Simulated biologic intelligence used to predict length of stay and survival of burns.
From July 13, 1988, to May 14, 1995, 1585 patients with burns and no other injuries besides inhalation were treated; 4.5% did not survive. Artificial neural networks were trained on patient presentation data with known outcomes on 90% of the randomized cases. The remaining cases were then used to predict survival and length of stay in cases not trained on. Survival was predicted with more than 98% accuracy and length of stay to within a week with 72% accuracy in these cases. For anatomic area involved by burn, burns involving the feet, scalp, or both had the largest negative effect on the survival prediction. In survivors burns involving the buttocks, transport to this burn center by the military or by helicopter, electrical burns, hot tar burns, and inhalation were associated with increasing the length of stay prediction. Neural networks can be used to accurately predict the clinical outcome of a burn. What factors affect that prediction can be investigated.
Frye
K E
KE
University of South Alabama Medical Center, USA.
Izenberg
S D
SD
Williams
M D
MD
Luterman
A
A
D001185:Artificial Intelligence; D002053:Burn Units / Q000706:statistics & numerical data*; D002056:Burns / Q000401:mortality* / Q000503:physiopathology; D005260:Female; D006801:Humans; D007902:Length of Stay; D008297:Male; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D015996:Survival Rate