[Optimal medical performance develops on the decision tree. Decision methodology and analysis--clinical economy series, 6].
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Cost-Benefit Analysis↗
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Online adaptive decision trees.
Decision trees and neural networks are widely used tools for pattern classification. Decision trees provide highly localized representation, whereas neural networks provide a distributed but compact representation of the decision space. Decision trees cannot be induced in the online mode, and they are not adaptive to changing environment, whereas neural networks are inherently capable of online learning and adpativity. Here we provide a classification scheme called online adaptive decision trees (OADT), which is a tree-structured network like the decision trees and capable of online learning like neural networks. A new objective measure is derived for supervised learning with OADT. Experimental results validate the effectiveness of the proposed classification scheme. Also, with certain real-life data sets, we find that OADT performs better than two widely used models: the hierarchical mixture of experts and multilayer perceptron.
Computer Simulation↗
The influence of class discretization to attribute hierarchy of decision trees.
Decision support systems that help physicians are becoming very important part of medical decision making. They are based on different models and the best of them are providing an explanation together with an accurate, reliable and quick response. One of the most viable among decision-making models is the concept of decision trees, already successfully used for many medical decision making purposes. Although effective and reliable, the traditional decision tree construction approach still contains several deficiencies. Therefore we decided to develop and compare several decision supporting models, each of them built with different discretization of attributes and decision classes. For the construction of decision trees we used MtDeciT, in our laboratory developed tool for building decision trees using the classical induction method. All solutions were evolved for determining the influence of basic properties of child and his/her parents to length of successful breastfeeding. A comparison between developed models and obtained results has shown that the way of discretization obviously plays a great role in the reliable and accurate real-world medical decision making.
Adult↗
The art of building decision trees.
Decision support systems that help physicians are becoming a very important part of medical decision making. They are based on different models and the best of them are providing an explanation together with an accurate, reliable, and quick response. One of the most viable among models are decision trees, already successfully used for many medical decision-making purposes. Although effective and reliable, the traditional decision tree construction approach still contains several deficiencies. Therefore we decided to develop and compare several decision support models using four different approaches. We took statistical analysis, a MtDeciT, in our laboratory developed tool for building decision trees with a classical method, the well-known C5.0 tool and a self-adapting evolutionary decision support model that uses evolutionary principles for the induction of decision trees. Several solutions were evolved for the classification of metabolic and respiratory acidosis (MRA). A comparison between developed models and obtained results has shown that our approach can be considered as a good choice for different kinds of real-world medical decision making.
Acidosis, Respiratory↗
Computational methods for probabilistic decision trees.
Decision tree models may be more realistic if branching probabilities (and possibly utilities) are represented as distributions rather than point estimates. However, numerical analysis of such "probabilistic" trees is more difficult. This study employed the Mathematica computer algebra system to implement and verify previously described probabilistic methods. Both algebraic approximations and Monte Carlo simulation methods were used; in particular, simulations with beta, logistic-normal, and triangular distributions for branching probabilities were compared. Algebraic and simulation methods of sensitivity analysis were also implemented and compared. Computation required minimal programming and was reasonably fast using Mathematica on a standard personal computer. This study verified previously published results, including methods of sensitivity analysis. Changing the input distributional form had little effect. Computation is no longer a significant barrier to the use of probabilistic methods for analysis of decision trees.
Computer Simulation↗
Neuro-fuzzy decision trees.
Fuzzy decision trees are powerful, top-down, hierarchical search methodology to extract human interpretable classification rules. However, they are often criticized to result in poor learning accuracy. In this paper, we propose Neuro-Fuzzy Decision Trees (N-FDTs); a fuzzy decision tree structure with neural like parameter adaptation strategy. In the forward cycle, we construct fuzzy decision trees using any of the standard induction algorithms like fuzzy ID3. In the feedback cycle, parameters of fuzzy decision trees have been adapted using stochastic gradient descent algorithm by traversing back from leaf to root nodes. With this strategy, during the parameter adaptation stage, we keep the hierarchical structure of fuzzy decision trees intact. The proposed approach of applying backpropagation algorithm directly on the structure of fuzzy decision trees improves its learning accuracy without compromising the comprehensibility (interpretability). The proposed methodology has been validated using computational experiments on real-world datasets.
Decision Making, Computer-Assisted↗
Production of diagnostic rules from a neurotologic database with decision trees.
