Case-based approach to managing angle closure glaucoma with anterior segment imaging

Rosenblum, Hannah; Radcliffe, Nathan M.

doi:10.1016/j.jcjo.2014.10.004

Cited by 6 publications

(3 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Some, such as the above, are based exclusively on expert knowledge. Others, however, learn from previous problems (casebased systems) [37,38] or are representations (e.g. decision trees) that support decision-making (model-based systems) [39].…”

Section: Decision Support Systems In Medicinementioning

confidence: 99%

Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

Aljawarneh

Anguera

Atwood

et al. 2019

J Wireless Com Network

View full text Add to dashboard Cite

Nowadays, large amounts of data are generated in the medical domain. Various physiological signals generated from different organs can be recorded to extract interesting information about patients’ health. The analysis of physiological signals is a hard task that requires the use of specific approaches such as the Knowledge Discovery in Databases process. The application of such process in the domain of medicine has a series of implications and difficulties, especially regarding the application of data mining techniques to data, mainly time series, gathered from medical examinations of patients. The goal of this paper is to describe the lessons learned and the experience gathered by the authors applying data mining techniques to real medical patient data including time series. In this research, we carried out an exhaustive case study working on data from two medical fields: stabilometry (15 professional basketball players, 18 elite ice skaters) and electroencephalography (100 healthy patients, 100 epileptic patients). We applied a previously proposed knowledge discovery framework for classification purpose obtaining good results in terms of classification accuracy (greater than 99% in both fields). The good results obtained in our research are the groundwork for the lessons learned and recommendations made in this position paper that intends to be a guide for experts who have to face similar medical data mining projects.

show abstract

Section: Decision Support Systems In Medicinementioning

confidence: 99%

Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

Aljawarneh

Anguera

Atwood

et al. 2019

J Wireless Com Network

View full text Add to dashboard Cite

show abstract

“…Das OCT kann dazu eingesetzt werden, den vorderen Augenabschnitt und sogar den Kammerwinkel darzustellen und zu vermessen [18][19][20][21][22]. Die Reproduzierbarkeit der Ergebnisse der Vorderabschnitts-OCT zur Untersuchung des Kammerwinkels sind als hoch anzusehen [23,24].…”

Section: Ist Die Gonioskopie In Zukunft Noch Notwendig?unclassified

Gonioskopie – wozu, weshalb, warum?

Unterlauft

2017

Klin Monatsbl Augenheilkd

View full text Add to dashboard Cite

Gonioscopy is used to exam the anterior chamber angle and the neighbouring structures. Gonioscopy can be performed using a contact lens, with the patient sitting at the slit lamp or in a supine position. Due to the total inner reflection of the cornea, the anterior chamber angle would otherwise be invisible in a healthy eye. During normal daily clinical routine, gonioscopy is often omitted, due to lack of time, although additional information could be gained. The aim is to give a summary of the diagnostic possibilities using gonioscopy and to emphasise the importance of this relatively easy clinical method. The development of gonioscopy, the appropriate tools, proper procedure at the slit lamp and the most popular grading systems are described. In addition, an overview of detectable pathological changes in the anterior chamber angle is given and future prospects about developments in gonioscopy. Gonioscopy can provide additional information otherwise missed by slit lamp examination or ophthalmoscopy alone. Up to now, no (semi-)automatic devices have been developed which could completely replace gonioscopy. Therefore gonioscopy remains an important part of a complete ophthalmological examination and should be learned, mastered and performed regularly by every ophthalmologist.

show abstract

“…Some are based exclusively on expert knowledge [7] , [8] , [9] , [10] , [11] , [12] . Others, however, learn from previous problems (case-based approaches) [13] , [14] or are representations (e.g., decision trees) that support decision making (model-based approaches) [15] . There are also hybrid approaches, such as the one illustrated in this article, where expert knowledge is used to gain a better understanding of the domain and KDD techniques are then applied to build models for use in decision making (e.g., diagnosis) based on the medical data.…”

Section: Introductionmentioning

confidence: 99%

Applying data mining techniques to medical time series: an empirical case study in electroencephalography and stabilometry

Anguera

Barreiro

Lara

et al. 2016

Computational and Structural Biotechnology Journal

View full text Add to dashboard Cite

One of the major challenges in the medical domain today is how to exploit the huge amount of data that this field generates. To do this, approaches are required that are capable of discovering knowledge that is useful for decision making in the medical field. Time series are data types that are common in the medical domain and require specialized analysis techniques and tools, especially if the information of interest to specialists is concentrated within particular time series regions, known as events.This research followed the steps specified by the so-called knowledge discovery in databases (KDD) process to discover knowledge from medical time series derived from stabilometric (396 series) and electroencephalographic (200) patient electronic health records (EHR). The view offered in the paper is based on the experience gathered as part of the VIIP project.1Knowledge discovery in medical time series has a number of difficulties and implications that are highlighted by illustrating the application of several techniques that cover the entire KDD process through two case studies.This paper illustrates the application of different knowledge discovery techniques for the purposes of classification within the above domains. The accuracy of this application for the two classes considered in each case is 99.86% and 98.11% for epilepsy diagnosis in the electroencephalography (EEG) domain and 99.4% and 99.1% for early-age sports talent classification in the stabilometry domain. The KDD techniques achieve better results than other traditional neural network-based classification techniques.

show abstract

Case-based approach to managing angle closure glaucoma with anterior segment imaging

Cited by 6 publications

References 36 publications

Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

Gonioskopie – wozu, weshalb, warum?

Applying data mining techniques to medical time series: an empirical case study in electroencephalography and stabilometry

Contact Info

Product

Resources

About