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786 results on '"Ma, Yuxia"'

1. Using Machine Learning Technologies in Pressure Injury Management: Systematic Review

Author

Jiang, Mengyao, Ma, Yuxia, Guo, Siyi, Jin, Liuqi, Lv, Lin, Han, Lin, and An, Ning

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

BackgroundPressure injury (PI) is a common and preventable problem, yet it is a challenge for at least two reasons. First, the nurse shortage is a worldwide phenomenon. Second, the majority of nurses have insufficient PI-related knowledge. Machine learning (ML) technologies can contribute to lessening the burden on medical staff by improving the prognosis and diagnostic accuracy of PI. To the best of our knowledge, there is no existing systematic review that evaluates how the current ML technologies are being used in PI management. ObjectiveThe objective of this review was to synthesize and evaluate the literature regarding the use of ML technologies in PI management, and identify their strengths and weaknesses, as well as to identify improvement opportunities for future research and practice. MethodsWe conducted an extensive search on PubMed, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, China National Knowledge Infrastructure (CNKI), the Wanfang database, the VIP database, and the China Biomedical Literature Database (CBM) to identify relevant articles. Searches were performed in June 2020. Two independent investigators conducted study selection, data extraction, and quality appraisal. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). ResultsA total of 32 articles met the inclusion criteria. Twelve of those articles (38%) reported using ML technologies to develop predictive models to identify risk factors, 11 (34%) reported using them in posture detection and recognition, and 9 (28%) reported using them in image analysis for tissue classification and measurement of PI wounds. These articles presented various algorithms and measured outcomes. The overall risk of bias was judged as high. ConclusionsThere is an array of emerging ML technologies being used in PI management, and their results in the laboratory show great promise. Future research should apply these technologies on a large scale with clinical data to further verify and improve their effectiveness, as well as to improve the methodological quality.

Published
2021
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6. The Accuracy of Screening for Post-stroke Cognitive Impairment Assessment Tools: a Meta-analysis

Author

MA Yuxia, YANG Yiyi, WEI Xiaoqin, CHEN Yanru, QIN Jiangxia, YUAN Yue, CHEN Yajing, WU Yinping, HAN Lin

Subjects
post-stroke cognitive impairment, screening tools, meta-analysis, diagnostic test, Medicine
Abstract

Background Post-stroke cognitive impairment (PSCI) brings a heavy burden to patients and their families. An early recognition and intervention can help delay the occurrence and development of PSCI. Therefore, the use of accurate neuropsychological assessment tools to screen for PSCI is essential for the management and treatment of PSCI. Objective To analyze the screening accuracy of assessment tools for PSCI by meta-analysis, thus providing references for an accurate screening of PSCI. Methods Diagnostic trials on screening tools of PSCI published from the establishment of the database to December 2022 were searched in CNKI, VIP, Wanfang Data, SinoMed, PubMed, Embase, Web of Science, Cochrane Library. Two researchers respectively screened literatures, extracted data, and assessed the risk of bias. Stata 17.0 software was used to analyze the data. Results A total of 57 articles were included, involving 7 assessment tools [the National Institute of Neurological Disorders and Stroke-Canadian Stroke Network 5-Minute Battery (NINDS-CSN 5-Minutes), the Montreal Cognitive Assessment (MoCA), the Mini-Mental State Examination (MMSE), the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE), the Addenbrooke's Cognitive Examination-Revised (ACE-R), the Telephone Interview for Cognitive Status Modified (TICS-m) and the Montreal Cognitive Assessment 5-minute protocol (MoCA-5 min) ] to screen 12 113 patients. Meta-analysis results showed that the combined sensitivity and specificity of MoCA in screening PSCI were 0.84 (95%CI=0.80-0.87) and 0.74 (95%CI=0.67-0.80), respectively, with a combined area under the curve (AUC) of 0.87 (95%CI=0.84-0.90). The combined sensitivity and specificity of MMSE in screening PSCI were 0.73 (95%CI=0.67-0.79) and 0.76 (95%CI=0.69-0.82), respectively, with a combined AUC of 0.81 (95%CI=0.77-0.84). The combined sensitivity and specificity of IQCODE in screening PSCI were 0.73 (95%CI=0.48-0.89) and 0.95 (95%CI=0.75-0.99), respectively, with a combined AUC of 0.91 (95%CI=0.88-0.93). The combined sensitivity and specificity of the NINDS-CSN 5-min in screening PSCI were 0.83 (95%CI=0.78-0.87) and 0.69 (95%CI=0.60-0.76), respectively, with a combined AUC of 0.85 (95%CI=0.81-0.88). The combined sensitivity and specificity of the ACE-R in screening PSCI were 0.90 (95%CI=0.80-0.95) and 0.61 (95%CI=0.19-0.91), respectively, with a combined AUC of 0.90 (95%CI=0.87-0.92). The combined sensitivity and specificity of TICS-m in screening PSCI were 0.84 (95%CI=0.75-0.91) and 0.67 (95%CI=0.61-0.74), respectively, with a combined AUC of 0.66 (95%CI=0.60-0.71) . Conclusion The combined AUC of IQCODE and ACE-R is larger, and the former as a higher combined specificity and the latter has a higher combined sensitivity. Therefore, IQCODE and ACE-R are optimal assessment tools to accurately screen PSCI. Due to the limited number of literatures reporting the IQCODE and ACE-R in screening PSCI, our conclusions still need to be validated by multicenter and large-sample studies.

Published
2024
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