By Katarzyna A. Tarnowska, Zbigniew W. Ras, Pawel J. Jastreboff
The e-book provides a data discovery established method of construct a recommender method assisting a doctor in treating tinnitus sufferers with the hugely profitable strategy referred to as Tinnitus Retraining Therapy.
It describes experiments on extracting novel wisdom from the ancient dataset of sufferers taken care of via Dr. P. Jastreboff in order that to higher comprehend components at the back of therapy's effectiveness and higher customize remedies for various profiles of patients.
The e-book is a reaction for a starting to be call for of a complicated information analytics within the healthcare so that it will supply larger care with the information pushed decision-making solutions.
The strength monetary advantages of utilising automatic scientific choice aid structures comprise not just more suitable potency in overall healthiness care supply (by lowering charges, bettering caliber of care and sufferer safety), but additionally enhancement in treatment's standardization, objectivity and availability in locations of scarce expert's wisdom in this tough to regard listening to disorder.
Furthermore, defined technique should be utilized in evaluate of the medical effectiveness of evidence-based intervention of varied proposed remedies for tinnitus.
Read or Download Decision Support System for Diagnosis and Treatment of Hearing Disorders: The Case of Tinnitus PDF
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Extra info for Decision Support System for Diagnosis and Treatment of Hearing Disorders: The Case of Tinnitus
5 algorithm) and random forests, • rule induction (similar to extracting decision rules, built, for example, on RIPPER algorithm). The last method, based on rule induction has two advantages over other learning methods. First, the rules can serve as a basis for generating explanations for the system’s recommendations. Second, existing prior domain knowledge can be incorporated in the models. 3 Example Applications Considering industrial adoption, content-based systems are rarely found in commercial environments.
In other words, the treatment process is not repeatable, as in case of buying books or CDs. ). Thus, it cannot be expressed as easily as preference rating given by the users of an online bookstore, for example. Furthermore, the size of dataset is not large enough (about 550 patients) to find users similar to the one under consideration (this problem is known as startup problem). For these reasons and given the specific knowledge domain (protocol of TRT treatment), collaborative filtering would be less suitable approach for a RS for tinnitus.
Movies, music, books), linguistic knowledge about items’ descriptions comes from the WordNet lexical ontology. SEWeP (Semantic Enhancement for Web Personalization), a Web personalization system, uses logs and the semantics of a Website’s content, and also WordNet to “interpret” the content of an item by using word sense disambiguation. Quickstep, a system for the recommendation of on-line academic research papers, adopts a research paper topic ontology based on the computer science classifications.