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MeSH Review

Decision Trees

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Disease relevance of Decision Trees

  • The proposed "decision tree" classifies subjects into five subgroups with different risks of GAC and peptic ulcer, based on the information of age, serum pepsinogen and gastrin levels [1].
  • Computerized decision-tree analysis and simulation modeling were used to evaluate control and eradication strategies for pseudorabies virus (PRV) in swine [2].
  • OBJECTIVE: To establish whether a decision tree based on a combination of clinical, morphological and biochemical parameters could be constructed to help in the selection of women with tubal ectopic pregnancies for expectant management [3].
  • In the application to four protease datasets (Trypsin, Factor Xa, Hepatitis C Virus and HIV protease cleavage site prediction), our algorithm is superior to C5, a conventional method for deriving decision trees [4].
  • Based on the International Life Science Institute (ILSI) risk decision tree, the methods are discussed and three scenarios are suggested: (i) testing for a well-known allergen; (ii) testing for a well-known allergen, but with no previous history of food allergy; and (iii) testing for unknown allergens and cross-reactivity with known allergens [5].

Psychiatry related information on Decision Trees

  • The Decision Tree and Clinical Paths for Assessment and Management of ADHD identify the critical components of care through a stepwise decision-making process involving the assessment, diagnosis, treatment, and outcome evaluation of children who present with ADHD symptomatology [6].

High impact information on Decision Trees

  • As an illustration, a decision tree constructed using data from Col-0 during diurnal changes and a prolonged dark treatment was used to show that, irrespective of the time of harvest during the diurnal cycle, the pgm mutant resembles a wild-type plant that has been exposed to a 3 d prolongation of the night [7].
  • A decision tree algorithm identified classifiers for response to celecoxib with relatively high sensitivity but moderate to low specificity [8].
  • Diagnostic and therapeutic challenges are highlighted, and a decision tree to guide treatment in patients with early or established RA is provided [9].
  • METHODS: The decision tree simulated a 25-year-old HD patient who relapsed less than 12 months after mechlorethamine, vincristine, procarbazine, and prednisone (MOPP) chemotherapy [10].
  • This review presents a decision tree analysis of risk of progression to IDDM, highlights the different prognosis of markers when applied to those with and without a family history of the disease, and proposes a strategy for disease prediction in the latter [11].

Biological context of Decision Trees

  • An evaluation of the decision tree format of the American College of Rheumatology 1987 classification criteria for rheumatoid arthritis: performance over five years in a primary care-based prospective study [12].
  • Of the 47 quantitative variables, the decision tree technique retained 8: i.e., 2 tissue architecture-, 2 DNA ploidy level-, 2 morphonuclear-, 1 lectin histochemical-, and 1 vimentin immunostain-related variables [13].
  • The decision tree, consisting of the FT and the Lesch Typology Questionnaire, distinguishes four subgroups of nicotine-dependent persons [14].
  • A classification and regression decision tree with the same variables predicted AUR (AUC = 0.74, sensitivity = 72%, specificity = 67%) as well as did a tree with PSA alone (AUC = 0.70, sensitivity = 75%, specificity = 64%) [15].
  • The decision tree is readily interpretable from a clinical perspective and uses five out of the seven predictors (in descending hierarchical order: ever had asthma, current asthma, shortness of breath, atopy and wheezing and breathless) [16].

Associations of Decision Trees with chemical compounds

  • METHODS: A decision tree was used to assess the cost and the expected quality-adjusted years of life (QALY) after treatment with either low-molecular-weight heparin or warfarin, based on pooled data from six published trials [17].
  • A decision tree was devised to compare the costs per patient of two different strategies: (a) systematic performance of post-treatment urea breath test and new treatment if positive; and (b) clinical follow-up, 13C-urea breath test if dyspeptic symptoms recurred and eradication treatment if the test was positive [18].
  • In comparison to the accuracy of the differentiation after calcium and oxalate of 80% and 75.6%, respectively, the decision tree showed an accuracy of 97.8% [19].
  • RESULTS: Six competing strategies (MTZ trial, CIP trial, MTZ-then-CIP trial, CIP-then-MTZ trial, pouch endoscopy with biopsy, and pouch endoscopy without biopsy) were modeled in a decision tree [20].
  • DATA SOURCES: Conditional probabilities for the decision trees were derived from cholesterol distributions in national population-based surveys [21].

Gene context of Decision Trees

  • OBJECTIVE: To elaborate a clinical practice decision tree for the choice of the first disease modifying antirheumatic drug (DMARD) for untreated rheumatoid arthritis of less than six months' duration [22].
  • Compared to the independent RP models (obtained by considering each activity separately), the PUMP-RP decision trees provide easier identification and interpretation of those descriptors that are common to both COX-1 and COX-2 activities [23].
  • In the decision tree, only four antibodies (alpha CK7, alpha CEA, alpha ER, and alpha GCDFP-15) were used to obtain a correct classification score of 89% for the learning set and 84% for the test set [24].
  • A decision tree was designed to simulate, over 12 months, the clinical and economic outcomes of patients newly started on warfarin associated with two alternatives: (1) no genotyping (non-genotyped group) and (2) CYP2C9 genotyping prior to initiation of warfarin therapy (genotyped group) [25].
  • Preliminary field testing supports the clinical utility and validity of the ADHD Decision Tree/Paths [6].

