This document summarizes a study that used artificial neural networks and genetic algorithms to predict mortality in critically ill patients. Researchers analyzed data from over 4,000 patients, identifying characteristics like demographics, physiology, and organ failure to train a neural network. An initial set of 20 randomly generated neural networks evolved over generations, with structure optimized by a genetic algorithm. The best-performing 7th generation network used 18 variables to predict mortality with comparable accuracy to logistic regression models. Receiver operating characteristic curves demonstrated the neural network and logistic regression tests' ability to identify patients at risk of death.

1. Medical Informatics Journal Club
12 January 1998
Frank W. Meissner, M.D., F.A.C.P., F.A.C.C., F.C.C.P.
Prediction of outcome in critically ill
patients using artificial neural network
synthesised by genetic algorithm.
Dybowski R, Weller P, Chang R, Grant V
Lancet; 347, 1146-1150, April 27 1996

2. Patient Population
●Guy’s Hospital London
●Data from 4484 Patients
●14502 daily records
○Avg hospital d’s = 3.82
●‘Randomized’ selection of 1Day record

3. ●Sepsis Syndrome Definition
○MBP < 70 mmHg
○Mean Temp > 38 degrees Centigrade
○Mean WBC < 4K or >12K
●Case definition = 50% mortality rate
Patient Population

4. Methodology
●463 records =Case definition
●258 randomly selected by random
number generator
●Training set = 168 records
○Mortality rate = 46%
●Test set = 90 records
○Mortality rate = 49%

5. Source of Patient Admit
ALL Meeting
Entry Criteria

6. Patient Demographics
ALL Meeting
Entry Criteria

7. Patient Physiology
ALL Meeting
Entry Criteria

8. Type of Organ Failure/Apache III Score
ALL Meeting
Entry Criteria

9. Neural Network Construction

10. ●CART
○157 Variables => 11 variables
●Logistic Regression Analysis
○157 variables => 9 variables
●Neural Net inputs
○CART + Logistic Regression Variables
○18 variables (2 variables in both sets)
Neural Network Construction

11. CART/Logisitic Regression Variables

12. Neural Net ‘Quasi-Darwinian’ Evolution
●20 randomly generated neural networks
●Structure of neural net
○One ‘chromosome’ with 3 ‘gene’ loci
○Loci = 5-bit binary number = # nodes in
intermediate layer of neural net
●Probability of gene passage = function of
performance
●Cross-over position = random event

13. 1 2 3 4
5
Conceptual Diagram of Genetic Algorithm

14. Encoding Structure of Hidden Layer- Neural Net
Chromosome
01000 00110 00011
=8 nodes =6 nodes =3 nodes
Inputs
Output
Hidden Layer

15. 7th Generation Neural Network
POST ARREST?
NONCARDIAC PULMONARY EDEMA?
CHRONIC CARDIOVASCULAR DZ?
CARDIOTHORACIC SURGERY?
# D’S WITH ARF
URINE OUTPUT
BUN
ARTERIAL pH
ADMIT DUE TO ACID/BASE DISTURBANCE?
PaCO2
AGE
RACE
PLANNED POSTOP MONITORING IN ICU?
ELECTIVE OPERATION?
GI INDICATOR FOR ADMIT?
NEURO INDICATOR FOR ADMIT?
# D’S AN ORGAN FAILURE OCCURRED
RESP FAILURE DURING DAY IN ICU?

16. ONE MINUTE UNIVERSITY: Stats 690
Reciever Operating Curves
A means of graphically demonstrating the ability of a
test to indicate the presence of the disease for which
the test tests
Area under the ROC = probability of the test
correctly identifying the presence of disease

17. True
Positive
Rate
ROC Curves For Neural Nets vs Logistic Regression

18. Critique of Paper