1
|
Deng J, Chen Y, Zhang X, Zhou Y, Xiong B. Intelligent supervision of PIVAS drug dispensing based on image recognition technology. PLoS One 2024; 19:e0298109. [PMID: 38573999 PMCID: PMC10994394 DOI: 10.1371/journal.pone.0298109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 01/13/2024] [Indexed: 04/06/2024] Open
Abstract
Pharmacy Intravenous Admixture Services (PIVAS) are places dedicated to the centralized dispensing of intravenous drugs, usually managed and operated by professional pharmacists and pharmacy technicians, and are an integral part of modern healthcare. However, the workflow of PIVAS has some problems, such as low efficiency and error-prone. This study aims to improve the efficiency of drug dispensing, reduce the rate of manual misjudgment, and minimize drug errors by conducting an in-depth study of the entire workflow of PIVAS and applying image recognition technology to the drug checking and dispensing process. Firstly, through experimental comparison, a target detection model suitable for drug category recognition is selected in the drug-checking process of PIVAS, and it is improved to improve the recognition accuracy and speed of intravenous drug categories. Secondly, a corner detection model for drug dosage recognition was studied in the drug dispensing stage to further increase drug dispensing accuracy. Then the PIVAS drug category recognition system and PIVAS drug dosage recognition system were designed and implemented.
Collapse
Affiliation(s)
- Jianzhi Deng
- College of Earth Sciences, Guilin University of Technology, Guilin, China
- College of Physics and Electronic Information Engineering, Guilin University of Technology, Guilin, China
| | - Ying Chen
- College of Physics and Electronic Information Engineering, Guilin University of Technology, Guilin, China
| | - Xiaoyu Zhang
- College of Physics and Electronic Information Engineering, Guilin University of Technology, Guilin, China
| | - Yuehan Zhou
- Department of Clinical Pharmacy, Guilin Medical University, Guilin, China
| | - Bin Xiong
- College of Earth Sciences, Guilin University of Technology, Guilin, China
| |
Collapse
|
2
|
Wang Z, Hu C, Liu W, Zhou X, Zhao X. EEG-based high-performance depression state recognition. Front Neurosci 2024; 17:1301214. [PMID: 38371369 PMCID: PMC10871719 DOI: 10.3389/fnins.2023.1301214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 12/14/2023] [Indexed: 02/20/2024] Open
Abstract
Depression is a global disease that is harmful to people. Traditional identification methods based on various scales are not objective and accurate enough. Electroencephalogram (EEG) contains abundant physiological information, which makes it a new research direction to identify depression state. However, most EEG-based algorithms only extract the original EEG features and ignore the complex spatiotemporal information interactions, which will reduce performance. Thus, a more accurate and objective method for depression identification is urgently needed. In this work, we propose a novel depression identification model: W-GCN-GRU. In our proposed method, we censored six sensitive features based on Spearman's rank correlation coefficient and assigned different weight coefficients to each sensitive feature by AUC for the weighted fusion of sensitive features. In particular, we use the GCN and GRU cascade networks based on weighted sensitive features as depression recognition models. For the GCN, we creatively took the brain function network based on the correlation coefficient matrix as the adjacency matrix input and the weighted fused sensitive features were used as the node feature matrix input. Our proposed model performed well on our self-collected dataset and the MODMA datasets with a accuracy of 94.72%, outperforming other methods. Our findings showed that feature dimensionality reduction, weighted fusion, and EEG spatial information all had great effects on depression recognition.
Collapse
Affiliation(s)
- Zhuozheng Wang
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Chenyang Hu
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Wei Liu
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Xiaofan Zhou
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Xixi Zhao
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| |
Collapse
|
3
|
Jellinger KA. Pathobiology of Cognitive Impairment in Parkinson Disease: Challenges and Outlooks. Int J Mol Sci 2023; 25:498. [PMID: 38203667 PMCID: PMC10778722 DOI: 10.3390/ijms25010498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 12/11/2023] [Accepted: 12/27/2023] [Indexed: 01/12/2024] Open
Abstract
Cognitive impairment (CI) is a characteristic non-motor feature of Parkinson disease (PD) that poses a severe burden on the patients and caregivers, yet relatively little is known about its pathobiology. Cognitive deficits are evident throughout the course of PD, with around 25% of subtle cognitive decline and mild CI (MCI) at the time of diagnosis and up to 83% of patients developing dementia after 20 years. The heterogeneity of cognitive phenotypes suggests that a common neuropathological process, characterized by progressive degeneration of the dopaminergic striatonigral system and of many other neuronal systems, results not only in structural deficits but also extensive changes of functional neuronal network activities and neurotransmitter dysfunctions. Modern neuroimaging studies revealed multilocular cortical and subcortical atrophies and alterations in intrinsic neuronal connectivities. The decreased functional connectivity (FC) of the default mode network (DMN) in the bilateral prefrontal cortex is affected already before the development of clinical CI and in the absence of structural changes. Longitudinal cognitive decline is associated with frontostriatal and limbic affections, white matter microlesions and changes between multiple functional neuronal networks, including thalamo-insular, frontoparietal and attention networks, the cholinergic forebrain and the noradrenergic system. Superimposed Alzheimer-related (and other concomitant) pathologies due to interactions between α-synuclein, tau-protein and β-amyloid contribute to dementia pathogenesis in both PD and dementia with Lewy bodies (DLB). To further elucidate the interaction of the pathomechanisms responsible for CI in PD, well-designed longitudinal clinico-pathological studies are warranted that are supported by fluid and sophisticated imaging biomarkers as a basis for better early diagnosis and future disease-modifying therapies.
