A recurrent machine learning model predicts intracranial hypertension in neurointensive care patients.

The evolution of intracranial pressure (ICP) of critically ill patients admitted to a neurointensive care unit (ICU) is difficult to predict. Besides the underlying disease and compromised intracranial space, ICP is affected by a multitude of factors, many of which are monitored on the ICU, but the complexity of the resulting patterns limits their clinical use. This paves the way for new machine learning techniques to assist clinical management of patients undergoing invasive ICP monitoring independent of the underlying disease. An institutional cohort (ICP-ICU) of patients with invasive ICP monitoring (n = 1346) was used to train recurrent machine learning models to predict the occurrence of ICP increases of ≥22 mmHg over a long (>2 h) time period in the upcoming hours. External validation was performed on patients undergoing invasive ICP measurement in two publicly available datasets [Medical Information Mart for Intensive Care (MIMIC, n = 998) and eICU Collaborative Research Database (n = 1634)]. Different distances (1-24 h) between prediction time point and upcoming critical phase were evaluated, demonstrating a decrease in performance but still robust AUC-ROC with larger distances (24 h AUC-ROC: ICP-ICU 0.826 ± 0.0071, MIMIC 0.836 ± 0.0063, eICU 0.779 ± 0.0046, 1 h AUC-ROC: ICP-ICU 0.982 ± 0.0008, MIMIC 0.965 ± 0.0010, eICU 0.941 ± 0.0025). The model operates on sparse hourly data and is stable in handling variable input lengths and missingness through its nature of recurrence and internal memory. Calculation of gradient-based feature importance revealed individual underlying decisions for our long short time memory-based model and thereby provided improved clinical interpretability. Recurrent machine learning models have the potential to be an effective tool for the prediction of ICP increases with high translational potential.

The Embodied Crossmodal Self Forms Language and Interaction: A Computational Cognitive Review.

Human language is inherently embodied and grounded in sensorimotor representations of the self and the world around it. This suggests that the body schema and ideomotor action-effect associations play an important role in language understanding, language generation, and verbal/physical interaction with others. There are computational models that focus purely on non-verbal interaction between humans and robots, and there are computational models for dialog systems that focus only on verbal interaction. However, there is a lack of research that integrates these approaches. We hypothesize that the development of computational models of the self is very appropriate for considering joint verbal and physical interaction. Therefore, they provide the substantial potential to foster the psychological and cognitive understanding of language grounding, and they have significant potential to improve human-robot interaction methods and applications. This review is a first step toward developing models of the self that integrate verbal and non-verbal communication. To this end, we first analyze the relevant findings and mechanisms for language grounding in the psychological and cognitive literature on ideomotor theory. Second, we identify the existing computational methods that implement physical decision-making and verbal interaction. As a result, we outline how the current computational methods can be used to create advanced computational interaction models that integrate language grounding with body schemas and self-representations.

Proximal femur fracture in older patients--rehabilitation and clinical outcome.

BACKGROUND: hip fracture is a major cause of morbidity and mortality in older people; optimal post-surgical treatment is a matter of controversy. OBJECTIVE: to examine the effects of rehabilitation on the clinical outcome following surgical treatment of hip fracture. DESIGN: prospective longitudinal study in three groups of patients with different post-surgical care. METHODS: initial screening of 283 elderly patients with proximal femur fracture; documentation of medical and social history and clinical data; geriatric assessments (Activities of Daily Living and Instrumental Activities of Daily Living) during hospital stay and follow-up for 12 months. One hundred and forty-five patients (>or=65 years) of normal mental status were eligible for the study. Successful follow-up could be monitored in 120 and 117 patients for 6 and 12 months, respectively. Sixty-nine and 39 patients underwent supervised inpatient rehabilitation in an orthopaedic or geriatric hospital, respectively (intervention groups A and B, respectively) whereas 34 patients received no special rehabilitation as they were directly discharged home (control group C). RESULTS: initially a fall-/surgical-induced reduction (P<0.001) of the main outcome measure (Activities of Daily Living) was observed in all patients. Within 6 months of rehabilitation there was an improvement (P<0.01) in Activities of Daily Living; however the pre-fracture scores were not reached. The same time pattern was seen in group C. Therefore no significant differences between the three groups of patients in approaching the baseline status was visible. Moreover, the one-year total mortality in the studied population with normal mental status averaged 11.7% and did not differ between the three groups. CONCLUSION: based on our measured outcome variables institutional rehabilitation after surgical treatment of hip fracture apparently had no significant impact on mortality and morbidity in older patients of normal mental status.