Natural language processing (NLP) techniques can be used to make inferences about peoples’ mental states from what they write on Facebook, Twitter and other social media. These inferences can then be used to create online pathways to direct people to health information and assistance and also to generate personalized interventions. Regrettably, the computational methods used to collect, process and utilize online writing data, as well as the evaluations of these techniques, are still dispersed in the literature. This paper provides a taxonomy of data sources and techniques that have been used for mental health support and intervention. Specifically, we review how social media and other data sources have been used to detect emotions and identify people who may be in need of psychological assistance; the computational techniques used in labeling and diagnosis; and finally, we discuss ways to generate and personalize mental health interventions. The overarching aim of this scoping review is to highlight areas of research where NLP has been applied in the mental health literature and to help develop a common language that draws together the fields of mental health, human-computer interaction and NLP.

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Natural language processing in mental health applications using non-clinical texts†

Assessing long-term user experience on a mobile health application through an in-app embedded conversation-based questionnaire

This paper presents an approach for reproducing optimal 3-D facial expressions based on blendshape regression. It aims to improve fidelity of facial expressions but maintain the efficiency of the blendshape method, which is necessary for applications such as human–machine interaction and avatars. The method intends to optimize the given facial expression using action units (AUs) based on the facial action coding system recorded from human faces. To help capture facial movements for the target face, an intermediate model space is generated, where both the target and source AUs have the same mesh topology and vertex number. The optimization is conducted interactively in the intermediate model space through adjusting the regulating parameter. The optimized facial expression model is transferred back to the target facial model to produce the final facial expression. We demonstrate that given a sketched facial expression with rough vertex positions indicating the intended facial expression, the proposed method approaches the sketched facial expression through automatically selecting blendshapes with corresponding weights. The sketched expression model is finally approximated through AUs representing true muscle movements, which improves the fidelity of facial expressions.

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Regression-Based Facial Expression Optimization

About one-third of individuals with focal epilepsy continue to have seizures despite optimal medical management. These patients are potentially curable with neurosurgery if the epileptogenic zone (EZ) can be identified and resected. Stereo-electroencephalography (SEEG) to record epileptic activity with intracranial depth electrodes may be required to identify the EZ. Each SEEG electrode trajectory, the path between the entry on the skull and the cerebral target, must be planned carefully to avoid trauma to blood vessels and conflicts between electrodes. In current clinical practice trajectories are determined manually, typically taking 2–3 h per patient (15 min per electrode). Manual planning (MP) aims to achieve an implantation plan with good coverage of the putative EZ, an optimal spatial resolution, and 3D distribution of electrodes. Computer-assisted planning tools can reduce planning time by quantifying trajectory suitability. We present an automated multiple trajectory planning (MTP) algorithm to compute implantation plans. MTP uses dynamic programming to determine a set of plans. From this set a depth-first search algorithm finds a suitable plan. We compared our MTP algorithm to (a) MP and (b) an automated single trajectory planning (STP) algorithm on 18 patient plans containing 165 electrodes. MTP changed all 165 trajectories compared to MP. Changes resulted in lower risk (122), increased grey matter sampling (99), shorter length (92), and surgically preferred entry angles (113). MTP changed 42 % (69/165) trajectories compared to STP. Every plan had between 1 to 8 (median 3.5) trajectories changed to resolve electrode conflicts, resulting in surgically preferred plans. MTP is computationally efficient, determining implantation plans containing 7–12 electrodes within 1 min, compared to 2–3 h for MP.

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Automated multiple trajectory planning algorithm for the placement of stereo-electroencephalography (SEEG) electrodes in epilepsy treatment.

Approximately 20–30% of patients with focal epilepsy are medically refractory and may be candidates for curative surgery. Stereo EEG is the placement of multiple depth electrodes into the brain to record seizure activity and precisely identify the area to be resected. The two important criteria for electrode implantation are accurate navigation to the target area, and avoidance of critical structures such as blood vessels. In current practice neurosurgeons have no assistance in the planning of the electrode trajectories.

/pdf/a-computer-assisted-planning-system-for-the-placement-of-t87p8pe99c.pdf

A Computer Assisted Planning System for the Placement of sEEG Electrodes in the Treatment of Epilepsy

Advances in computational methods and hardware platforms provide efficient processing of medical-imaging datasets for surgical planning. For neurosurgical interventions employing a straight access path, planning entails selecting a path from the scalp to the target area that's of minimal risk to the patient. A proposed GPU-accelerated method enables interactive quantitative estimation of the risk for a particular path. It exploits acceleration spatial data structures and efficient implementation of algorithms on GPUs. In evaluations of its computational efficiency and scalability, it achieved interactive rates even for high-resolution meshes. A user study and feedback from neurosurgeons identified this methods' potential benefits for preoperative planning and intraoperative replanning.

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GPU-Accelerated Interactive Visualization and Planning of Neurosurgical Interventions

In this letter, we investigate the use of conversational avatars as a means to improve the user experience on instant messaging (IM) for mobile devices. We describe the design and implementation of an interface for IM featuring a 3-D facial avatar that is driven by the text messages being exchanged between chatting participants. Our design is affordable under the limited computational capacity of current generation mobiles. We evaluate user acceptance and reaction via user studies, by comparing it to a more conventional IM interface, and provide recommendations for the effective design of conversational avatar interfaces for mobile applications.

A Text-Driven Conversational Avatar Interface for Instant Messaging on Mobile Devices

Ray tracing is at the core of most techniques for creating realistic imagery. Parallel implementations of ray tracing handle the irregular workload through task systems. The strengths of static and dynamic scheduling strategies are complementary to each other. Static strategies do not incur in synchronization overhead while dynamic strategies generally provide computational times closer to the optimal scheduling. Hybrid strategies combining good static initialization and dynamic task assignation have been shown to be a better alternative than pure static and dynamic strategies [Heirich and Arvo 1998]. We experiment with a novel strategy for load balancing on multi-GPU systems. We obtain a quick estimate of the cost of traversing batches of rays over bounding volume hierarchies. The estimated costs are used to achieve a tighter assignation of tasks to processing units. Results suggest that cost-based initialization can enhance common balancing strategies and reduce rendering times.

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Mario Rincon-Nigro

Papers

GPU-Accelerated Interactive Visualization and Planning of Neurosurgical Interventions

A Text-Driven Conversational Avatar Interface for Instant Messaging on Mobile Devices

Cost-based workload balancing for ray tracing on multi-GPU systems