Collapse
Affiliation(s)
- Kurt A Jellinger
- Institute of Clinical Neurobiology, Alberichgasse 5/13, A-1150 Vienna, Austria
| |
Collapse
|
4
|
Lian H, Lu C, Li S, Zhao Y, Tang C, Zong Y. A Survey of Deep Learning-Based Multimodal Emotion Recognition: Speech, Text, and Face. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1440. [PMID: 37895561 PMCID: PMC10606253 DOI: 10.3390/e25101440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 09/20/2023] [Accepted: 10/09/2023] [Indexed: 10/29/2023]
Abstract
Multimodal emotion recognition (MER) refers to the identification and understanding of human emotional states by combining different signals, including-but not limited to-text, speech, and face cues. MER plays a crucial role in the human-computer interaction (HCI) domain. With the recent progression of deep learning technologies and the increasing availability of multimodal datasets, the MER domain has witnessed considerable development, resulting in numerous significant research breakthroughs. However, a conspicuous absence of thorough and focused reviews on these deep learning-based MER achievements is observed. This survey aims to bridge this gap by providing a comprehensive overview of the recent advancements in MER based on deep learning. For an orderly exposition, this paper first outlines a meticulous analysis of the current multimodal datasets, emphasizing their advantages and constraints. Subsequently, we thoroughly scrutinize diverse methods for multimodal emotional feature extraction, highlighting the merits and demerits of each method. Moreover, we perform an exhaustive analysis of various MER algorithms, with particular focus on the model-agnostic fusion methods (including early fusion, late fusion, and hybrid fusion) and fusion based on intermediate layers of deep models (encompassing simple concatenation fusion, utterance-level interaction fusion, and fine-grained interaction fusion). We assess the strengths and weaknesses of these fusion strategies, providing guidance to researchers to help them select the most suitable techniques for their studies. In summary, this survey aims to provide a thorough and insightful review of the field of deep learning-based MER. It is intended as a valuable guide to aid researchers in furthering the evolution of this dynamic and impactful field.
Collapse
Affiliation(s)
- Hailun Lian
- Key Laboratory of Child Development and Learning Science (Ministry of Education), Southeast University, Nanjing 210000, China; (H.L.); (C.L.); (S.L.); (Y.Z.); (C.T.)
- School of Information Science and Engineering, Southeast University, Nanjing 210000, China
| | - Cheng Lu
- Key Laboratory of Child Development and Learning Science (Ministry of Education), Southeast University, Nanjing 210000, China; (H.L.); (C.L.); (S.L.); (Y.Z.); (C.T.)
- School of Biological Science and Medical Engineering, Southeast University, Nanjing 210000, China
| | - Sunan Li
- Key Laboratory of Child Development and Learning Science (Ministry of Education), Southeast University, Nanjing 210000, China; (H.L.); (C.L.); (S.L.); (Y.Z.); (C.T.)
- School of Information Science and Engineering, Southeast University, Nanjing 210000, China
| | - Yan Zhao
- Key Laboratory of Child Development and Learning Science (Ministry of Education), Southeast University, Nanjing 210000, China; (H.L.); (C.L.); (S.L.); (Y.Z.); (C.T.)
- School of Information Science and Engineering, Southeast University, Nanjing 210000, China
| | - Chuangao Tang
- Key Laboratory of Child Development and Learning Science (Ministry of Education), Southeast University, Nanjing 210000, China; (H.L.); (C.L.); (S.L.); (Y.Z.); (C.T.)
- School of Biological Science and Medical Engineering, Southeast University, Nanjing 210000, China
| | - Yuan Zong
- Key Laboratory of Child Development and Learning Science (Ministry of Education), Southeast University, Nanjing 210000, China; (H.L.); (C.L.); (S.L.); (Y.Z.); (C.T.)
- School of Biological Science and Medical Engineering, Southeast University, Nanjing 210000, China
| |
Collapse
|