Intelligent Data Analysis In Medicine And Pharmacology: An Overview

Lavrač, Nada; Keravnou, Elpida; Zupan, Blaž

doi:10.1007/978-1-4615-6059-3_1

Cited by 38 publications

(38 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, the machine learning community has developed various tools that have been successfully used in the construction of classification models, including medical prognostic models [15,18]. In this paper, we propose a framework which allows us to use machine learning techniques to construct classification models from survival data.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for survival analysis: a case study on recurrence of prostate cancer

Zupan

Demšar

Kattan

et al. 2000

Artificial Intelligence in Medicine

Self Cite

112

View full text Add to dashboard Cite

Machine learning techniques have recently received considerable attention, especially when used for the construction of prediction models from data. Despite their potential advantages over standard statistical methods, like their ability to model non-linear relationships and construct symbolic and interpretable models, their applications to survival analysis are at best rare, primarily because of the difficulty to appropriately handle censored data. In this paper we propose a schema that enables the use of classification methods -including machine learning classifiers -for survival analysis. To appropriately consider the follow-up time and censoring, we propose a technique that, for the patients for which the event did not occur and have short follow-up times, estimates their probability of event and assigns them a distribution of outcome accordingly. Since most machine learning techniques do not deal with outcome distributions, the schema is implemented using weighted examples. To show the utility of the proposed technique, we investigate a particular problem of building prognostic models for prostate cancer recurrence, where the sole prediction of the probability of event (and not its probability dependency on time) is of interest. A case study on preoperative and postoperative prostate cancer recurrence prediction shows that by incorporating this weighting technique the machine learning tools stand beside modern statistical methods and may, by inducing symbolic recurrence models, provide further insight to relationships within the modeled data.

show abstract

Section: Introductionmentioning

confidence: 99%

Machine learning for survival analysis: a case study on recurrence of prostate cancer

Zupan

Demšar

Kattan

et al. 2000

Artificial Intelligence in Medicine

Self Cite

112

View full text Add to dashboard Cite

show abstract

“…Temporal data abstraction methods represent an important subgroup where the processed data are temporal [5].…”

Section: Temporal Data Abstractionmentioning

confidence: 99%

Using time-oriented data abstraction methods to optimize oxygen supply for neonates

Seyfang

Miksch

Horn

et al. 2001

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

Abstract. Therapy management needs sophisticated patient monitoring and therapy planning, especially in high-frequency domains, like Neonatal Intensive Care Units (NICUs), where complex data sets are collected every second. An elegant method to tackle this problem is the use of time-oriented, skeletal plans. Asgaard is a framework for the representation, visualization, and execution of such plans. These plans work on qualitative abstracted time-oriented data which closely resemble the concepts used by experienced clinicians. This papers presents the data abstraction unit of the Asgaard system. It provides a range of connectable data abstraction methods bridging the gap between the raw data collected by monitoring devices and the abstract concepts used in therapeutic plans. The usability of this data abstraction unit is demonstrated by the implementation of a controller for the automated optimization of the fraction of inspired oxygen (FiO2). The use of the time-oriented data abstraction methods results in safe and smooth adjustment actions of our controller in a neonatal care setting.

show abstract

“…Analyzing these data is critical for medical decision makers and managers. The performance of patient management tasks will be improved by analyzing the medical data [1]. Medical data analysis is highly required for the following reasons: 1) Support of specific knowledge-based problem solving activities through the analysis of patient's raw data collected in monitoring [2], 2) Discovery of new knowledge that can be mined through the analysis of groups of case studies, described by symbolic or numeric descriptors [2].…”

Section: Introductionmentioning

confidence: 99%

Mining Medical Databases Using Graph based Association Rules

AlZoubi¹

2013

IJMLC

View full text Add to dashboard Cite

Abstract-Medical databases have accumulated huge amounts of information about patients and their medical conditions. Relationships and patterns within these data can provide new medical knowledge. Sorry to say that few methodologies have been developed and applied to discover this hidden knowledge. In this paper, the graph bases association rules mining (data mining is the main part of Knowledge Discovery in Databases) is used to search for relationships in a large medical database. The data that was collected on 6549 obstetrical patients were evaluated for factors potentially contributing to preterm birth using exploratory factor analysis. This paper describes the processes involved in mining a medical database including data warehousing, data query and cleaning, and data analysis.Index Term-Graph, medical database, rule mining.

show abstract

Intelligent Data Analysis In Medicine And Pharmacology: An Overview

Cited by 38 publications

References 3 publications

Machine learning for survival analysis: a case study on recurrence of prostate cancer

Machine learning for survival analysis: a case study on recurrence of prostate cancer

Using time-oriented data abstraction methods to optimize oxygen supply for neonates

Mining Medical Databases Using Graph based Association Rules

Contact Info

Product

Resources

About