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D - which uses only the four adverse outcomes. Since the SOFA score takes costs and time to obtain, in this study, a special attention will be given to the last two settings. In the initial experiments, it was considered more important to approach feature selection than model selection. Due to time constrains, the number of hidden nodes was set to round(N/2), where N denotes the number of input nodes (N = 5, N = 21, N = 16 and N =4, for the A, B, C and D setups, respectively); and round(x) gives nearest integer to the x value. The commonly used 2/3 and 1/3 partitions were adopted for the training and test sets (Flexer, 1996), while the maximum number of training epochs was set to E = 100. Each input configuration was tested for all organ systems, being the accuracy measures given in terms of the mean of thirty runs (Table 5). The A selection manages to achieve a high performance, with an Accuracy ranging from 86% to 97%, even surpassing the B configuration. This is not surprising, since it is a well established fact that the SOFA is a adequate score for organ dysfunction. Therefore, the results suggest that there is a high correlation between SOFAd−1 and SOFAd. When the SOFA index is omitted (C and D), the Accuracy values only decay slightly. However, this measure (which is popular within Data Mining community) is not sufficient in Medicine. Ideally, a test should report both high Sensitivity and Specificity values, which suggest a high level of confidence (Essex- Sorlie, 1995). In fact, there seems to be a tradeoff between these two characteristics, since when the SOFA values are not present (Table 5), the Sensitivity values suffer a huge loss, while the Specificity values increase. 3.2 Balanced Training Why do the A/B selections lead to high Accuracy /Specificity values and low Sensitivity ones? The answer may be due to the biased nature of the organ dysfunction distributions; i.e., there is a much higher number of false (0) than true (1) conditions (Figure 3). One solution to solve this handicap, is to balance the training data; i.e., to use an equal number of true and false learning examples. Therefore, another set of experiments was devised (Table 6), using random sampling training sets, which contained 2/3 of the true examples, plus an equal number of false examples. The test set was composed of the other 1/3 positive entries. In order to achieve a fair comparison with the previous results, the negative test examples were randomly selected from the remaining ones, with a distribution identical to the one found in the original MULTIPLE ORGAN FAILURE DIAGNOSIS dataset (as given by Figure 3). The obtained results show a clear improvement in the Sensitivity values, specially for the C configuration, stressing the importance of the case mix attributes. Yet, the overall results are still far from the ones given by the A selection. 3.3 Improving Learning Until now, the main focus was over selecting the correct training data. Since the obtained results are still not satisfactory, the attention will move towards better Neural Network modeling. This will be achieved by changing two parameters: the number of hidden nodes and the maximum number of training epochs. Due to computational power restrictions, these factors were kept fixed in the previous experiments. However, the adoption of balanced training leads to a considerable reduction of the number of training cases, thus reducing the required training time. Several experimental trials were conducted, using different combinations of hidden nodes (H =4, 8, 16 and 32) and maximum number of epochs (E = 100, 500 and 1000), being selected the configuration which gave the lowest training errors (H = 16 and E = 1000). These setup lead to better results, for all organ systems and accuracy measures (Table 6). To evaluate the obtained results, a comparison with other Machine Learning classifiers was performed (Table 7), using two classical methods from the WEKA Machine Learning software (Witten and Frank, 2000): Naive Bayes - a statistical algorithm based on probability estimation; and JRIP - a learner based on ” IF-THEN” rules. Although presenting a better Accuracy, theNaive Bayes tends to emphasize the Specificity values, giving poor Sensitivity results. A better behavior is given by the JRIP method, with similar Sensitivity and Specificity values. Nevertheless, the Neural Networks still exhibit the best overall performances. 4 CONCLUSIONS 131 The surge of novel bio-inspired tools, such as Neural Networks, has created new exciting possibilities for the field of Clinical Data Mining. In this work, these techniques were applied for organ failure diagnosis of ICU patients. Preliminary experiments were drawn to test several feature selection configurations, being the best results obtained by the solely use of the SOFA value, measured in the previous day. However, this score takes much more time and costs to be obtained, when compared with the physiologic adverse events. Therefore, another set of experiments were conducted, in order
132 ENTERPRISE INFORMATION SYSTEMS VI Table 5: The feature selection performances (in percentage) Organ Acc. A Sen. Spe. Acc. B Sen. Spe. Acc. C Sen. Spe. Acc. D Sen. Spe. respirat 86.3 72.4 90.2 86.2 70.0 90.8 77.9 4.4 98.8 77.6 1.8 99.4 coagulat 97.4 68.8 98.7 97.3 59.6 99.0 95.8 4.6 99.9 95.7 0.0 100 liver 98.3 68.6 99.1 98.3 60.2 99.4 97.3 7.6 99.9 97.3 0.0 100 cardiova 94.2 84.1 96.3 94.2 84.0 96.3 82.8 7.5 99.0 82.2 0.5 99.8 cns 95.7 92.7 96.4 95.7 92.3 96.4 83.5 23.4 97.1 81.6 0.4 99.9 renal 95.5 71.3 97.8 95.3 66.6 98.1 91.4 5.7 99.7 91.1 0.3 100 Mean 94.6 76.3 96.4 94.5 72.1 96.7 88.1 8.9 99.1 87.6 0.5 99.96 Acc. - Accuracy, Sen. - Sensitivity, Spe - Specificity. Table 6: The balanced C, D and C improved performances (in percentage) Organ Acc. C Sen. Spe. Acc. D Sen. Spe. C(improved) Acc. Sen. Spe. respirat 61.3 66.4 59.8 67.1 41.1 74.5 63.3 70.4 61.3 coagulat 67.6 66.8 67.7 73.7 41.5 75.1 70.0 72.0 69.9 liver 70.0 71.6 70.0 66.9 36.5 67.8 72.5 77.3 72.4 cardiova 65.9 62.5 66.7 68.2 37.9 74.8 69.1 66.3 69.8 cns 73.6 63.9 75.7 66.8 36.3 73.7 75.2 72.2 75.8 renal 67.8 65.6 68.0 73.2 37.6 76.6 71.9 70.5 72.0 Mean 67.7 66.2 68.0 69.3 38.5 73.8 70.3 71.5 70.2 Acc. - Accuracy, Sen. - Sensitivity, Spe - Specificity. Table 7: The classifiers performances for the C selection balanced data (in percentage) Organ Naive Bayes Acc. Sen. Spe. Acc. JRIP Sen. Spe. Neural Networks Acc. Sen. Spe. respirat 73.5 25.2 87.3 62.8 61.9 63.0 63.3 70.4 61.3 coagulat 83.3 24.8 85.8 67.8 62.4 68.0 70.0 72.0 69.9 liver 70.8 54.3 71.2 75.7 73.7 75.7 72.5 77.3 72.4 cardiova 73.4 33.4 82.0 66.6 70.3 65.8 69.1 66.3 69.8 cns 76.3 41.3 84.2 77.6 74.4 78.3 75.2 72.2 75.8 renal 76.8 45.6 79.9 69.1 68.5 69.2 71.9 70.5 72.0 Mean 75.7 37.4 81.7 69.9 68.5 70.15 70.3 71.5 70.2 Acc. - Accuracy, Sen. - Sensitivity, Spe - Specificity.
