Abstract
Therapy decision is one of the most important tasks clinicians have to perform in their clinical practice. The decision process requires taking into account many different factors. The Authors have proposed a neural computing approach for supporting clinical decision analysis. The mathematical model of artificial neural network (ANN) has been applied on a pool of clinical information gathered through case description freely filled by senior psychiatrists into 416 clinical charts. Sertraline, as drug for treatment, has been chosen since its clinical uses range from treatment of depression to that of many other psychiatric clinical conditions so that it has been thought to be a good candidate to this type of study. The ANN performance in forecasting successful and unsuccessful treatment cases showed an overall accuracy of classification of 97.35%. This result suggests a possible future application of this method to obtain a reliable prediction of a given psychiatric patient outcome during a specific psychopharmacological therapy, optimising the decisional making process.
| Original language | English |
|---|---|
| Pages (from-to) | 239-248 |
| Number of pages | 10 |
| Journal | Artificial Intelligence in Medicine |
| Volume | 23 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2001 |
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Keywords
- ANN
- Psychiatry
- Sertraline
ASJC Scopus subject areas
- Artificial Intelligence
- Medicine(all)
Cite this
A neural network approach to the outcome definition on first treatment with sertraline in a psychiatric population. / Franchini, L.; Spagnolo, C.; Rossini, D.; Smeraldi, E.; Bellodi, L.; Politi, E.
In: Artificial Intelligence in Medicine, Vol. 23, No. 3, 2001, p. 239-248.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A neural network approach to the outcome definition on first treatment with sertraline in a psychiatric population
AU - Franchini, L.
AU - Spagnolo, C.
AU - Rossini, D.
AU - Smeraldi, E.
AU - Bellodi, L.
AU - Politi, E.
PY - 2001
Y1 - 2001
N2 - Therapy decision is one of the most important tasks clinicians have to perform in their clinical practice. The decision process requires taking into account many different factors. The Authors have proposed a neural computing approach for supporting clinical decision analysis. The mathematical model of artificial neural network (ANN) has been applied on a pool of clinical information gathered through case description freely filled by senior psychiatrists into 416 clinical charts. Sertraline, as drug for treatment, has been chosen since its clinical uses range from treatment of depression to that of many other psychiatric clinical conditions so that it has been thought to be a good candidate to this type of study. The ANN performance in forecasting successful and unsuccessful treatment cases showed an overall accuracy of classification of 97.35%. This result suggests a possible future application of this method to obtain a reliable prediction of a given psychiatric patient outcome during a specific psychopharmacological therapy, optimising the decisional making process.
AB - Therapy decision is one of the most important tasks clinicians have to perform in their clinical practice. The decision process requires taking into account many different factors. The Authors have proposed a neural computing approach for supporting clinical decision analysis. The mathematical model of artificial neural network (ANN) has been applied on a pool of clinical information gathered through case description freely filled by senior psychiatrists into 416 clinical charts. Sertraline, as drug for treatment, has been chosen since its clinical uses range from treatment of depression to that of many other psychiatric clinical conditions so that it has been thought to be a good candidate to this type of study. The ANN performance in forecasting successful and unsuccessful treatment cases showed an overall accuracy of classification of 97.35%. This result suggests a possible future application of this method to obtain a reliable prediction of a given psychiatric patient outcome during a specific psychopharmacological therapy, optimising the decisional making process.
KW - ANN
KW - Psychiatry
KW - Sertraline
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UR - http://www.scopus.com/inward/citedby.url?scp=0034773012&partnerID=8YFLogxK
U2 - 10.1016/S0933-3657(01)00088-4
DO - 10.1016/S0933-3657(01)00088-4
M3 - Article
C2 - 11704439
AN - SCOPUS:0034773012
VL - 23
SP - 239
EP - 248
JO - Artificial Intelligence in Medicine
JF - Artificial Intelligence in Medicine
SN - 0933-3657
IS - 3
ER -