Department of Computing, Engineering & Technology, University of Sunderland, St Peter's Way, Sunderland SR6 0DD, UK.
BMC Med Inform Decis Mak. 2012 Apr 30;12 Suppl 1(Suppl 1):S6. doi: 10.1186/1472-6947-12-S1-S6.
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.
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 (χ(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.
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.
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.
本文描述了对英国米德尔斯堡詹姆斯库克大学医院听力诊所收集的超过 23000 名患者的 180000 多份患者记录的数据库进行的分析。这些记录包括听力图（患者在六个不同音高上能听到的最微弱声音的图表）、分类数据（如年龄、性别、诊断和助听器类型）和由技术人员记录的简短自由文本注释。挖掘这些数据的目的是确定哪些因素有助于决定为患者配备耳背式（戴在耳后）助听器还是耳内式（戴在耳内）助听器。
通过主成分分析（PCA）确定了四种主要的听力图类型，并将其与所选助听器的类型相关联。年龄、性别、诊断、掩蔽器、模具和个体听力图频率的影响通过逻辑回归组合成一个单一的模型。还通过卡方检验（χ(2)）在自由文本字段中发现了一些重要的关键字，该检验也可用于模型中。最终模型可以作为决策支持工具，帮助决定是否为个体患者提供耳背式或耳内式助听器。
最终模型使用 5 折交叉验证进行了测试，能够以 0.79 到 0.87 的精度复制听力学家决定为患者配备耳内式或耳背式助听器的决策。
制作了一个决策支持系统来预测应开具的助听器类型，并提供了解释功能，说明如何做出该决策。该系统应有助于为听力学家提供“第二意见”。
