Automated Identification of Patients with Advanced Illness.

Early identification of advanced illness patients within an inpatient population is essential in order to establish the patient's goals of care. Having goals of care conversations enables hospital patients to dictate a plan for care in concordance with their values and wishes. These conversations allow a patient to maintain some control, rather than be subjected to a default care process that may not be desired and may not provide benefit.

Let Sleeping Patients Lie, avoiding unnecessary overnight vitals monitoring using a clinically based deep-learning model.

Impaired sleep for hospital patients is an all too common reality. Sleep disruptions due to unnecessary overnight vital sign monitoring are associated with delirium, cognitive impairment, weakened immunity, hypertension, increased stress, and mortality. It is also one of the most common complaints of hospital patients while imposing additional burdens on healthcare providers.

An approach to predicting patient experience through machine learning and social network analysis.

Objective: Improving the patient experience has become an essential component of any healthcare system's performance metrics portfolio. In this study, we developed a machine learning model to predict a patient's response to the Hospital Consumer Assessment of Healthcare Providers and Systems survey's "Doctor Communications" domain questions while simultaneously identifying most impactful providers in a network.

Materials And Methods: This is an observational study of patients admitted to a single tertiary care hospital between 2016 and 2020.

Determining the role of correlated firing in large populations of neurons using white noise and natural scene stimuli.

The role of correlated firing in representing information has been a subject of much discussion. Several studies in retina, visual cortex, somatosensory cortex, and motor cortex, have suggested that it plays only a minor role, carrying <10% of the total information carried by the neurons (Gawne & Richmond, 1993; Nirenberg et al., 2001; Oram et al.

Symmetry, probability, and recognition in face space.

The essential midline symmetry of human faces is shown to play a key role in facial coding and recognition. This also has deep and important connections with recent explorations of the organization of primate cortex, as well as human psychophysical experiments. Evidence is presented that the dimension of face recognition space for human faces is dramatically lower than previous estimates.

On the dimensionality of face space.

The dimensionality of face space is measured objectively in a psychophysical study. Within this framework, we obtain a measurement of the dimension for the human visual system. Using an eigenface basis, evidence is presented that talented human observers are able to identify familiar faces that lie in a space of roughly 100 dimensions and the average observer requires a space of between 100 and 200 dimensions.