Biomedical semantic text summarizer.

BACKGROUND: Text summarization is a challenging problem in Natural Language Processing, which involves condensing the content of textual documents without losing their overall meaning and information content, In the domain of bio-medical research, summaries are critical for efficient data analysis and information retrieval. While several bio-medical text summarizers exist in the literature, they often miss out on an essential text aspect: text semantics. RESULTS: This paper proposes a novel extractive summarizer that preserves text semantics by utilizing bio-semantic models. We evaluate our approach using ROUGE on a standard dataset and compare it with three state-of-the-art summarizers. Our results show that our approach outperforms existing summarizers. CONCLUSION: The usage of semantics can improve summarizer performance and lead to better summaries. Our summarizer has the potential to aid in efficient data analysis and information retrieval in the field of biomedical research.

Can ACS-NSQIP score be used to predict postoperative mortality in Saudi population?

Background: Various scoring systems help in classifying the patient's risk preoperatively and hence to decide the best available treatment option. ACS-NSQIP score has been introduced in clinical practice for few years. This study was done to find out whether there is any difference between predicted mortality from ACS-NSQIP score and observed mortality in Saudi population. Methods: This prospective observational study was conducted at Security Forces Hospital, Riyadh, Kingdom of Saudi Arabia. We included patients undergoing elective and emergency surgical procedures in our hospital. Thirty days mortality data was collected and then observed to expected (O/E) mortality ratio was calculated. The sample size for our study was nine hundred and three (903) patients. Results: The mean ACS-NSQIP mortality risk score (%) for the study was 0.49. Expected number of mortalities was 4.42 while observed mortalities were 11, yielding an O/E ratio of 2.48 (p-value 0.000). We did not find a significant difference between expected and observed mortalities except for ASA class 3 and 4 patients where expected numbers of mortalities were lower than observed (p-value < 0.05). Conclusion: ACS-NSQIP can be reliably used for postoperative mortality prediction especially in lower risk groups.

Is the SORT score reliable in predicting postoperative 30-day mortality after a nonemergency surgery in Saudi population?

CONTEXT: The process of stratifying patient risk preoperatively helps in the decision about the best-possible postoperative care for patients. There have been many scoring systems that are used in anesthesia practice. AIMS: To find out whether there is any difference between the mortality predicted from SORT scoring and the observed mortality among Saudi patients. SETTINGS AND DESIGN: This was a prospective, observational study in which we included patients underoing nonemergency surgical procedures at the Security Forces Hospital, Riyadh. METHODS AND MATERIAL: We calculated the SORT scores for all the included patients. We then collected the 30-day mortality data of all the patients having nonemergency surgical procedures. STATISTICAL ANALYSIS USED: We calculated the expected mortality ratio. A P value of less than 0.05 was considered significant. RESULTS: The mean SORT mortality risk score (%) for the whole sample was 0.30. The expected number of deaths was 1.638 while the observed deaths were 2, which yields an O/E ratio of 0.819 (p-value: 0.006). The O/E mortality ratios for patients in each individual ASA class were found to be statistically insignificant which means that SORT score can reliably predict mortality for each ASA class. CONCLUSIONS: SORT scores can be used to predict 30-day mortality after nonemergency surgeries in Saudi population.