Currently, many critical care indices are not captured automatically at a granular level, rather are repetitively assessed by overburdened nurses. In this pilot study, we examined the feasibility of using pervasive sensing technology and artificial intelligence for autonomous and granular monitoring in the Intensive Care Unit (ICU). As an exemplary prevalent condition, we characterized delirious patients and their environment. We used wearable sensors, light and sound sensors, and a camera to collect data on patients and their environment. We analyzed collected data to detect and recognize patient’s face, their postures, facial action units and expressions, head pose variation, extremity movements, sound pressure levels, light intensity level, and visitation frequency. We found that facial expressions, functional status entailing extremity movement and postures, and environmental factors including the visitation frequency, light and sound pressure levels at night were significantly different between the delirious and non-delirious patients. Our results showed that granular and autonomous monitoring of critically ill patients and their environment is feasible using a noninvasive system, and we demonstrated its potential for characterizing critical care patients and environmental factors.

Attention can be attracted reflexively by sensory signals, biased by learning or reward, or focused voluntarily based on momentary goals. When voluntary attention is focused by purely internal decision processes (will), rather than instructions via external cues, we call this "willed attention." In prior work, we reported ERP and fMRI correlates of willed spatial attention in trial-by-trial cuing tasks. Here we further investigated the oscillatory mechanisms of willed attention by contrasting the event-related EEG spectrogram between instructional and choice cues. Two experiments were conducted at 2 different sites using the same visuospatial attention paradigm. Consistent between the 2 experiments, we found increases in frontal theta power (starting at ~500 ms post cue) for willed attention relative to instructed attention. This frontal theta increase was accompanied by increased frontal-parietal theta-band coherence and bidirectional Granger causality. Additionally, the onset of attention-related posterior alpha power lateralization was delayed in willed attention relative to instructed attention, and the amount of delay was related to the timing of frontal theta increase. These results, replicated across 2 experiments, suggest that theta oscillations are the neuronal signals indexing decision-making in the frontal cortex, and mediating reciprocal communications between the frontal executive and parietal attentional control regions during willed attention.

In cognitive psychology, automatic and selfreinforcing irrational thought patterns are known as cognitive distortions. Left unchecked, patients exhibiting these types of thoughts can become stuck in negative feedback loops of unhealthy thinking, leading to inaccurate perceptions of reality commonly associated with anxiety and depression. In this paper, we present a machine learning framework for the automatic detection and classification of 15 common cognitive distortions in two novel mental health free text datasets collected from both crowdsourcing and a real-world online therapy program. When differentiating between distorted and non-distorted passages, our model achieved a weighted F1 score of 0.88. For classifying distorted passages into one of 15 distortion categories, our model yielded weighted F1 scores of 0.68 in the larger crowdsourced dataset and 0.45 in the smaller online counseling dataset, both of which outperformed random baseline metrics by a large margin. For both tasks, we also identified the most discriminative words and phrases between classes to highlight common thematic elements for improving targeted and therapist-guided mental health treatment. Furthermore, we performed an exploratory analysis using unsupervised content-based clustering and topic modeling algorithms as first efforts towards a data-driven perspective on the thematic relationship between similar cognitive distortions traditionally deemed unique. Finally, we highlight the difficulties in applying mental health-based machine learning in a real-world setting and comment on the implications and benefits of our framework for improving automated delivery of therapeutic treatment in conjunction with traditional cognitive-behavioral therapy.

Patients staying in the Intensive Care Unit (ICU) have a severely disrupted circadian rhythm. Due to patients’ critical medical condition, ICU physicians and nurses have to provide round-the-clock clinical care, further disrupting patients’ circadian rhythm. Mistimed family visits during rest-time can also disrupt patients’ circadian rhythm. Currently, such effects are only reported based on hospital visitation policies rather than the actual number of visitors and care providers in the room. To quantify visitation disruptions, we used a deep Mask R-CNN model, a deep learning framework for object instance segmentation to detect and quantify the number of individuals in the ICU unit. This study represents the first effort to automatically quantify visitations in an ICU room, which could have implications in terms of policy adjustment, as well as circadian rhythm investigation. Our model achieved precision of 0.97 and recall of 0.67, with F1 score of 0.79 for detecting disruptions in the ICU units.