Analytical, diagnostic and therapeutic context of Decision Trees

  • CONCLUSION: SELDI-TOF-MS technology allows rapid analysis of many serum samples, and use of decision tree boosting analysis as the main statistical method allowed us to propose a pattern of protein peaks specific for RA [26].
  • STUDY DESIGN AND METHODS: A decision tree was used to compare a hypothetical strategy of no epoetin with one in which epoetin was utilized to control blood transfusion needs in CABG; each strategy was tested with and without ABD [27].
  • MLMs usually represent either rules that can be encoded, such as generating a warning that the potassium concentration is decreasing in a patient taking digoxin, or complex decision trees for individual patient care plans and clinical protocols [28].
  • The decision tree in this article helps the critical care nurse to minimize or prevent complications of shunting from nitroprusside therapy [29].
  • During strict application of this decision tree to 1429 consecutive hepatectomies, of which 685 were performed on HCC patients, during the last 10 years, we encountered only a single mortality [30].


  1. A novel tree-structured analysis for non-invasive diagnosis of gastric adenocarcinoma. Wu, M.S., Lee, W.C., Lin, J.T., Wang, H.P., Wang, T.H., Chen, C.J. Anticancer Res. (1995) [Pubmed]
  2. Financial analysis of pseudorabies control and eradication in swine. Rodrigues, C.A., Gardner, I.A., Carpenter, T.E. J. Am. Vet. Med. Assoc. (1990) [Pubmed]
  3. Expectant management of tubal ectopic pregnancy: prediction of successful outcome using decision tree analysis. Elson, J., Tailor, A., Banerjee, S., Salim, R., Hillaby, K., Jurkovic, D. Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology. (2004) [Pubmed]
  4. Searching for discrimination rules in protease proteolytic cleavage activity using genetic programming with a min-max scoring function. Yang, Z.R., Thomson, R., Hodgman, T.C., Dry, J., Doyle, A.K., Narayanan, A., Wu, X. BioSystems (2003) [Pubmed]
  5. In-vitro diagnosis: serum-based methods used for risk assessment of allergenic food. Poulsen, L.K. Current opinion in allergy and clinical immunology. (2001) [Pubmed]
  6. A decision tree and clinical paths for the assessment and management of children with ADHD. Magyary, D., Brandt, P. Issues in mental health nursing. (2002) [Pubmed]
  7. A Robot-based platform to measure multiple enzyme activities in Arabidopsis using a set of cycling assays: comparison of changes of enzyme activities and transcript levels during diurnal cycles and in prolonged darkness. Gibon, Y., Blaesing, O.E., Hannemann, J., Carillo, P., Höhne, M., Hendriks, J.H., Palacios, N., Cross, J., Selbig, J., Stitt, M. Plant Cell (2004) [Pubmed]
  8. Serum proteomic profiles suggest celecoxib-modulated targets and response predictors. Xiao, Z., Luke, B.T., Izmirlian, G., Umar, A., Lynch, P.M., Phillips, R.K., Patterson, S., Conrads, T.P., Veenstra, T.D., Greenwald, P., Hawk, E.T., Ali, I.U. Cancer Res. (2004) [Pubmed]
  9. New concepts in the treatment of rheumatoid arthritis. Goldbach-Mansky, R., Lipsky, P.E. Annu. Rev. Med. (2003) [Pubmed]
  10. The optimal timing of autologous bone marrow transplantation in Hodgkin's disease patients after a chemotherapy relapse. Desch, C.E., Lasala, M.R., Smith, T.J., Hillner, B.E. J. Clin. Oncol. (1992) [Pubmed]
  11. Can we really predict IDDM? Bingley, P.J., Bonifacio, E., Gale, E.A. Diabetes (1993) [Pubmed]
  12. An evaluation of the decision tree format of the American College of Rheumatology 1987 classification criteria for rheumatoid arthritis: performance over five years in a primary care-based prospective study. Lunt, M., Symmons, D.P., Silman, A.J. Arthritis Rheum. (2005) [Pubmed]
  13. The contribution of image cytometry and artificial intelligence-related methods of numerical data analysis for adipose tumor histopathologic classification. Goldschmidt, D., Decaestecker, C., Berthe, J.V., Gordower, L., Remmelink, M., Danguy, A., Pasteels, J.L., Salmon, I., Kiss, R. Lab. Invest. (1996) [Pubmed]
  14. The Austrian multicentre study on smoking: subgroups of nicotine dependence and their craving. Lesch, O.M., Dvorak, A., Hertling, I., Klingler, A., Kunze, M., Ramskogler, K., Saletu-Zyhlarz, G., Schoberberger, R., Walter, H. Neuropsychobiology (2004) [Pubmed]
