Overview of Clinical Decision Support Systems

Berner, Eta S.; Lande, Tonya J. La

doi:10.1007/978-3-319-31913-1_1

Cited by 130 publications

(131 citation statements)

References 52 publications

Supporting

Mentioning

129

Contrasting

Unclassified

Order By: Relevance

“…The first presentation of a system for computer-aided interpretation appeared in 1966, developed by the American Medical Association and represents a system of standardized terms for medical procedures used to facilitate documentation, called Current Procedural Terminology (CPT ® ). Today this is the most widely accepted medical nomenclature used to report medical procedures and services under public and private health insurance programs (51,52).…”

Section: Related Workmentioning

confidence: 99%

Using Data Mining Techniques to Support Breast Cancer Diagnosis

Diz

Marreiros

2015

New Contributions in Information Systems and Technologies

View full text Add to dashboard Cite

More than ever, there is an increase of the number of decision support methods and computer aided diagnostic systems applied to various areas of medicine. In breast cancer research, many works have been done in order to reduce false-positives when used as a double reading method.In this study, we aimed to present a set of data mining techniques that were applied to approach a decision support system in the area of breast cancer diagnosis. This method is geared to assist clinical practice in identifying mammographic findings such as microcalcifications, masses and even normal tissues, in order to avoid misdiagnosis.In this work a reliable database was used, with 410 images from about 115 patients, containing previous reviews performed by radiologists as microcalcifications, masses and also normal tissue findings.Throughout this work, two feature extraction techniques were used: the gray level co-occurrence matrix and the gray level run length matrix.For classification purposes, we considered various scenarios according to different distinct patterns of injuries and several classifiers in order to distinguish the best performance in each case described. The many classifiers used were Naïve Bayes, Support Vector Machines, k-nearest Neighbors and Decision Trees (J48 and Random Forests).The results in distinguishing mammographic findings revealed great percentages of PPV and very good accuracy values. Furthermore, it also presented other related results of classification of breast density and BI-RADS ® scale.The best predictive method found for all tested groups was the Random Forest classifier, and the best performance has been achieved through the distinction of microcalcifications.The conclusions based on the several tested scenarios represent a new perspective in breast cancer diagnosis using data mining techniques. VIII IX ResumoCada vez mais assistimos a um aumento global do número de métodos de apoio a decisão e diagnóstico assistido por computador, aplicados a diversas áreas da medicina.Na área de investigação do cancro da mama muitos são os trabalhos que têm sido desenvolvidos como segunda leitura de modo a reduzir o número de falsos positivos no diagnóstico.Neste estudo é apresentado um conjunto de técnicas de data mining que poderão ser aplicadas a um sistema de apoio à decisão na área do diagnóstico de cancro da mama.Esta abordagem tem por objetivo ajudar os clínicos na identificação de achados mamográficos como microcalcificações, massas e mesmo tecidos normais, de forma a evitar diagnósticos errados.Para isso, neste trabalho é usada uma base de dados fidedigna, de 410 imagens correspondentes a 115 pacientes, contendo análises prévias, realizadas por radiologistas, de microcalcificações, massas e tecidos considerados normais.Ao longo deste trabalho são utilizadas duas técnicas de extração de características, a matriz de coocorrência de níveis de cinza e a matriz de comprimento da linha de níveis de cinza.

show abstract

Section: Related Workmentioning

confidence: 99%

Using Data Mining Techniques to Support Breast Cancer Diagnosis

Diz

Marreiros

2015

New Contributions in Information Systems and Technologies

View full text Add to dashboard Cite

show abstract

“…The general target areas of care for CDSS are: Preventive care which has to do with screening and disease management Diagnosis which is done based on the patients' signs and symptoms Follow-up management which has to do with frequent checkups Hospital Provider Efficiency [9].…”

Section: A Target Area Of Carementioning

confidence: 99%

An Adaptive Modeling and Execution Framework for a Knowledge-Based Intelligent Clinical Decision Support System to Predict Schizophrenia

Y.A¹

2015

International Journal of Advanced Research in Computer and Comm

View full text Add to dashboard Cite

This paper proposes an adaptive framework for a Knowledge Based Intelligent Clinical Decision Support System for the prediction of schizophrenia which is one of the most deadly illnesses that has a monumental effect on the health of people afflicted with it and has for long remained a persistent health problem affecting a significant number of people all over the world. In the framework the patient information is fed into the system; the Knowledge base of the system stores all the information to be used by the Clinical Decision Support System and the classification algorithm selected after an exhaustive evaluation of relevant classification algorithms for this work is the C5.0 Decision Tree Algorithm with its percentage of correctly classified instances given as 78.4534%; it searches the Knowledge base and matches the patient information with the related rules that match with each case and thereafter gives the most precise prediction as to whether the patient is likely to develop schizophrenia or not. This approach to the prediction of schizophrenia provides a very reliable solution to the problem of ascertaining if a person is likely to develop this illness or is almost not susceptible to the ailment.

show abstract

“…(Berner and La Lande 2016). Ideally, such resources should be evidence-based and continually updated and should contain practical knowledge that will help healthcare professionals at the point of care.…”

Section: Introduction Introductionmentioning

confidence: 99%