choice have been limited. Method: We aimed to determine if machine learning methods applied to electronic health records could predict differential response to lithium or olanzapine. From electronic United Kingdom primary care records, we extracted a cohort of individuals prescribed either lithium (19,106 individuals) or olanzapine (12,412) monotherapy. Machine learning models were used to predict successful monotherapy maintenance treatment, using 113 clinical and demographic variables, 8,017 (41.96%) lithium responders and 3,831 (30.87%) olanzapine responders. Results: We found a quantitative structural difference in that lithium maintenance responders were weakly predictable in our holdout sample, consisting of the 5% of patients with the most recent exposure. Age at first diagnosis, age at first treatment and the time between these were the most important variables in all models. Discussion: Even if we failed to predict successful monotherapy olanzapine treatment, and so to definitively separate lithium vs. olanzapine responders, the characterization of the two groups may be used for classification by proxy. This can, in turn, be useful for establishing maintenance therapy. The further exploration of machine learning methods on EHR data for drug treatment selection could in the future play a role for clinical decision support. Signals in the data encourage further experiments with larger datasets to definitively separate lithium vs. olanzapine responders. 

In a long-term commitment to designing for the aesthetics of human–drone interactions, we have been troubled by the lack of tools for shaping and interactively feeling drone behaviours. By observing participants in a three-day drone challenge, we isolated components of drones that, if made transparent, could have helped participants better explore their aesthetic potential. Through a bricolage approach to analysing interviews, feld notes, video recordings, and inspection of each team’s code, we describe how teams 1) shifted their eforts from aiming for seamless human–drone interaction, to seeing drones as fragile, wilful, and prone to crashes; 2) engaged with intimate, bodily interactions to more precisely probe, understand and defne their drone’s capabilities; 3) adopted diferent workaround strategies, emphasising either training the drone or the pilot. We contribute an empirical account of constraints in shaping the potential aesthetics of drone behaviour, and discuss how programming environments could better support somaesthetic perception–action loops for design and programming purposes.

We present a novel intercorporeal experience - an intersubjective haptic voice. Through an autobiographical design inquiry, based on singing techniques from the classical opera tradition, we created Corsetto, a kinesthetic garment for transferring somatic reminiscents of vocal experience from an expert singer to a listener. We then composed haptic gestures enacted in the Corsetto, emulating upper-body movements of the live singer performing a piece by Morton Feldman named Three Voices. The gestures in the Corsetto added a haptics-based 'fourth voice' to the immersive opera performance. Finally, we invited audiences who were asked to wear Corsetto during live performances. Afterwards they engaged in micro-phenomenological interviews. The analysis revealed how the Corsetto managed to bridge inner and outer bodily sensations, creating a feeling of a shared intercorporeal experience, dissolving boundaries between listener, singer and performance. We propose that 'intersubjective haptics' can be a generative medium not only for singing performances, but other possible intersubjective experiences. © 2023 Owner/Author.

This study applied supervised machine learning with multi-modal data to predict remission of major depressive disorder (MDD) after psychotherapy. Genotyped adult patients (n = 894, 65.5% women, age 18–75 years) diagnosed with mild-to-moderate MDD and treated with guided Internet-based Cognitive Behaviour Therapy (ICBT) at the Internet Psychiatry Clinic in Stockholm were included (2008–2016). Predictor types were demographic, clinical, process (e.g., time to complete online questionnaires), and genetic (polygenic risk scores). Outcome was remission status post ICBT (cut-off ≤10 on MADRS-S). Data were split into train (60%) and validation (40%) given ICBT start date. Predictor selection employed human expertise followed by recursive feature elimination. Model derivation was internally validated through cross-validation. The final random forest model was externally validated against a (i) null, (ii) logit, (iii) XGBoost, and (iv) blended meta-ensemble model on the hold-out validation set. Feature selection retained 45 predictors representing all four predictor types. With unseen validation data, the final random forest model proved reasonably accurate at classifying post ICBT remission (Accuracy 0.656 [0.604, 0.705], P vs null model = 0.004; AUC 0.687 [0.631, 0.743]), slightly better vs logit (bootstrap D = 1.730, P = 0.084) but not vs XGBoost (D = 0.463, P = 0.643). Transparency analysis showed model usage of all predictor types at both the group and individual patient level. A new, multi-modal classifier for predicting MDD remission status after ICBT treatment in routine psychiatric care was derived and empirically validated. The multi-modal approach to predicting remission may inform tailored treatment, and deserves further investigation to attain clinical usefulness. © 2022, The Author(s).

