[Keyword Network Analysis and Topic Modeling of News Articles Related to Artificial Intelligence and Nursing].

PURPOSE: The purpose of this study was to identify the main keywords, network properties, and main topics of news articles related to artificial intelligence technology in the field of nursing. METHODS: After collecting artificial intelligence-and nursing-related news articles published between January 1, 1991, and July 24, 2022, keywords were extracted via preprocessing. A total of 3,267 articles were searched, and 2,996 were used for the final analysis. Text network analysis and topic modeling were performed using NetMiner 4.4. RESULTS: As a result of analyzing the frequency of appearance, the keywords used most frequently were education, medical robot, telecom, dementia, and the older adults living alone. Keyword network analysis revealed the following results: a density of 0.002, an average degree of 8.79, and an average distance of 2.43; the central keywords identified were 'education,' 'medical robot,' and 'fourth industry.' Five topics were derived from news articles related to artificial intelligence and nursing: 'Artificial intelligence nursing research and development in the health and medical field,' 'Education using artificial intelligence for children and youth care,' 'Nursing robot for older adults care,' 'Community care policy and artificial intelligence,' and 'Smart care technology in an aging society.' CONCLUSION: The use of artificial intelligence may be helpful among the local community, older adult, children, and adolescents. In particular, health management using artificial intelligence is indispensable now that we are facing a super-aging society. In the future, studies on nursing intervention and development of nursing programs using artificial intelligence should be conducted.

Masking of the mouth area impairs reconstruction of acoustic speech features and higher-level segmentational features in the presence of a distractor speaker.

Multisensory integration enables stimulus representation even when the sensory input in a single modality is weak. In the context of speech, when confronted with a degraded acoustic signal, congruent visual inputs promote comprehension. When this input is masked, speech comprehension consequently becomes more difficult. But it still remains inconclusive which levels of speech processing are affected under which circumstances by occluding the mouth area. To answer this question, we conducted an audiovisual (AV) multi-speaker experiment using naturalistic speech. In half of the trials, the target speaker wore a (surgical) face mask, while we measured the brain activity of normal hearing participants via magnetoencephalography (MEG). We additionally added a distractor speaker in half of the trials in order to create an ecologically difficult listening situation. A decoding model on the clear AV speech was trained and used to reconstruct crucial speech features in each condition. We found significant main effects of face masks on the reconstruction of acoustic features, such as the speech envelope and spectral speech features (i.e. pitch and formant frequencies), while reconstruction of higher level features of speech segmentation (phoneme and word onsets) were especially impaired through masks in difficult listening situations. As we used surgical face masks in our study, which only show mild effects on speech acoustics, we interpret our findings as the result of the missing visual input. Our findings extend previous behavioural results, by demonstrating the complex contextual effects of occluding relevant visual information on speech processing.