Uniqueness of medical data mining

By Krzysztof Jozef Cios, G. William Moore. Artif. Intell. Medicine, 2002

711 citations recorded by the source corpus.

Subfields: Health Information Management, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/66d29b7d051de6da8c568342a3c4f0ddb016124d

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In Health Information Management

In Information Systems

In Management Science and Operations Research

Citations in the corpus

Performance Evaluation of Machine Learning Algorithms in Post-operative Life Expectancy in the Lung Cancer Patients (2015, background): “Clinical data often originate from clinical consultation with patients, medical images, ECG, EEG and RTG signals, physician's notes and interpretations and other screening results that may bear upon clinical analysis and treatment of the patient [22-23].”
Predicting breast cancer survivability: a comparison of three data mining methods (2005, background): “Thus, reducing the problem of missing, redundant or inconsistent values data [11].”
Security and Privacy Issues with Health Care Information Technology (2006, background): “Moreover, HIPAA requires that all patients be able to access their own medical records, correct errors or omissions, and be informed how their personal information is shared or used.”
Limestone: High-throughput candidate phenotype generation via tensor factorization (2014, background): “…heterogeneous information covering a variety of inter-related aspects of patients such as diagnoses, medication orders, and laboratory test ﬁndings [17,18]; sparsely sampled medical event sequences with different time scales across patients [19–21]; and noisy, incomplete, and inaccurate…”
A survey of Knowledge Discovery and Data Mining process models (2006, background): “Many domains have their speciﬁc problems , such as medicine where problems are often encountered with a physician’s interpretations that are written in unstructured free-text English ( Cios & Moore , 2002).”
Mortality assessment in intensive care units via adverse events using artificial neural networks (2006, background): “On the other hand, the use of data mining in medicine is a rapidly growing field, which aims at discovering some structure in large clinical heterogeneous data [3].”
A Bibliometric Analysis and Visualization of Medical Big Data Research (2018, background): “The original clinical data is large and heterogeneous, mostly from electronic medical records, medical images, medical record parameters, laboratory results, and clinical observation and interpretation [54].”
Predictive data mining in clinical medicine: Current issues and guidelines (2008, background): “Compared to data mining in business, marketing and the economy, medical data mining applications have several distinguishing features [104].”
Axes of a revolution: challenges and promises of big data in healthcare (2020, background): “Medical data have unique features compared with big data in other domains 7 .”
Impact of HbA1c Measurement on Hospital Readmission Rates: Analysis of 70,000 Clinical Database Patient Records (2014, neutral): “[8].”
Interactive knowledge discovery with the doctor-in-the-loop: a practical example of cerebral aneurysms research (2016, background): “Keeping in mind that medical domain-experts are required to be deeply involved into the process of medical knowledge discovery [5], the known process models are hardly suitable.”
Data Mining in Healthcare and Biomedicine: A Survey of the Literature (2012, background): “This may be an important consideration in the United States where malpractice laws are stringent and people are litigious [ 28 ].”
Ontology-Guided Principal Component Analysis: Reaching the Limits of the Doctor-in-the-Loop (2016, background): “Consequently, the role of the domain expert turns from a passive external supervisor – or customer – to an active actor of the process, which is necessary due to the enormous complexity of the medical research domain [5].”