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		题名	作者	年代	出处	被引量
	1	Laparoscopic cholecystectomy and the umbilicus显示文摘	Nassar AH Ashkar KA Rashed AA	1997	British Journal of Surgery1997，，05：	1
	2	Prevalence of Candida colonization in preterm newborns and VLBW in neonatal intensive care unit:role of maternal colonization as a risk factor in transmission of disease显示文摘	Ali GY Algohery EH Rashed KA	2012	Journal of MaternalFetal and Neonatal Medicine2012，25，6：	1
	3	Laparoscopic cholecystectomy and the umbilicus显示文摘	Nassar AH Ashkar KA Rashed AA	1997	Br J Surg1997，84，5：	1
	4	Laparoscopic cholecystectomy andthe umbilicus显示文摘	Nassar AHM Ashkar KA Rashed AA	1997	Br J Surg1997，84，6：	1
	5	Laparoscopic cholecystectomy and the umbilicus显示文摘	Nassar AH Ashkar KA Rashed AA	1997	Br J Surg1997，84，5：	1
	6	Penalty function approach in heuristic algorithms for constrained redundancy reliability optimization 显示文摘	MANJU AGARWAL RASH	2005	IEEE Transaction on Reliability2005，54，3：	1
	7	Laparoscopic cholecystectomy and the umbilicus显示文摘	Nassar AH Ashkar KA Rashed AA		0，，：	1
	8	Laparoscopic chole-eysteetomy and umbilicus显示文摘	Nassar AH Ashkar KA Rashed AA		0，，：	1
	9	Laparoscopic cholecystectomy and the umbilicus 显示文摘	NASSAR AHM ASHKAR KA RASHED AA	1997	Br J Surg1997，84，：	1
	10	A machine learning model for colorectal liver metastasis post-hepatectomy prognostications显示文摘Background:Currently,surgical resection is the mainstay for colorectal liver metastases(CRLM)management and the only potentially curative treatment modality.Prognostication tools can support patient selection for surgical resection to maximize therapeutic benefit.This study aimed to develop a survival prediction model using machine learning based on a multicenter patient sample in Hong Kong.Methods:Patients who underwent hepatectomy for CRLM between 1 January 2009 and 31 December 2018 in four hospitals in Hong Kong were included in the study.Survival analysis was performed using Cox proportional hazards(CPH).A stepwise selection on Cox multivariable models with Least Absolute Shrinkage and Selection Operator(LASSO)regression was applied to a multiply-imputed dataset to build a prediction model.The model was validated in the validation set,and its performance was compared with that of Fong Clinical Risk Score(CRS)using concordance index.Results:A total of 572 patients were included with a median follow-up of 3.6 years.The full models for overall survival(OS)and recurrence-free survival(RFS)consist of the same 8 established and novel variables,namely colorectal cancer nodal stage,CRLM neoadjuvant treatment,Charlson Comorbidity Score,pre-hepatectomy bilirubin and carcinoembryonic antigen(CEA)levels,CRLM largest tumor diameter,extrahepatic metastasis detected on positron emission-tomography(PET)-scan as well as KRAS status.Our CRLM Machine-learning Algorithm Prognostication model(CMAP)demonstrated better ability to predict OS(C-index=0.651),compared with the Fong CRS for 1-year(C-index=0.571)and 5-year OS(C-index=0.574).It also achieved a C-index of 0.651 for RFS.Conclusions:We present a promising machine learning algorithm to individualize prognostications for patients following resection of CRLM with good discriminative ability.	Cynthia Sin Nga Lam Alina Ashok Bharwani Evelyn Hui Yi Chan Vernice Hui Yan Chan Howard Lai Ho Au Margaret Kay Ho Shireen Rashed Bernard Ming Hong Kwong Wentao Fang Ka Wing Ma Chung Mau Lo Tan To Cheung	2023	Hepatobiliary Surgery and Nutrition2023，12，4：	0

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