A decision tree is an artificial intelligence program that is adaptive and is closely related to a neural network, but can handle missing or nondecisive data in decision-making. Data on patients with Meniere's disease, vestibular schwannoma, traumatic vertigo, sudden deafness, benign paroxysmal positional vertigo, and vestibular neuritis were retrieved from the database of the otoneurologic expert system ONE for the development and testing of the accuracy of decision trees in the diagnostic workup. Decision trees were constructed separately for each disease. The accuracies of the best decision trees were 94%, 95%, 99%, 99%, 100%, and 100% for the respective diseases. The most important questions concerned the presence of vertigo, hearing loss, and tinnitus; duration of vertigo; frequency of vertigo attacks; severity of rotational vertigo; onset and type of hearing loss; and occurrence of head injury in relation to the timing of onset of vertigo. Meniere's disease was the most difficult to classify correctly. The validity and structure of the decision trees are easily comprehended and can be used outside the expert system.
Databases, Factual↗
A computerised guidance tree (decision aid) for hypertension, based on decision analysis: development and preliminary evaluation.
This paper discusses the piloting of a computerised decision aid that provides individualised information about hypertension to patients. The program is based on decision analysis, using decision trees as a way of structuring information. It incorporates the Framingham risk equation to assess a users' risk of coronary artery disease, together with a detailed assessment of the patient's current lifestyle and their willingness to change behaviour. Users of the program can decide how much or how little information they access. The program assesses individual's preferences for different treatment outcomes, before providing them with guidance on what might be the best treatment option for them. The program was evaluated by 10 patients with a diagnosis of mild to moderate hypertension and 8 health care professionals. Overall, both health care professionals and patients assessed the program positively. The use of a decision aid based on decision analysis may be a useful way of providing information to patients in order to promote shared decision making.
Aged↗
Determination of hERG channel blockers using a decision tree.
A decision tree approach for the in silico prediction of Torsade de Pointes (TdP)-causing drugs is presented. As TdP is frequently associated with QT-interval prolongation due to inhibition of the rapid activating delayed rectifier potassium channel in the heart (hERG channel), the properties of such blockers were investigated by molecular modeling and semi-empirical AM1 molecular orbital calculations. In addition, we derived a pharmacophoric SMARTS string using structural information from high affinity compounds. A corresponding search in the PubChem database identified several compounds that exhibit QT-interval prolonging activity that were not among our data set. This SMARTS string furthermore showed to be the most significant descriptor in the decision tree approach from which guidelines for the design of safe compounds are suggested.
Decision Trees↗
Evaluating training data suitability for decision tree induction.
Decision tree induction, as well as other inductive learning methods, requires training data of high quality to be able to generate accurate and reliable classification models. Example cases should form a representative sample from the application area, and the attributes used to describe example cases should be relevant and adequate for the classification task to be solved. In this paper, measures of the strength of association and an entropy-based approach have been used to assess the quality of the training data. Studied classification tasks related to three otological data sets: a conscript data set, a vertigo data set, and a postoperative nausea and vomiting data set. The paper suggests that the studied approaches give some guidelines about the quality of the training data, but other approaches are also needed to guide training data building.
Adolescent↗
Mining SARS-CoV protease cleavage data using non-orthogonal decision trees: a novel method for decisive template selection.
MOTIVATION: Although the outbreak of the severe acute respiratory syndrome (SARS) is currently over, it is expected that it will return to attack human beings. A critical challenge to scientists from various disciplines worldwide is to study the specificity of cleavage activity of SARS-related coronavirus (SARS-CoV) and use the knowledge obtained from the study for effective inhibitor design to fight the disease. The most commonly used inductive programming methods for knowledge discovery from data assume that the elements of input patterns are orthogonal to each other. Suppose a sub-sequence is denoted as P2-P1-P1'-P2', the conventional inductive programming method may result in a rule like 'if P1 = Q, then the sub-sequence is cleaved, otherwise non-cleaved'. If the site P1 is not orthogonal to the others (for instance, P2, P1' and P2'), the prediction power of these kind of rules may be limited. Therefore this study is aimed at developing a novel method for constructing non-orthogonal decision trees for mining protease data. RESULT: Eighteen sequences of coronavirus polyprotein were downloaded from NCBI (http://www.ncbi.nlm.nih.gov). Among these sequences, 252 cleavage sites were experimentally determined. These sequences were scanned using a sliding window with size k to generate about 50,000 k-mer sub-sequences (for short, k-mers). The value of k varies from 4 to 12 with a gap of two. The bio-basis function proposed by Thomson et al. is used to transform the k-mers to a high-dimensional numerical space on which an inductive programming method is applied for the purpose of deriving a decision tree for decision-making. The process of this transform is referred to as a bio-mapping. The constructed decision trees select about 10 out of 50,000 k-mers. This small set of selected k-mers is regarded as a set of decisive templates. By doing so, non-orthogonal decision trees are constructed using the selected templates and the prediction accuracy is significantly improved.
