editorial data mining for biomedicine and...

EditorialData Mining for Biomedicine and Healthcare

Zhengxing Huang,1 Jose M. Juarez,2 and Xiang Li3

1College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China2University of Murcia, Murcia, Spain3IBM Research, Beijing, China

Correspondence should be addressed to Zhengxing Huang; [email protected]

Received 8 June 2017; Accepted 8 June 2017; Published 20 August 2017

Copyright © 2017 Zhengxing Huang et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.

1. Introduction

Artificial intelligence (AI), as a computational method,attempts tomimic, in a very simplistic way, the human cogni-tion capability so as to solve problems that have defined solu-tion using conventional computational techniques. Medicineis unquestionably the main area in which AI tools and tech-niques have been applied. Medicine plays an essential rolein human life to assess and solve problems with guaranteesof success. Medicine can be considered a multidisciplinaryscience by itself, laying on the advances and discoveries ofother sciences (biology, physics, statistics, etc.) and using suchresults as tools to solve diagnosis or prognosis problems. Forthis reason, AI in medicine has become a great issue. In thisregard, developing and applying novel AI techniques andtools, due to their particular strengths, to solve medical prob-lems and provide valuable information might be of interest tohealthcare professionals in their decision making [1, 2].

AI-based medical applications can be classified intoeither knowledge intensive or data driven. The knowledgeintensive approaches intend to obtain computational models,extracted from the clinical literature and experts’ experience,to represent concepts, their relations, and the mechanisms toenable automatic reasoning to support medical decisions.Unlike knowledge intensive, data-driven approaches, atthe center of the vision of learning health systems, extractsuch knowledge directly from the collected clinical dataand this knowledge is traditionally used to explain a patient’scurrent symptoms/vital signs and to predict future diseaseprogression of the patient.

While an increasing amount of data is being producedby various biomedical and healthcare systems, they havenot yet fully capitalized on the transformative opportunitiesthat these data provide. Applying data-driven techniques tobig health data can be of great benefit in the biomedicaland healthcare domain, allowing identification and extrac-tion of relevant information and reducing the time spentby biomedical and healthcare professionals and researcherswho are trying to find meaningful patterns and new threadsof knowledge.

The major goal of this special issue is to bring togetherthe researchers in healthcare and data mining to illustratepressing needs, demonstrate challenging research issues,and showcase the state-of-the-art research and development.The selected papers underwent a rigorous extra refereeingand revision process. It is glad to see that the selected paperspresented novel methods that were empirically evaluated onmedical datasets. In addition, most of the papers in thisspecial issue include healthcare experts as coauthors, whichis beneficial to open new approaches to medical experts inthis multidisciplinary field. Moreover, the relevance of thisspecial issue is strengthened by the fact that data-drivenmedicine is also an object of research in different communi-ties. These communities include AI, medicine, medicalinformatics, decision support, and healthcare management.

2. The Special Issue

Medical activities can be divided into six tasks: screening,diagnosis, treatment, prognosis, monitoring, and manage-

HindawiJournal of Healthcare EngineeringVolume 2017, Article ID 7107629, 2 pageshttps://doi.org/10.1155/2017/7107629

https://doi.org/10.1155/2017/7107629

ment. Among these, diagnosis is a particular important taskthat data-driven approaches have been widely applied totackle with. Diagnosis is the process of determining whichdisease or condition explains the patient’s symptoms. Theinformation required for diagnosis is typically collected froma patient’s medical records. The paper presented by F. Yanget al. studied a problem of esophageal cancer diagnosis.Esophageal cancer is one of the fastest rising types of cancersin China, and the Kazak nationality is the highest-risk groupin Xinjiang, China. They propose an effective computer-aided diagnostic system to assist physicians in interpretingdigital X-ray image features and improving the quality ofdiagnosis. The modules of the proposed system includeimage preprocessing, feature extraction and selection, andimage classification for disease diagnosis. They evaluatedtheir system using 300 original esophageal X-ray images,and the experimental results show that their system is prom-ising for the diagnostics of esophageal cancer.

