Artificial Neural Network assessment of substitutive pharmacological treatments in hospitalized intravenous drug users

Artificial neural networks (ANNs) provide better solutions than linear discriminant analysis (LDA) to problems of classification and estimation involving a large number of non-homogeneous (categorical and metric) variables. In this study, we compared the ability of traditional LDA and a feed-forward back-propagation (FF-BP) ANN with self-momentum to predict pharmacological treatments received by intravenous drug users (IDUs) hospitalised for coexisting medical illness. When medical staff considered detoxification appropriate they usually suggested methadone (MET) and (or) benzodiazepines (BDZ). Given four different treatment options (MET, BDZ, MET+BDZ, no treatment) as dependent variables and 38 independent variables, the FF-BP ANN provided the best prediction of the consultant's decision (overall accuracy: 62.7%). It achieved the highest level of predictive accuracy for the BDZ option (90.5%), the lowest for no treatment (29.6), often misclassifying no treatment as BDZ. The LDA yielded a lower mean accuracy (50.3%). When the untreated group was excluded, ANN improved its absolute recognition rate by only 1.2% and the BDZ group remained the best predicted. In contrast, LDA improved its absolute recognition rate from 50.3 to 58.9%, maximum 65.7% for the BDZ group. In conclusion, the FF-BP ANN was more accurate than the statistical model (discriminant analysis) in predicting the pharmacological treatment of IDUs

B-mode ultrasound measurements of carotid intima media thickness and the assessment of global cardiovascular risk

Carotid intima-media thickness (C-IMT) has been shown to be related to vascular risk factors (VRFs), prevalent cardiovascular disease (CVD), and atherosclerosis in coronary and peripheral arteries. Despite these relationships only a few studies have evaluated the potentiality of C-IMT to identify patient at high risk of CVD. In these, C-IMT included in a risk function for the assessment of global risk does not increases its predictivity. This can be due to a real lack of prediction capacity of C-IMT but also to the use of statistical tools unable to disentangle the non linear relationships connecting IMT to the global risk. Artificial neural networks (ANNs) are highly sophisticated computer algorithms able to recognise even the more hidden non linear relationships relating different variables, and to absolve complex classification tasks. In the present study the potentiality of C-IMT, alone or added to established VRFs, to identify patients at high risk of vascular disease (VD) was investigated by using ANNs and the classical statistical approach based on discriminant analyses. Patients were arbitrarily assigned to the high risk group when suffering from overt cardio- (myocardial infarction or angina), cerebro- (transient ischemic attack or stroke) or peripheral-VD. Arterial near and far wall of left and right carotid arteries were measured from 578 patients (464 at low and 114 at high risk for VD) by using B-Mode ultrasound. The results show that ANNs can be trained to identify low and high risk subjects with a greater accuracy than discriminant analyses. In addition, with optimal settings, a prediction accuracy of about 87% was achieved using conventional VRFs as input variables in the ANN classification task. When only ultrasonic variables were used, a prediction accuracy of about 77% was observed. The addition to this set of variables obtained without any additional cost (gender, age, weight, height and body mass index) led the accuracy of prediction to 86%. Pooling data of all ultrasonic variables and all VRFs did not significantly improve the performance of ANNs in the classification task (prediction accuracy = 83%). Finally, when ANNs were allowed to choose automatically the relevant input data (I.S. system-Semeion), 31 variables were selected and, among these, 6 were ultrasonic variables. By using this set of variables as input data the performance of ANNs in the classification task increased, reaching a prediction accuracy close to 92%, with 100% of correct classification of high risk patients. In conclusion, with the ANN technology C-IMT may increase the discriminant capacity of vascular risk factors in the classification of patients into low or high risk classes

Reti neurali artificiali: identificazione di pazienti ad alto rischio cardiovascolare