Cooperative Intelligent Transport Systems (C-ITS) enable information to be shared wirelessly between vehicles and infrastructure in order to improve transport safety and efficiency. Delivering C-ITS services using existing cellular networks offers both financial and technological advantages, not least since these networks already offer many of the features needed by C-ITS, and since many vehicles on our roads are already connected to cellular networks. Still, C-ITS pose stringent requirements in terms of availability and latency on the underlying communication system; requirements that will be hard to meet for currently deployed 3G, LTE, and early-generation 5G systems. Through a series of experiments in the MONROE testbed (a cross-national, mobile broadband testbed), the present study demonstrates how cellular multi-access selection algorithms can provide close to 100% availability, and significantly reduce C-ITS transaction times. The study also proposes and evaluates a number of low-complexity, low-overhead single-access selection algorithms, and shows that it is possible to design such solutions so that they offer transaction times and availability levels that rival those of multi-access solutions.

We investigate the feasibility of applying standard text categorisation methods to patient text in order to predict treatment outcome in Internet-based cognitive behavioural therapy. The data set is unique in its detail and size for regular care for depression, social anxiety, and panic disorder. Our results indicate that there is a signal in the depression data, albeit a weak one. We also perform terminological and sentiment analysis, which confirm those results. 

In this paper we present our late-breaking work in leveraging a soft robotic fiber-based wearable system for the transposition of somatic knowledge and experience within the context of singing. We examine how the transposition of the physical nuances of singing from one body to another, or multiple other bodies, is possible by engaging with a soma design process. We share our findings in the context of experience transposition, resulting in a preliminary prototype: a pneumatically controlled soft robotic garment—called ADA (short for air-driven actuator) for re-enacting felt experiences of singing onto the human body. We contribute with 1) our initial findings in transposing singing experiences between and across bodies, and 2) a preliminary wearable robotic garment to mediate intersomatic experiences of singing.

OBJECTIVE: Therapist guided Internet-Delivered Cognitive Behavior Therapy (ICBT) is effective, but as in traditional CBT, not all patients improve, and clinicians generally fail to identify them early enough. We predict treatment failure in 12-week regular care ICBT for Depression, Panic disorder and Social anxiety disorder, using only patients' weekly symptom ratings to identify when the accuracy of predictions exceed 2 benchmarks: (a) chance, and (b) empirically derived clinician preferences for actionable predictions.

METHOD: Screening, pretreatment and weekly symptom ratings from 4310 regular care ICBT-patients from the Internet Psychiatry Clinic in Stockholm, Sweden was analyzed in a series of regression models each adding 1 more week of data. Final score was predicted in a holdout test sample, which was then categorized into Success or Failure (failure defined as the absence of both remitter and responder status). Classification analyses with Balanced Accuracy and 95% Confidence intervals was then compared to predefined benchmarks.

RESULTS: Benchmark 1 (better than chance) was reached 1 week into all treatments. Social anxiety disorder reached Benchmark 2 (> 65%) at week 5, whereas Depression and Panic Disorder reached it at week 6.

CONCLUSIONS: For depression, social anxiety and panic disorder, prediction with only patient-rated symptom scores can detect treatment failure 6 weeks into ICBT, with enough accuracy for a clinician to take action. Early identification of failing treatment attempts may be a viable way to increase the overall success rate of existing psychological treatments by providing extra clinical resources to at-risk patients, within a so-called Adaptive Treatment Strategy. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Given the diverse focuses of emerging online social networks (OSNs), it is common that a user has signed up on multiple OSNs. Social hub services, a.k.a., social directory services, help each user manage and exhibit her OSN accounts on one webpage. In this work, we conduct a data-driven study by crawling over one million user profiles from about.me, a representative online social hub service. Our study aims at gaining insights on cross-OSN social influence from the crawled data. We first analyze the composition of the social hub users. For each user, we collect her social accounts from her social hub webpage, and aggregate the content generated by these accounts on different OSNs to gain a comprehensive view of this user. According to our analysis, there is a high probability that a user would provide consistent information on different OSNs. We then explore the correlation between user activities on different OSNs, based on which we propose a cross-OSN social influence prediction model. With the model, we can accurately predict a user’s social influence on emerging OSNs, such as Instagram, Foursquare, and Flickr, based on her data published on well-established OSNs like Twitter.