BMC Med Inform Decis Mak - Data mining of audiology patient records: factors influencing the choice of hearing aid type.

Tópicos

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Resumo

CKGROUND: This paper describes the analysis of a database of over 180,000 patient records, collected from over 23,000 patients, by the hearing aid clinic at James Cook University Hospital in Middlesbrough, UK. These records consist of audiograms (graphs of the faintest sounds audible to the patient at six different pitches), categorical data (such as age, gender, diagnosis and hearing aid type) and brief free text notes made by the technicians. This data is mined to determine which factors contribute to the decision to fit a BTE (worn behind the ear) hearing aid as opposed to an ITE (worn in the ear) hearing aid.METHODS: From PCA (principal component analysis) four main audiogram types are determined, and are related to the type of hearing aid chosen. The effects of age, gender, diagnosis, masker, mould and individual audiogram frequencies are combined into a single model by means of logistic regression. Some significant keywords are also discovered in the free text fields by using the chi-squared ((2)) test, which can also be used in the model. The final model can act a decision support tool to help decide whether an individual patient should be offered a BTE or an ITE hearing aid.RESULTS: The final model was tested using 5-fold cross validation, and was able to replicate the decisions of audiologists whether to fit an ITE or a BTE hearing aid with precision in the range 0.79 to 0.87.CONCLUSIONS: A decision support system was produced to predict the type of hearing aid which should be prescribed, with an explanation facility explaining how that decision was arrived at. This system should prove useful in providing a "second opinion" for audiologists.

Resumo Limpo

ckground paper describ analysi databas patient record collect patient hear aid clinic jame cook univers hospit middlesbrough uk record consist audiogram graph faintest sound audibl patient six differ pitch categor data age gender diagnosi hear aid type brief free text note made technician data mine determin factor contribut decis fit bte worn behind ear hear aid oppos ite worn ear hear aidmethod pca princip compon analysi four main audiogram type determin relat type hear aid chosen effect age gender diagnosi masker mould individu audiogram frequenc combin singl model mean logist regress signific keyword also discov free text field use chisquar test can also use model final model can act decis support tool help decid whether individu patient offer bte ite hear aidresult final model test use fold cross valid abl replic decis audiologist whether fit ite bte hear aid precis rang conclus decis support system produc predict type hear aid prescrib explan facil explain decis arriv system prove use provid second opinion audiologist

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