SEARCH · PubMed Health

Results for “Decision Trees”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources

PubMed1980

Decision trees: construction, uses, and limits.

Decision trees are models of the temporal and logical flow of clinical problems. Their purpose is to help the physician choose a clinical management strategy that offers the greatest expected value for the patient. Decision trees help answer questions such as:"should a risky diagnostic test be performed?" "Given our present knowledge, which of several available treatments is best for this patient?" "What are the expected benefits, risks, and financial costs of pursuing different stages of patient care?" Decision trees do not create new information, but they can provide new insights based on existing information. The principles of analyzing a clinical situation from a decision analytic perspective and of constructing and using a decision tree are illustrated with three clinical examples: a patient with possible urinary tract infection, a young man with Hodgkin's disease, and patients with chronic progressive liver failure. We present a simplified quantitative analysis to determine whether the patient with Hodgkin's disease should undergo a staging laparotomy. The last example serves as a prelude to R. Fuhrer's discussion of the expected value of test information. Following R. Fuhrer's presentation, we discuss some of the objections and advantages to medical decision analysis. Despite its limitations we believe decision analysis can be a powerful aid to medical practitioners.

Adolescent↗

PubMedDate not supplied

Methods of decision analysis: protocols, decision trees, and algorithms in medicine.

Algorithms, decision trees, and protocols are defined and explained since they constitute an accepted part of clinical decision analysis and application to clinical care. Algorithms are particularly useful for common clinical problems where uncertainties are unlikely. Decision trees are helpful when--as usually occurs in difficult clinical decisions--there are problems in probability. Clinical protocols, which, at best, are based on algorithms and decision trees, provide instruction of how to best treat a patient given the strict definitions of the clinical problem. These techniques are, in essence, merely graphic representations of a logical scientific approach to clinical problems. Criticisms of these techniques center on their rigidity and the automatic unthinking cookbook medicine they might sponsor. It is concluded that if these techniques are wisely designed and, even more importantly, wisely administered with an understanding flexibility, they can lead to both economy and patient benefit.

Algorithms↗

PubMed1989

[Construction of a decision tree for preoperative diagnosis in urinary incontinence].

In order to analyse the decisions used in non-urodynamic preoperative diagnosis of female urinary incontinence we constructed a decision tree, consisting of decision nodes and probability nodes. Each branch of the decision tree leads to an end point (terminal node), to which an utility must be assigned. We describe the different steps in the construction of the decision tree: 1. listing of all possible decisions (questionnaire, clinical stress test, both, no preoperative tests), 2. assignment of probabilities to the different events (e.g. cure, failure, operation in the absence of genuine stress incontinence), and 3. assignment of utility factors to the end points. We used previously published probabilities from our clinic, utility was assigned arbitrarily using a score from -10 to +90. The decision tress allows not only the calculation of the strategy with the highest expected utility, but also threshold analysis, sensitivity analysis and cost-benefit analysis. We feel that decision trees are valuable tools for analysing how medical decisions are reached and are particularly useful in teaching situations.

Adult↗

PubMedDate not supplied

Automated critiquing of medical decision trees.

The authors developed a decision tree-critiquing program (called BUNYAN) that identifies potential modeling errors in medical decision trees. The program's critiques are based on the structure of a decision problem, obtained from an abstract description specifying only the basic semantic categories of the model's components. A taxonomy of node and branch types supplies the primitive building blocks for representing decision trees. Bunyan detects potential problems in a model by matching general pattern expressions that refer to these primitives. A small set of general principles justifies critiquing rules that detect four categories of potential structural problems: impossible strategies, dominated strategies, unaccountable violations of symmetry, and omission of apparently reasonable strategies. Although critiquing based on structure alone has clear limitations, principled structural analysis constitutes the core of a methodology for reasoning about decision models.

Decision Trees↗

PubMed1991

Application of portfolio theory in decision tree analysis.

A general application of portfolio analysis for herd decision tree analysis is described. In the herd environment, this methodology offers a means of employing population-based decision strategies that can help the producer control economic variation in expected return from a given set of decision options. An economic decision tree model regarding the use of prostaglandin in dairy cows with undetected estrus was used to determine the expected return of the decisions to use prostaglandin and breed on a timed basis, use prostaglandin and then breed on sign of estrus, or breed on signs of estrus. The risk attributes of these decision alternatives were calculated from the decision tree, and portfolio theory was used to find the efficient decision combinations (portfolios with the highest return for a given variance). The resulting combinations of decisions could be used to control return variation.

Animals↗

PubMed1986

Epidemiologic programs for computers and calculators. Decision-tree analysis using a microcomputer.

Decision analysis using decision trees enables the medical decision maker to simplify and solve complex problems. The technique, however, becomes cumbersome when the decision maker attempts to perform sensitivity analysis on the problem. This paper describes a computer program, written in PASCAL, to construct, modify, and perform deterministic and stochastic simulations and risk analysis on a hypothetical, decision-analysis problem. The deterministic version can be run on microcomputers with CP/M, PC-DOS, or MS-DOS operating systems, and the deterministic/stochastic version requires a PC-DOS or MS-DOS operating system.

Computers↗

PubMed1988

A decision tree for early differentiation between obstructive and non-obstructive jaundice.

We present a method for early differentiation between obstructive and non-obstructive jaundice. On the basis of 14 variables (clinical data and clinical chemical tests, all available within 48 h) a simple decision tree or flow chart has been constructed. The diagnostic yield was as follows: 857 of 982 consecutive jaundiced patients (87%) in a data base and 98 of 108 patients in an independent test sample (91%) were correctly classified. Decision trees for the differentiation between benign or malignant causes within the obstructive group and between acute or chronic causes within the non-obstructive group are also presented. The resulting four-way classification was correct for 77% of the patients in the data base and for 72% of the patients in the test sample. The decision trees are compared with previous methods founded on Bayes' rule and logistic discrimination. The decision trees enable a quick and reliable classification of jaundiced patients, thus providing a valid basis for rational planning of the further diagnostic study.

Cholestasis↗

PubMedDate not supplied

An evaluation of the decision tree approach for assessing priorities for safety testing of food additives.

In this publication we report an evaluation of the decision tree scheme of Cramer, Ford and Hall (1978) for assigning priorities for toxicity testing of chemicals. The original scheme has been modified to allow more chemical structures to be considered and to take into account recent advances in toxicology. The majority of the food additives permitted in either the UK, USA or Canada have been processed through the modified decision tree questionnaire and their classification compared with currently available chronic toxicity data. A large proportion of the additives (53/73) assigned to the lowest toxicity (I) class have a low order of chronic oral toxicity as do many of the compounds assigned to the moderate toxicity (II) class. Although the majority of the additives assigned to the highest toxicity (III) class are substantially more toxic than those in the lower toxicity classes, some relatively innocuous compounds reached this classification. In addition, a few toxic compounds were assigned to the lowest toxicity class. The reasons for these incorrect assignments are discussed. It was concluded that the decision tree approach, although less discriminating than originally suggested, remains a useful method for classifying compounds in terms of their probable toxicity and that further modifications to the tree could be made.

Accident Prevention↗

PubMed1986

When is a diagnostic test result positive? Decision tree models based on net utility and threshold.

The question "When is a diagnostic test result positive?" can be addressed by clinical decision analysis. We developed two simple decision tree models for selecting appropriate cutoff levels: a net utility model and a threshold model. These models have been incorporated in a software program for desktop computers. We believe it is important for investigators to provide raw data on test performance, for three reasons. First, these data can be used in simple decision tree models to identify "appropriate" cutoff levels. Second, they can be used to evaluate empiric cutoff levels or decision rules. Third, they can be used to evaluate optimal cutoff levels for detailed decision trees depicting specific clinical problems.

Cost-Benefit Analysis↗

PubMedDate not supplied

Use of a decision tree to improve accuracy of diagnosis.

Thirty triads of nurses matched for educational background, length of experience, and previous performance were studied to determine if use of a decision tree would improve diagnostic accuracy. One experimental and two control groups were given a written case study and asked to list all possible diagnoses that could cause the change in behavior exhibited by the patient. Experimental group nurses were given a set of decision trees to enable them to use the information systematically to determine if characteristics of each condition were present. Significant improvement in diagnostic accuracy was shown by nurses who used the decision trees.

Decision Making↗

PubMedDate not supplied

A thrombolytic decision tree.

We constructed a decision analysis model based on data in the medical literature to estimate the possible outcomes of thrombolytic therapy in patients 50 to 80 years old with possible myocardial infarction. We used the model to test the most likely effects of treatment (determined by averaging the values in reports of large studies) and the worst effects reported so far. The program begins by asking the patient's age, the hours from the onset of pain, and the probability of acute myocardial infarction. It then provides an opportunity to perform sensitivity analyses by changing the values for these variables and for the probability of death in the absence of thrombolytic therapy, as well as for the probability of major stroke and hemorrhage. The counterintuitive findings observed with this program are that the benefits of thrombolytic therapy increase with age and that young patients derive surprisingly little benefit from it.

Age Factors↗

PubMedDate not supplied

Probabilistic analysis of decision trees using symbolic algebra.

Uncertainty in medical decision making techniques occurs in the specification of both decision tree probabilities and utilities. Using a computer-based algebraic approach, methods for modeling this uncertainty have been formulated. This analytic procedure allows an exact calculation of the statistical variance at the final decision node using automated symbolic manipulation. Confidence and conditional confidence levels for the preferred decision are derived from gaussian theory, and the mutual information index that identifies probabilistically important tree variables is provided. The computer-based algebraic method is illustrated for a problem previously analyzed by Monte Carlo simulation. This methodology provides the decision analyst with a procedure to evaluate the outcome of specification uncertainty, in many decision problems, without resorting to Monte Carlo analysis.

Adult↗

PubMed1989

Decision-tree analysis of treatment alternatives for left displaced abomasum.

This study evaluates treatment alternatives for left displaced abomasum (LDA) in dairy cattle. The technique of decision-tree analysis was used to evaluate 3 treatment possibilities. A computerized spreadsheet decision tree was used for the evaluation. Results of this investigation suggested that surgical treatment had the highest expected monetary value. Closed surgical techniques had expected monetary values close to the surgical techniques. Rolling the cow had the lowest expected monetary value, but the expected monetary value for rolling was higher than the expected monetary value for selling. If an LDA recurs after treatment, surgical and closed surgical treatments are preferred over rolling. From this study, we can conclude that surgical and closed surgical treatment alternatives are preferred if expected monetary values are considered. Rolling is preferred over selling the cow. However, if an LDA recurs, selling the cow is preferred over rolling.

Abomasum↗

PubMed1986

Applying decision analysis in therapeutic drug monitoring: using decision trees to interpret serum theophylline concentrations.

In the second of a three-part series, decision analysis is applied to therapeutic drug monitoring decisions that affect individual patients, using theophylline concentration and toxicity data to illustrate general concepts. Likelihood ratios and conditional probability curves were developed. The curves were used to determine the probability of toxicity based on the clinician's assessment of patient status and a measured serum theophylline concentration. A decision tree to "rule in" or "rule out" toxicity was constructed. Selection of a serum concentration cutoff level for classifying patients as toxic or nontoxic depends on the probabilities of the possible outcomes of the decision process and the clinician's assessment of the value of each possible outcome; therefore, no single concentration value is best for classifying patients. A decision tree was also constructed for evaluating therapeutic options when the clinician is confronted with adverse effects that may be drug related. In three prototype cases, the expected utility for discontinuing theophylline, continuing the drug at the same dosage, or lowering the dosage was determined and used to evaluate the relative worth of each therapeutic option. A more comprehensive interpretation of the serum theophylline concentration is provided by decision-analysis techniques than by observation of pharmacologic effect alone, because other factors such as merit, risk, and consequences of alternative decisions are considered.

Decision Theory↗

PubMed1992

Well differentiated follicular neoplasms of the thyroid: reproducibility and validity of a 'decision tree' classification based on nucleolar and karyometric features.

This study was conducted on fine-needle aspirates of well differentiated follicular neoplasms of the thyroid. A 'decision tree' classification based on the percentage of nucleolated nuclei, percentage of nuclei with two or more nucleoli and mean major nuclear diameter was adopted. We observed that the reproducibility and the validity of the follicular adenoma vs follicular carcinoma discrimination are greater than in the subjective evaluation. Moreover, similar classification results were obtained when measurements were performed either with a fully automated image analysis system or with semiautomatic or manual instrumentation. As for reproducibility of the inter-instrument comparisons, the k statistic values ranged from 0.85 to 1.00 (mean value 0.90, that is, an 'almost perfect' degree of agreement); in the subjective evaluations, the inter-observer comparisons showed values ranging from 0.20 to 0.56 (mean value 0.37, that is, 'fair'). In the decision tree classification, feature value thresholds were selected in order to have specificity of 100% and the predictive value of a positive result (carcinoma) of 100%; accuracy was 87% (range 86-89%), sensitivity 74% (71-79%), the predictive value of a negative result (adenoma) 79% (78-82%). In the subjective evaluation the values were as follows: accuracy 67% (64-71%), sensitivity 57% (50-64%), specificity 77% (71-79%), predictive value of a negative result (adenoma) 64% (61-69%), predictive value of a positive result (carcinoma) 71% (67-75%). The conclusion is that, by using a routine microscope equipped with a micrometer, the preoperative diagnosis of follicular carcinoma from smears can be formulated with a high degree of certainty.

Adenocarcinoma↗

PubMed1988

Development and testing of a decision tree for blunt trauma.

The aim of the present study was to examine the essential problems in a retrospective study of 381 organ injuries in 260 patients, to identify problems, to define criteria, to describe decision rules, and to organize these rules into branch-chain decision trees or clinical algorithms. The basic hypothesis of this study is that criteria organized into a prioritized decision tree can provide objective standards to evaluate the quality of trauma care and to compare alternative approaches. The algorithm was designed to provide prompt therapy for the most life-threatening problems: respiratory and cardiac arrest, shock, head injury, tamponade, lacerations of the great vessels, cardiac contusion, ruptured parenchymal organs, lacerated viscera, and injury to other intraperitoneal organs. Resuscitation from shock, correction of circulatory problems, and monitoring of physiologic variables were prioritized to evaluate the presence of circulatory deficits and the adequacy of specific therapy to correct them. Concomitantly, diagnosis of the underlying problems was approached using peritoneal lavage, abdominal and chest x-rays, iv urograms, cystograms, endoscopy, upper and lower GI barium or hypaque studies, ultrasound, scintograms, and CT scans. In emergency conditions these are limited to a large extent by time factors. The diagnostic accuracy, priorities, and limitations of each of these were evaluated in emergency conditions. The algorithm was used to track management decisions in a prospective series; the mortality of 51 patients with satisfactory compliance was 4% and 44% in nine patients with major deviations from the algorithm.

Adult↗

PubMed1992

Decision tree for the management of substance-abusing psychiatric patients.

This paper describes a short-term approach developed at a time of numerous drug-related incidents occurring at a large VA Hospital to help staff manage psychiatric patients abusing alcohol or drugs during hospitalization. This was accomplished through the development of a decision tree designed to improve the clinical problem-solving process by identifying key decision points. Prior to this, staff responded emotionally either by prematurely discharging patients or by not recognizing the problem at all. Decision Trees have widespread applicability for resolving complex clinical problems.

Adult↗

PubMed1983

A revised decision tree for the DSM-III differential diagnosis of psychotic patients.

Clinician-researchers involved in developing DSM-III found that the decision tree for the differential diagnosis of psychotic features that appears in the manual does not accurately or clearly represent the logic of the classification. They present a revised decision tree that is more accurate, simpler, and clearer and that they believe will greatly facilitate the tree's use in teaching and clinical practice. A revised criterion for major depressive episode and manic episode (criterion C), which was published in the third and subsequent printings of DSM-III, is also discussed.

Bipolar Disorder↗