Algorithms↗
Leptospirosis vaccination in beef cattle: use of decision tree analysis.
Decision tree analysis was used to evaluate the economic benefits of leptospirosis vaccination in beef cattle. The analysis used estimated values for the cost of vaccination, the probability of calf losses from leptospirosis and the economic losses resulting from calf mortality to calculate the expected value of vaccination. A computer spreadsheet was used to calculate the expected value of the vaccination programme over a wide range of estimated costs and probabilities and to determine the break-even point at which vaccination becomes economic. A sensitivity analysis demonstrated that the outcome of the analysis was most dependent on calf value and the probability of calf loss due to leptospirosis. The analysis was relatively insensitive to changes in vaccine cost, vaccine efficacy and herd fertility.
Journal Article↗
Assessing school readiness for a practice arrangement using decision tree methodology.
Decision-tree methodology provides a systematic approach for schools to use in examining their own situation to determine if a faculty practice arrangement is appropriate for their particular school at this particular time. Questions in the decision tree address mission, faculty interest, administrative support, a practice plan, and practice models. Specific questions in the decision tree include the following: Does the institutional mission include service/practice? Does the college/school mission include practice? Does the college/school have a critical mass of faculty interested in practice? Does college/school administration support practice? Does a critical mass of faculty desire a faculty practice plan? Can faculty and administration work together to produce a mutually beneficial practice arrangement? Do faculty and administration desire to operate their own practice arrangement? Is there an identified need for nursing services that can be met by the college/school? Does the internal and external environment support the college/school operating a practice? Do internal or external organizations desire the talents/expertise of faculty members? Schools frequently evaluate two models, the Integration/Nursing Center Model and the Organizational Contracting/Entrepreneurship Model, for their specific risks and benefits. Critical to the success of any practice arrangement is that there be congruency between the school's allocation of human resources and its reward system.
Decision Making, Organizational↗
The limitations of decision trees and automatic learning in real world medical decision making.
The decision tree approach is one of the most common approaches in automatic learning and decision making. The automatic learning of decision trees and their use usually show very good results in various "theoretical" environments. But in real life it is often impossible to find the desired number of representative training objects for various reasons. The lack of possibilities to measure attribute values, high cost and complexity of such measurements, and unavailability of all attributes at the same time are the typical representatives. For this reason we decided to use the decision trees not for their primary task--the decision making--but for outlining the most important attributes. This was possible by using a well-known property of the decision trees--their knowledge representation, which can be easily understood by humans. In a delicate field of medical decision making, we cannot allow ourselves to make any inaccurate decisions and the "tips," provided by the decision trees, can be of a great assistance. Our main interest was to discover a predisposition to two forms of acidosis: the metabolic acidosis and respiratory acidosis, which can both have serious effects on child's health. We decided to construct different decision trees from a set of training objects. Instead of using a test set for evaluation of a decision tree, we asked medical experts to take a closer look at the generated trees. They examined and evaluated the decision trees branch by branch. Their comments show that trees generated from the available training set mainly have surprisingly good branches, but on the other hand, for some, no medical explanation could be found.
Acidosis↗
The limitations of decision trees and automatic learning in real world medical decision making.
The decision tree approach is one of the most common approaches in automatic learning and decision making. It is popular for its simplicity in constructing, efficient use in decision making and for simple representation, which is easily understood by humans. The automatic learning of decision trees and their use usually show very good results in various "theoretical" environments. The training sets are usually large enough for learning algorithm to construct a hypothesis consistent with the underlying concept. But in real life it is often impossible to find the desired number of training objects for various reasons. The lack of possibilities to measure attribute values, high cost and complexity of such measurements, unavailability of all attributes at the same time are the typical representatives. There are different ways to deal with some of these problems, but in a delicate field of medical decision making, we cannot allow ourselves to make any inaccurate decisions. We have measured the values of 24 attributes before and after the 82 operations of children in age between 2 and 10 years. The aim was to find the dependencies between attribute values and a child's predisposition to acidemia--the decrease of blood's pH. Our main interest was in discovering predisposition to two forms of acidosis, the metabolic acidosis and the respiratory acidosis, which can both have serious effects on child's health. We decided to construct different decision trees from a set of training objects, which was complete (there were no missing attribute values), but on the other hand not large enough to avoid the effect of overfitting. A common approach to evaluation of a decision tree is the use of a test set. In our case we decided that instead of using a test set, we ask medical experts to take a closer look at the generated trees. They examined and evaluated the decision trees branch by branch. Their comments on the generated trees can be found in this paper. The comments show, that trees generated from available training set mainly have surprisingly good branches, but on the other hand some are very "stupid" and no medical explanation could be found. Thereafter we can conclude, that the decision tree concept and automatic learning can be successfully used in real world situations, constrained with the real world limitations, but they should be used only with the guidelines of appropriate medical experts.