Asan irremediableneurodegenerativedisorder that causesdementia in elderly people around the globe, Alzheimer’s dis-ease has been widely studied in medical informatics. Earlier,the majority of studies were accomplished manually or semi-manually for measuring the a priori region of interest (ROI)of patient images, which are not practicable in hospital. Tothis end, D. Jha et al. presented an automated approach forAlzheimer’s disease diagnosis. Specifically, a dual-tree com-plex wavelet transform was first adopted for extractingfeatures from magnetic resonance images. Then, the authorsapplied principal component analysis for feature dimen-sionality reduction. The reduced feature vector was sentto feed-forward neural network to distinguish Alzheimer’sdisease and healthy control from the input MR images.The experimental results showed their approach achievedbetter performance than the state-of-the-art algorithms.

Reusing the data from healthcare information systemscan effectively facilitate clinical trials; however, the selectionof eligible patients for clinical trial recruitment criteria is stillan open problem. In this issue, Y. Zhang et al. presented acomputer-aided clinical trial recruitmentmethodology, basedon syntax translation between different domain-specific lan-guages. In their proposal, the clinical trial recruitment criteriaare formally represented as general rule that are translatedinto intermediate query-oriented domain specific languagesto map the native database queries. This approach directlyuses the underlying database schema as a reference model.In consequence, the system complexity and data-mappingefforts are greatly reduced.

F. Gräßer et al. presented a recommender system fordata-driven therapy decision support. They proposed twomethods for therapy recommendation, namely, collaborativerecommender and demographic-based recommender. Bothalgorithms aim to predict the individual response to differenttherapy options using patient data. The authors evaluatedtheir methods using a clinical database incorporating patientssuffering from the autoimmune skin disease psoriasis andshowed that their methods profit from a combination intoan overall therapy recommender system.

A major challenge in medical informatics is to build aregional health information exchange system. J. Lei et al. have

faced this challenge in the context of health national reformin China. That is, they study the impact of the large-scaleconstruction of hospitals and the rapid development ofhealth information systems. Specifically, the authors usedinterviews, focus groups, a filed study, and a literature reviewto collect insights and analyze data. The case study was con-ducted in Xinjin region, which was able to build a complete,unified, and shared information system and many electronichealth record components to integrate and manage healthresources for 198 health institutions in its jurisdiction. Costsand benefits were carefully discussed, and the unique charac-teristics of the Xinjin case and a comparison with US caseswere analyzed.

Acknowledgments

The guest editors of this special issue would like to thankall authors of the submitted papers, as well as all reviewersfor their hard work and detailed reviews that led to eightaccepted papers.

Zhengxing HuangJose M. Juarez

Xiang Li

References

[1] E. H. Shortliffe, “The adolescence of AI in medicine: will thefield come of age in the '90s?,” Artificial Intelligence in Medicine,vol. 5, no. 2, pp. 93–106, 1993.

[2] N. Peek, C. Combi, R. Marin, and R. Bellazzi, “Thirty years ofartificial intelligence in medicine conferences: a review ofresearch themes,” Artificial Intelligence in Medicine, vol. 65,no. 1, pp. 61–73, 2013.

2 Journal of Healthcare Engineering

RoboticsJournal of

Hindawi Publishing Corporationhttp://www.hindawi.com Volume 2014


Active and Passive Electronic Components

Control Scienceand Engineering

Journal of


International Journal of

RotatingMachinery


Hindawi Publishing Corporation http://www.hindawi.com

Journal of

Volume 201

Submit your manuscripts athttps://www.hindawi.com

VLSI Design



Shock and Vibration


Civil EngineeringAdvances in

Acoustics and VibrationAdvances in



Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation http://www.hindawi.com

Volume 2014

The Scientific World JournalHindawi Publishing Corporation http://www.hindawi.com Volume 2014

SensorsJournal of


Modelling & Simulation in EngineeringHindawi Publishing Corporation http://www.hindawi.com Volume 2014


Chemical EngineeringInternational Journal of Antennas and

Propagation




Navigation and Observation



DistributedSensor Networks


editorial data mining for biomedicine and...

Documents