L\u2019identificazione dei pazienti ad alto rischio di malattie cardiovascolari (MC) \ue8 un importante obiettivo della medicina dei paesi occidentali. Negli ultimi anni numerosi sforzi sono stati effettuati al fine di sviluppare strumenti a basso costo per il riconoscimento precoce di questi soggetti. Importanti studi epidemiologici (PROCAM, FRAMINGHAM) hanno prodotto algoritmi capaci di individuare i soggetti a rischio sulla base dei fattori di rischio. L\u2019ispessimento medio intimale (IMT) delle carotidi extracraniche \ue8 un indice precoce di aterosclerosi generalizzata, anch\u2019esso potenzialmente utilizzabile per l\u2019individuazione precoce di soggetti predisposti alla patologia aterosclerotica. Le reti neurali artificiali (RNA) sono algoritmi informatici ispirati ai processi altamente interattivi del cervello umano. Come il cervello, le RNA sono in grado di decifrare i sottili meccanismi che mettono in relazione le diverse variabili in modelli sperimentali complessi e di assolvere a compiti di classificazione. Il presente studio \ue8 stato disegnato al fine di valutare la capacit\ue0 delle RNA di distinguere, sulla base dei fattori di rischio convenzionali, dell\u2019IMT carotideo o di entrambi, tra pazienti a basso o alto rischio per patologie cardiovascolari. Lo studio \ue8 stato condotto in 578 soggetti dislipidemici. Fra questi, 114 erano sintomatici per malattie cardiovascolari (infarto miocardico, angina), o cerebrovascolari (ischemia cerebrale transitoria, ictus) o per ateropatie periferiche e in quanto tali sono stati definiti ad alto rischio. I restanti 464 soggetti erano asintomatici e sono stati definiti a basso rischio. L\u2019IMT carotideo, visualizzato mediante ultrasonografia B-mode, \ue8 stato misurato in tempo reale utilizzando il calibro elettronico della macchina stessa. Per l\u2019analisi sono stati effettuati numerosi esperimenti utilizzando diverse reti neurali ideate dal Centro Ricerche Semeion. Nel migliore dei casi, utilizzando i fattori di rischio convenzionali come variabili di entrata nel sistema di classificazione \ue8 stata ottenuta una accuratezza di classificazione fra soggetti a basso o alto rischio (media ponderata) del 87%. Utilizzando come variabili di entrata le variabili ultrasonografiche, si otteneva una accuratezza del 77%. Aggiungendo a questo set di variabili quelle ottenibili a costo zero (et\ue0, sesso, peso, altezza e indice di massa corporea) l\u2019accuratezza di predizione aumentava fino all\u201986%. L\u2019utilizzo, nel sistema di classificazione, di tutte le variabili ecografiche e di tutti i fattori di rischio come variabili di entrata non migliorava l\u2019accuratezza delle RNA nel compito di classificazione (accuratezza di predizione pari a circa 83%). Infine, permettendo al sistema di selezionare automaticamente le variabili pi\uf9 rilevanti (I.S. system-Semeion ), 31 variabili entravano nel modello e fra queste ben 6 erano variabili ultrasonografiche. Utilizzando questo set di variabili, l\u2019accuratezza delle RNA nella classificazione dei soggetti a basso o ad alto rischio aumentava drammaticamente raggiungendo un\u2019accuratezza globale di predizione del 92% ed un 100% di classificazione corretta dei soggetti ad alto rischio. In conclusione, le RNA sono una tecnologia promettente per lo sviluppo di strumenti diagnostici utilizzabili nella routine clinica per la classificazione di pazienti a basso e ad alto rischio di patologie vascolari.Early recognition of patients at high risk of vascular diseases (VDs) is an important goal in medicine of western countries. Efforts in developing inexpensive screening devices that can assist in the differentiation between low and high risk subjects have been numerous. Large epidemiological studies (PROCAM, FRAMINGHAM) provide algorithms, which assess the individual global risk to develop new vascular events on the basis of vascular risk factors (VRFs). The intima media thickness (IMT) of carotid arteries (CAs) evaluated by ultrasound methodologies is an early manifestation of atherosclerosis, potentially predictive of symptomatic VD. IMT is a reliable index of the presence of atherosclerosis in other vascular districts and can predict new vascular events. Artificial neural networks (ANNs) are computer algorithms inspired by highly interactive processing of human brain. Like the brain, ANNs can recognize patterns and manage data, and when exposed to complex data sets, they have the capability to learn the underlying mechanics relating different variables and to recognise complex patterns and classification tasks. The purpose of the study was to evaluate the performance of ANNs in the recognition of patients at low or high risk of VDs on the basis of conventional VRFs, IMT or both. Patients were arbitrarily assigned to the high risk group when suffering from overt cardio- (myocardial infarction or angina), cerebro- (transient ischemic attack or stroke) or peripheral-VDs. The near and far wall of left and right carotid arteries were measured from 578 patients (464 at low and 114 at high risk for VDs) using B-Mode ultrasound. CA-IMT images were processed in real time by using the electronic caliper of the machine. With optimal settings, a prediction accuracy (weighted mean) of about 87% were obtained when conventional VRFs were used as input variables in the ANN classification system. When only ultrasonic variables were used, a prediction accuracy of about 77% was observed. The addition, to this last set, of variables obtained without any additional cost (gender, age, weight, height and body mass index) led accuracy of prediction to 86%. Pooling data of all ultrasonic variables and all VRFs did not significantly improve the performance of ANNs in the classification task (prediction accuracy = 83%). Finally, when ANNs were allowed to choose automatically the relevant input data (I.S. system), 31 variables were selected and, among these, 6 were ultrasonic variables. By using this set of variables as input data the performance of ANNs in the classification task increased, reaching a prediction accuracy about 92%, with 100% of correct classification of high risk patients. In conclusion, ANN technology is promising in the development of highly specific diagnostic tools to be used for patients\u2019 classification into low or high risk classes

ARTIFICIAL NEURAL NETWORKS IN THE RECOGNITION OF PATIENTS AT HIGH RISK OF CARDIOVASCULAR DISEASE

We have previously shown, in a large cross-sectional study, that intima media thickness (IMT) of carotid arteries, as measured in the normal clinical practice, correlated with most of the vascular risk factors (VRFs), and discriminated well between patients with and without previous history of cardiovascular events. This approach, however, does not provide information on the cardiovascular risk of individual patients, but rather it allows the identification of groups of patients. Artificial neural networks (ANNs) are computer algorithms inspired by highly interactive processing of human brain. ANNs are able to recognize patterns and, when exposed to complex data sets, they have the capability to learn the underlying mechanisms relating different variables and to recognize classification tasks. In present study, we have assessed the performance of ANNs in the recognition of patients at low (n= 464) or high risk (n=114) of vascular disease on the basis of conventional VRFs, IMT or both. Patients were arbitrarily assigned to the high risk group when suffering from overt cardiovascular disease. With optimal settings, a prediction accuracy of about 87% were obtained when conventional VRFs were used as input variables in the ANN classification system, whereas a 77% was obtained with only ultrasonic variables. The addition to ultrasonic variables of gender, age, weight, height and body mass index led this figure to 86%. When ANNs were allowed to choose automatically the relevant input data (I.S. system) 31 variables were selected comprising 6 ultrasonic variables. With this input, the performance of ANNs in the classification task increased reaching a prediction accuracy about 92%, with 100% of correct classification of high risk patients. In conclusion, ANN technology is promising in the development of highly specific diagnostic tools to be used for patients\u2019 classification into low or high risk classes

L\u2019ispessimento medio-intimale delle carotidi extracraniche nel riconoscimento del paziente con e senza eventi vascolari

Nonostante lo spessore medio-intimale delle carotidi extracraniche (IMT) sia stato associato ai fattori di rischio cardiovascolari e alla presenza di aterosclerosi nelle arterie coronariche e periferiche, pochi sono, ad oggi, gli studi volti a valutare la potenzialit\ue0 dell\u2019IMT carotideo nell\u2019identificazione dei pazienti ad alto rischio cardiovascolare. Negli studi finora pubblicati, l\u2019aggiunta dell\u2019IMT agli algoritmi matematici utilizzati per la valutazione del rischio cardiovascolare non ne aumentava la capacit\ue0 predittiva. Tali risultati potrebbero essere dovuti sia ad una reale mancanza di capacit\ue0 predittiva dell\u2019IMT carotideo sia all\u2019uso di strumenti statistici inadeguati per riconoscere le relazioni non lineari che legano l\u2019IMT, i fattori di rischio vascolare ed il rischio di sviluppare la patologia stessa. In questo studio \ue8 stata valutata la capacit\ue0 delle reti neurali artificiali, un nuovo tipo di strumento informatico, di distinguere tra pazienti con o senza eventi cardiovascolari, sulla base dell\u2019IMT carotideo, dei fattori di rischio convenzionali, o di entrambi. Lo studio \ue8 stato condotto in 578 soggetti dislipidemici, 114 dei quali definiti ad alto rischio in quanto sintomatici per malattie cardiovascolari (infarto miocardico, angina), cerebrovascolari (ischemia cerebrale transitoria, ictus) o per ateropatie periferiche. I risultati dimostrano che permettendo alle reti neurali artificiali di selezionare automaticamente le variabili pi\uf9 rilevanti \ue8 possibile raggiungere un\u2019accuratezza di predizione globale nella classificazione dei soggetti a basso o ad alto rischio del 92% con un 100% di classificazione corretta dei soggetti ad alto rischio. Per raggiungere tali risultati \ue8 fondamentale la presenza delle variabili relative all\u2019IMT carotideo. In conclusione, grazie all\u2019utilizzo delle reti neurali artificiali, l\u2019IMT delle carotidi extracraniche pu\uf2 aumentare la capacit\ue0 discriminante dei fattori di rischio convenzionali nella identificazione del paziente ad alto rischio di patologie vascolari

CORRELAZIONE TRA SPESSORE MEDIO-INTIMALE, PLACCHE CAROTIDEE E LESIONI CORONARICHE

Nonostante lo spessore medio-intimale delle carotidi extracraniche (IMT) sia stato associato ai fattori di rischio cardiovascolari e alla presenza di aterosclerosi nelle arterie coronariche e periferiche, pochi sono, ad oggi, gli studi volti a valutare la potenzialit\ue0 dell\u2019IMT carotideo nell\u2019identificazione dei pazienti ad alto rischio cardiovascolare. Negli studi finora pubblicati, l\u2019aggiunta dell\u2019IMT agli algoritmi matematici utilizzati per la valutazione del rischio cardiovascolare non ne aumentava la capacit\ue0 predittiva. Tali risultati potrebbero essere dovuti sia ad una reale mancanza di capacit\ue0 predittiva dell\u2019IMT carotideo sia all\u2019uso di strumenti statistici inadeguati per riconoscere le relazioni non lineari che legano l\u2019IMT, i VRFs ed il rischio di sviluppare la patologia stessa. In un recente studio da noi effettuato \ue8 stata valutata la capacit\ue0 di un nuovo tipo di strumenti informatici, le reti neurali artificiali (RNA) di distinguere tra pazienti a basso o alto rischio cardiovascolare, sulla base dell\u2019IMT carotideo, dei fattori di rischio convenzionali, o di entrambi. Lo studio \ue8 stato condotto in 578 soggetti dislipidemici, 114 dei quali definiti ad alto rischio in quanto sintomatici per malattie cardiovascolari (infarto miocardico, angina), cerebrovascolari (ischemia cerebrale transitoria, ictus) o per ateropatie periferiche. I risultati dimostrano che permettendo alle RNA di selezionare automaticamente le variabili pi\uf9 rilevanti \ue8 possibile raggiungere un\u2019accuratezza di predizione globale nella classificazione dei soggetti a basso o ad alto rischio del 92% con un 100% di classificazione corretta dei soggetti ad alto rischio. Per raggiungere tali risultati la presenza delle variabili relative all\u2019IMT carotideo \ue8 fondamentale. In conclusione, grazie all\u2019utilizzo delle RNA, l\u2019IMT delle carotidi extracraniche pu\uf2 aumentare la capacit\ue0 discriminante dei fattori di rischio convenzionali nella identificazione del paziente ad alto rischio di patologie vascolari

Intima media thickness and vascular risk factors for the recognition of patients at high risk of atherosclerosis

We have previously shown in a large cross-sectional study that intima media thickness (IMT) of carotid arteries, as measured in the normal clinical practice, correlated with most of the vascular risk factors (VRFs), and discriminated well between patients with and without previous history of cardiovascular events. This approach, however, does not provide information on the cardiovascular risk of individual patients, but rather it allows the identification of groups of patients. Artificial neural networks (ANNs) are computer algorithms inspired by highly interactive processing of human brain. ANNs are able to recognize patterns and, when exposed to complex data sets, they have the capability to learn the underlying mechanisms relating different variables and to recognize classification tasks. In present study, we have assessed the performance of ANNs in the recognition of patients at low (n= 464) or high risk (n=114) of vascular disease on the basis of conventional VRFs, IMT or both. Patients were arbitrarily assigned to the high risk group when suffering from overt cardiovascular disease. With optimal settings, a prediction accuracy of about 87% were obtained when conventional VRFs were used as input variables in the ANN classification system, whereas a 77% was obtained with only ultrasonic variables. The addition to ultrasonic variables of gender, age, weight, height and body mass index led this figure to 86%. When ANNs were allowed to choose automatically the relevant input data (I.S. system) 31 variables were selected comprising 6 ultrasonic variables. With this input, the performance of ANNs in the classification task increased reaching a prediction accuracy about 92%, with 100% of correct classification of high risk patients. In conclusion, ANN technology is promising in the development of highly specific diagnostic tools to be used for patients\u2019 classification into low or high risk classes

Recognition of patients with cardiovascular disease by artificial neural networks

BACKGROUND: Artificial neural networks (ANNs) are computer algorithms inspired by the highly interactive processing of the human brain. When exposed to complex data sets, ANNs can learn the mechanisms that correlate different variables and perform complex classification tasks. AIMS: A database, of 949 patients and 54 variables, was analysed to evaluate the capacity of ANNs to recognise patients with (VE+, n = 196) or without (VE-, n = 753) a history of vascular events on the basis of vascular risk factors (VRFs), carotid ultrasound variables (UVs) or both. METHOD: The performance of ANN was assessed by calculating the percentage of correct identifications of VE+ and VE- patients (sensitivity and specificity, respectively) and the prediction accuracy (weighted mean between sensitivity and specificity). RESULTS: The results showed that ANNs can be trained to identify VE+ and VE- subjects more accurately than discriminant analyses. When VRFs and UVs were used as input variables, the prediction accuracies of the ANN providing the best results were 80.8% and 79.2%, respectively. The addition of gender, age, weight, height and body mass index to UVs increased accuracy of prediction to 83.0%. When the ANNs were allowed to choose the relevant input data automatically (I.S. system-Semeion), 37 variables were selected among 54, five of which were UVs. Using this set of variables as input data, the performance of the ANNs in the classification task reached a prediction accuracy of 85.0%. with the 92.0% correct classification of VE+ patients. CONCLUSIONS: Artificial neural network technology is highly promising in the development of accurate diagnostic tools designed to recognize patients at high risk of cardiovascular diseases