Acidosis↗
Induction of decision trees via evolutionary programming.
Decision trees have been used extensively in cheminformatics for modeling various biochemical endpoints including receptor-ligand binding, ADME properties, environmental impact, and toxicity. The traditional approach to inducing decision trees based upon a given training set of data involves recursive partitioning which selects partitioning variables and their values in a greedy manner to optimize a given measure of purity. This methodology has numerous benefits including classifier interpretability and the capability of modeling nonlinear relationships. The greedy nature of induction, however, may fail to elucidate underlying relationships between the data and endpoints. Using evolutionary programming, decision trees are induced which are significantly more accurate than trees induced by recursive partitioning. Furthermore, when assessed on previously unseen data in a 10-fold cross-validated manner, evolutionary programming induced trees exhibit a significantly higher accuracy on previously unseen data. This methodology is compared to single-tree and multiple-tree recursive partitioning in two domains (aerobic biodegradability and hepatotoxicity) and shown to produce less complex classifiers with average increases in predictive accuracy of 5-10% over the traditional method.
Biological Evolution↗
A decision tree for the early diagnosis of acute myocardial infarction in nontraumatic chest pain patients at hospital admission.
STUDY OBJECTIVE: To find an accurate algorithm for the diagnosis of acute myocardial infarction in nontraumatic chest pain patients on presentation to the emergency department. DESIGN: In a prospective clinical study, we compared the diagnostic performances of clinical symptoms, presenting ECG, creatinine kinase, creatine kinase MB activity and mass concentration, myoglobin, and cardiac troponin T test results of hospital admission blood samples. By classification and regression trees, a decision tree for the diagnosis of acute myocardial infarction was developed. SETTING: Emergency room of a Department of Internal Medicine (University Hospital). PATIENTS: One hundred fourteen nontraumatic chest pain patients (median delay from onset of chest pain to hospital admission, 3 h; range, 0.33 to 22): 26 Q-wave and 19 non-Q-wave myocardial infarctions, 49 patients with unstable angina pectoris, and 20 patients with chest pain caused by other diseases. MEASUREMENTS AND RESULTS: Of each parameter taken by itself, the ECG was tendentiously most informative (areas under receiver operating characteristic plots: 0.87 +/- 0.04 [ECG], 0.80 +/- 0.08 [myoglobin], 0.80 +/- 0.04 [creatine kinase MB mass], 0.77 +/- 0.04 [creatine kinase activity], 0.69 +/- 0.06 [clinical symptoms] 0.67 +/- 0.06 [creatine kinase MB activity], 0.67 +/- 0.05 [troponin T]). In patients presenting 3 h or less after the onset of chest pain, ECG signs of acute transmural myocardial ischemia were the best discriminator between patients with and without myocardial infarction. In patients presenting more than 3 h, however, creatine kinase MB mass concentrations (discriminator value, 6.7 micrograms/L) were superior to the ECG, clinical symptoms, and all other biochemical markers tested. This algorithm for diagnosing acute myocardial infarction was superior to each parameter by itself and was characterized by 0.91 sensitivity, a 0.90 specificity, a 0.90 positive and negative predictive value, and a 0.90 efficiency. CONCLUSIONS: We found an algorithm that could accurately separate the myocardial infarction patients from the others on admission to the emergency department. Therefore, this classifier could be a valuable diagnostic aid for rapid confirmation of a suspected myocardial infarction.
Adult↗
Supporting medical decisions with vector decision trees.
The article presents the extension of a common decision tree concept to a multidimensional - vector - decision tree constructed with the help of evolutionary techniques. In contrary to the common decision tree the vector decision tree can make more than just one suggestion per input sample. It has the functionality of many separate decision trees acting on a same set of training data and answering different questions. Vector decision tree is therefore simple in its form, is easy to use and analyse and can express some relationships between decisions not visible before. To explore and test the possibilities of this concept we developed a software tool--DecRain--for building vector decision trees using the ideas of evolutionary computing. Generated vector decision trees showed good results in comparison to classical decision trees. The concept of vector decision trees can be safely and effectively used in any decision making process.
Algorithms↗