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vi TABLE OF CONTENTS PART 1 - DATAB
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viii TABLE OF CONTENTS A WIRELESS A
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CONFERENCE COMMITTEE Honorary Presi
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Hung, P. (AUSTRALIA) Jahankhani, H.
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Invited Papers
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2 ENTERPRISE INFORMATION SYSTEMS VI
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10 ENTERPRISE INFORMATION SYSTEMS V
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EVOLUTIONARY PROJECT MANAGEMENT: MU
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MANAGING COMPLEXITY OF ENTERPRISE I
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ASSESSING EFFORT PREDICTION MODELS
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ORGANIZATIONAL AND TECHNOLOGICAL CR
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ASAP Processes W Project Kickoff A
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since it means changing the relevan
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ACME-DB: AN ADAPTIVE CACHING MECHAN
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ACME-DB: AN ADAPTIVE CACHING MECHAN
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Hit Rate (%) ACME-DB: AN ADAPTIVE C
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Page 105 and 106: sive data filtering, presentation (
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Page 111 and 112: Data Rate [bytes/sec] 1600000 14000
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Page 119 and 120: PART 2 Artificial Intelligence and
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Page 127 and 128: DYNAMIC MULTI-AGENT BASED VARIETY F
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Page 169 and 170: AN EXPERIENCE IN MANAGEMENT OF IMPR
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BALANCING STAKEHOLDER'S PREFERENCES
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A POLYMORPHIC CONTEXT FRAME TO SUPP
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A POLYMORPHIC CONTE XT FRAME TO SUP
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FEATURE MATCHING IN MODEL-BASED SOF
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combination of solution domains - a
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• features for all the concepts:
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Existence In both steps it is possi
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matching strategies are maximal, mi
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TOWARDS A META MODEL FOR DESCRIBING
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elementary message (ae-message) can
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problems on a pragmatic level, whic
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TOWARDS A META MODEL FOR DESCRIBING
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INTRUSION DETECTION SYSTEMS USING A
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INTRUSION DETECTION SYSTEMS USING A
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(k� 1) (k) (k�1) (k) p � w
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Table 5: Performance Comparison of
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A USER-CENTERED METHODOLOGY TO GENE
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carries out the second phase of the
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1 1 1 1 2 PROCESS STORE ENTITY EDGE
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constraints rules. KOGGE (Ebert et
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PART 4 Software Agents and Internet
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230 ENTERPRISE INFORMATION SYSTEMS
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CABA 2 L A BLISS PREDICTIVE COMPOSI
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y disables evidencing severe mental
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Figure 4: Symbols emission in DAR-H
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they evidence a generalization erro
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A METHODOLOGY FOR INTERFACE DESIGN
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and mobility loss coupled with redu
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Tradeoff: The default input is poss
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Sessions on the VABS which are held
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A CONTACT RECOMMENDER SYSTEM FOR A
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A CONTACT RECOMMENDER SYSTEM FOR A
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- "Going to the sources". The user
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Here are the matrix W and P for our
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EMOTION SYNTHESIS IN VIRTUAL ENVIRO
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theme turns out to be surprisingly
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6 FROM FEATURES TO SYMBOLS 6.1 Face
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sions are described by different em
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Electronic Imaging 2000 Conference
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to the accessibility problem for th
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Figure 3: Hierarchical View of the
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4 CONCLUSIONS AND FUTURE WORK On th
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PERSONALISED RESOURCE DISCOVERY SEA
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profile, the user defines his curre
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Abdelkafi, N. .....................
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