Evaluation on knowledge extraction and machine learning in resolving Malay word ambiguity

The involvement of linguistic professionals in resolving the ambiguity of a word within a particular context will produce a concise meaning of the words that are found in the lexical knowledge based collection. Motivated from that issue, we employed lexical knowledge and machine learning approach which includes the integration of data or/and information from the lexical knowledge based, that is Malay collections which linked to the ambiguous words. We used the most open class word and removed the stop words from the targeted sentences. Experiments have been conducted with and without lexical knowledge on 50 ambiguous words. The Word Sense Disambiguation (WSD) method is determined by machine learning, corpus based approaches namely Malay-Malay corpus and English-Malay corpus. The results show that the proposed method has improved the precision in resolving ambiguity.Keywords: ambiguity; lexical knowledge; machine learning; Malay wor

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries