Bayesian Diagnostic Probabilities without Assuming Independence of Symptoms

Gammerman, Alexander; Thatcher, A. R.

doi:10.1055/s-0038-1634813

Cited by 37 publications

(29 citation statements)

References 6 publications

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“…Similarly, in a study comparing classifiers for predicting breast cancer recurrence, Mani, Pazzani & West (1997) found that the independence Bayes model did best. Moreover, in studies of heart disease (Russek, Kronmal & Fisher, 1983), thyroid disease (Nordyke, Kulikowski & Kulikowski, 1971), liver disease (Croft & Machol, 1987), abdominal pain (Gammerman & Thatcher, 1991;Todd & Stamper, 1994;Ohmann et al, 1996), and dyspepsia (Fox, Barber & Bardhan, 1980) the independence model was a good choice. The phenomenon is not limited to medicine.…”

Section: Why the Assumption Is Not So Absurdmentioning

confidence: 98%

“…However, despite writing as late as 1989, he claimed that Bayes formula assumes that the 'indicants are independent'. Gammerman & Thatcher (1991) compared the independence Bayes model with a model which included some symptom combinations and also the CART algorithm. Using error rate on an independent test set to assess the results, they found that the independence Bayes yielded best performance.…”

Section: For Details) One Interesting Observation Made In This Studymentioning

confidence: 99%

See 1 more Smart Citation

Idiot's Bayes: Not So Stupid after All?

Hand¹,

Yu²

2001

International Statistical Review / Revue Internationale de Stat

277

295

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JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. International Statistical Institute (ISI) is collaborating with JSTOR to digitize, preserve and extend access to International Statistical Review / Revue Internationale de Statistique. 2University of Plymouth, UK Summary Folklore has it that a very simple supervised classification rule, based on the typically false assumption that the predictor variables are independent, can be highly effective, and often more effective than sophisticated rules. We examine the evidence for this, both empirical, as observed in real data applications, and theoretical, summarising explanations for why this simple rule might be effective.

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Section: Why the Assumption Is Not So Absurdmentioning

confidence: 98%

Section: For Details) One Interesting Observation Made In This Studymentioning

confidence: 99%

Idiot's Bayes: Not So Stupid after All?

Hand¹,

Yu²

2001

International Statistical Review / Revue Internationale de Stat

277

295

View full text Add to dashboard Cite

show abstract

“…One can assess the joint likelihood ratio of a set of dependent clues from the following formula [26][27][28]:…”

Section: Methodsmentioning

confidence: 99%

Probabilistic master lists: integration of patient records from different databases when unique patient identifier is missing

Alemi

Loaiza

Vang

2006

Health Care Manage Sci

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We show how Bayesian probability models can be used to integrate two databases, one of which does not have a key for uniquely identifying clients (e.g., social security number or medical record number). The analyst selects a set of imperfect identifiers (last visit diagnosis, first name, etc.). The algorithm assesses the likelihood ratio associated with the identifier from the database of known cases. It estimates the probability that two records belong to the same client from the likelihood ratios. As it proceeds in examining various identifiers, it accounts for inter-dependencies among them by allowing overlapping and redundant identifiers to be used. We test that the procedure is effective by examining data from the Medical Expenditure Panel Survey (MEPS) Population Characteristics data set, a publicly available data set. We randomly selected 1,000 cases for training data set--these constituted the known cases. The algorithm was used to identify if 100 cases not in the training data set would be misclassified in terms of being a case in the training set or a new case. With 12 fields as identifiers, all 100 cases were correctly classified as new cases. We also selected 100 known cases from the training set and asked the algorithm to classify these cases. Again, all 100 cases were correctly classified. Less accurate results were obtained when the training data set was too small (e.g., less than 100 records) or the number of fields used as identifiers was too small (e.g., less than seven fields). In a test of performance of the algorithm, when the ratio of testing to training data set exceeds 4 to 1, the accuracy of the algorithm exceeded 90% of cases. As the ratio increases, the accuracy of algorithm improves further. These data suggest the accuracy of our automated and mathematical procedure to merge data from two different data sets without the presence of a unique identifier. The algorithm uses imperfect and overlapping clues to re-identify cases from information not typically considered to be a patient identifier.

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“…We use abdominal pain dataset, it has 9 kinds of diagnosis as labels and 33 types of symptoms as object [7,10], which are sex, age, pain-site onset, painsite present, aggravating factors, relieving factors, progress of pain, duration of pain, type of pain, severity, nausea, vomiting, anorexia, indigestion, jaundice, bowel habit, micturition, previous pain, previous surgery…”

Section: Data Descriptionmentioning

confidence: 99%

Feature Selection by Conformal Predictor

Yang

Nouretdinov

Luo

et al. 2011

IFIP Advances in Information and Communication Technology

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Abstract. In this work we consider the problem of feature selection in the context of conformal prediction. Unlike many conventional machine learning methods, conformal prediction allows to supply individual predictions with valid measure of confidence. The main idea is to use confidence measures as an indicator of usefulness of different features: we check how many features are enough to reach desirable average level of confidence. The method has been applied to abdominal pain data set. The results are discussed.

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Bayesian Diagnostic Probabilities without Assuming Independence of Symptoms

Cited by 37 publications

References 6 publications

Idiot's Bayes: Not So Stupid after All?

Idiot's Bayes: Not So Stupid after All?

Probabilistic master lists: integration of patient records from different databases when unique patient identifier is missing

Feature Selection by Conformal Predictor

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