  15. Clinical predictors of spontaneous acute urinary retention in men with LUTS and clinical BPH: a comprehensive analysis of the pooled placebo groups of several large clinical trials. Roehrborn, C.G., Malice, M., Cook, T.J., Girman, C.J. Urology (2001) [Pubmed]
  16. Classification methods for the identification of 'case' in epidemiological diagnosis of asthma. Grassi, M., Villani, S., Marinoni, A. Eur. J. Epidemiol. (2001) [Pubmed]
  17. Low-molecular-weight heparin versus warfarin for secondary prophylaxis of venous thromboembolism: a cost-effectiveness analysis. Marchetti, M., Pistorio, A., Barone, M., Serafini, S., Barosi, G. Am. J. Med. (2001) [Pubmed]
  18. Diagnosis of Helicobacter pylori after triple therapy in uncomplicated duodenal ulcers--a cost-effectiveness analysis. Gené, E., Calvet, X., Azagra, R. Aliment. Pharmacol. Ther. (2000) [Pubmed]
  19. Analysis of calcium, oxalate, and citrate interaction in idiopathic calcium urolithiasis in children. Milosević, D., Batinić, D., Konjevoda, P., Blau, N., Stambuk, N., Nizić, L., Vrljicak, K., Batinić, D. Journal of chemical information and computer sciences. (2003) [Pubmed]
  20. A cost-effectiveness analysis of diagnostic strategies for symptomatic patients with ileal pouch-anal anastomosis. Shen, B., Shermock, K.M., Fazio, V.W., Achkar, J.P., Brzezinski, A., Bevins, C.L., Bambrick, M.L., Remzi, F.H., Lashner, B.A. Am. J. Gastroenterol. (2003) [Pubmed]
  21. Designing a simpler high blood cholesterol case detection strategy: are the advantages of the NCEP protocol worth the complexity? Hofer, T., Weissfeld, J. Medical decision making : an international journal of the Society for Medical Decision Making. (1994) [Pubmed]
  22. Clinical practice decision tree for the choice of the first disease modifying antirheumatic drug for very early rheumatoid arthritis: a 2004 proposal of the French Society of Rheumatology. Le Loët, X., Berthelot, J.M., Cantagrel, A., Combe, B., De Bandt, M., Fautrel, B., Flipo, R.M., Lioté, F., Maillefert, J.F., Meyer, O., Saraux, A., Wendling, D., Guillemin, F. Ann. Rheum. Dis. (2006) [Pubmed]
  23. Partially unified multiple property recursive partitioning (PUMP-RP) analyses of cyclooxygenase (COX) inhibitors. Rao, S.N., Stockfisch, T.P. Journal of chemical information and computer sciences. (2003) [Pubmed]
  24. Immunohistochemical differentiation between primary adenocarcinomas of the ovary and ovarian metastases of colonic and breast origin. Comparison between a statistical and an intuitive approach. Lagendijk, J.H., Mullink, H., van Diest, P.J., Meijer, G.A., Meijer, C.J. J. Clin. Pathol. (1999) [Pubmed]
  25. The potential clinical and economic outcomes of pharmacogenetics-oriented management of warfarin therapy - a decision analysis. You, J.H., Chan, F.W., Wong, R.S., Cheng, G. Thromb. Haemost. (2004) [Pubmed]
  26. Discovery of new rheumatoid arthritis biomarkers using the surface-enhanced laser desorption/ionization time-of-flight mass spectrometry ProteinChip approach. de Seny, D., Fillet, M., Meuwis, M.A., Geurts, P., Lutteri, L., Ribbens, C., Bours, V., Wehenkel, L., Piette, J., Malaise, M., Merville, M.P. Arthritis Rheum. (2005) [Pubmed]
  27. Cost-effectiveness of epoetin and autologous blood donationin reducing allogeneic blood transfusions incoronary artery bypass graft surgery. Marchetti, M., Barosi, G. Transfusion (2000) [Pubmed]
  28. Arden Syntax: the emerging standard language for representing medical knowledge in computer systems. Poikonen, J. American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists. (1997) [Pubmed]
  29. Preventing pulmonary shunting complications from nitroprusside: using a decision tree. Gamble, J., Hamilton, P. Dimensions of critical care nursing : DCCN. (1995) [Pubmed]
  30. Assessment of hepatic reserve for indication of hepatic resection: decision tree incorporating indocyanine green test. Imamura, H., Sano, K., Sugawara, Y., Kokudo, N., Makuuchi, M. Journal of hepato-biliary-pancreatic surgery. (2005) [Pubmed]
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