Artificial intelligence prediction of cholecystectomy operative course from automated identification of gallbladder inflammation

Author(s):  
Thomas M. Ward ◽  
Daniel A. Hashimoto ◽  
Yutong Ban ◽  
Guy Rosman ◽  
Ozanan R. Meireles
2019 ◽  
Vol 46 (9) ◽  
pp. 4046-4056 ◽  
Author(s):  
Serafeim Moustakidis ◽  
Murad Omar ◽  
Juan Aguirre ◽  
Pouyan Mohajerani ◽  
Vasilis Ntziachristos

2021 ◽  
Vol 36 (5) ◽  
pp. 918-923
Author(s):  
Rafael da Mata Santos ◽  
Higor Prado ◽  
Idalísio Neto ◽  
Guilherme Alves de Oliveira ◽  
Amaro Silva ◽  
...  

Author(s):  
David L. Poole ◽  
Alan K. Mackworth

Crisis ◽  
2016 ◽  
Vol 37 (2) ◽  
pp. 140-147 ◽  
Author(s):  
Michael J. Egnoto ◽  
Darrin J. Griffin

Abstract. Background: Identifying precursors that will aid in the discovery of individuals who may harm themselves or others has long been a focus of scholarly research. Aim: This work set out to determine if it is possible to use the legacy tokens of active shooters and notes left from individuals who completed suicide to uncover signals that foreshadow their behavior. Method: A total of 25 suicide notes and 21 legacy tokens were compared with a sample of over 20,000 student writings for a preliminary computer-assisted text analysis to determine what differences can be coded with existing computer software to better identify students who may commit self-harm or harm to others. Results: The results support that text analysis techniques with the Linguistic Inquiry and Word Count (LIWC) tool are effective for identifying suicidal or homicidal writings as distinct from each other and from a variety of student writings in an automated fashion. Conclusion: Findings indicate support for automated identification of writings that were associated with harm to self, harm to others, and various other student writing products. This work begins to uncover the viability or larger scale, low cost methods of automatic detection for individuals suffering from harmful ideation.


Sign in / Sign up

Export Citation Format

Share Document