ABSTRACT
There has been an unprecedented generation of healthcare data at clinical practices. With the availability of advanced computing frameworks and the ability to electronically mine data from disparate sources (e.g. demographics, genetics, imaging, treatment, clinical decisions, and outcomes) big data research in medicine has become a very active field of interest. In this paper, we discuss the challenges associated with designing clinical decision support systems that try to leverage such disparate data sources and create smart healthcare tools to aid medical practitioners for better patient care and treatment plans. We next propose an integrated data curation, storage and analytics portal, called HINGE (the Health Information Gateway and Exchange application), that can effectively address many of the outstanding challenges in this domain. HINGE specifically caters to healthcare data from radiation oncology patients however, the underlying formalisms and principles, as discussed here, are readily extendible to other disease types making it an attractive tool for the design of next generation clinical decision support systems.
- http://www.who.int. World Health Organization, 2017.Google Scholar
- https://www.cancer.gov/.national Cancer Institute, year = 2017, url = https://www.cancer.gov/about-cancer/understanding/statistics/, urldate = 11-15-2017.Google Scholar
- Paolo Fraccaro, Panagiotis Plastiras, Chiara Dentone, Antonio Di Biagio, Peter Weller, et al. Behind the screens: Clinical decision support methodologies-a review. Health Policy and Technology, 4(1):29--38, 2015.Google ScholarCross Ref
- Issam El Naqa. Biomedical informatics and panomics for evidence-based radiation therapy. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 4(4):327--340, 2014. Google ScholarDigital Library
- Jatinder R Palta, Jason A Efstathiou, Justin E Bekelman, Sasa Mutic, Carl R Bogardus, Todd R McNutt, Peter E Gabriel, Colleen A Lawton, Anthony L Zietman, and Christopher M Rose. Developing a national radiation oncology registry: From acorns to oaks. Practical radiation oncology, 2(1):10--17, 2012.Google Scholar
- Charles S Mayo, Marc L Kessler, Avraham Eisbruch, Grant Weyburne, Mary Feng, James A Hayman, Shruti Jolly, Issam El Naqa, Jean M Moran, Martha M Matuszak, et al. The big data effort in radiation oncology: Data mining or data farming? Advances in Radiation Oncology, 1(4):260--271, 2016.Google ScholarCross Ref
- Rebecca Siegel, Deepa Naishadham, and Ahmedin Jemal. Cancer statistics, 2012. CA: A Cancer Journal for Clinicians, 62(1):10--29, 2012.Google Scholar
- Justin E Bekelman, Anand Shah, and Stephen M Hahn. Implications of comparative effectiveness research for radiation oncology. Practical radiation oncology, 1(2):72--80, 2011.Google Scholar
- Seer.cancer.gov. Epidemiology, and end results program, 2017.Google Scholar
- Reshma Jagsi, Paul Abrahamse, Sarah T Hawley, John J Graff, Ann S Hamilton, and Steven J Katz. Underascertainment of radiotherapy receipt in surveillance, epidemiology, and end results registry data. Cancer, 118(2):333--341, 2012.Google ScholarCross Ref
- Robert A Greenes. Clinical decision support: the road to broad adoption. Academic Press, 2014.Google Scholar
- Massimo Esposito, Ivanoe De Falco, and Giuseppe De Pietro. An evolutionary-fuzzy dss for assessing health status in multiple sclerosis disease. International journal of medical informatics, 80(12):e245--e254, 2011.Google Scholar
- Vijay K Mago, Ravinder Mehta, Ryan Woolrych, and Elpiniki I Papageorgiou. Supporting meningitis diagnosis amongst infants and children through the use of fuzzy cognitive mapping. BMC medical informatics and decision making, 12(1):98, 2012.Google Scholar
- David Riaño, Francis Real, Joan Albert López-Vallverdú, Fabio Campana, Sara Ercolani, Patrizia Mecocci, Roberta Annicchiarico, and Carlo Caltagirone. An ontology-based personalization of health-care knowledge to support clinical decisions for chronically ill patients. Journal of biomedical informatics, 45(3):429--446, 2012. Google ScholarDigital Library
- Ray S Lin, Susan D Horn, John F Hurdle, and Alexander S Goldfarb-Rumyantzev. Single and multiple time-point prediction models in kidney transplant outcomes. Journal of biomedical informatics, 41(6):944--952, 2008. Google ScholarDigital Library
- Jorge LM Amaral, Agnaldo J Lopes, José M Jansen, Alvaro CD Faria, and Pedro L Melo. Machine learning algorithms and forced oscillation measurements applied to the automatic identification of chronic obstructive pulmonary disease. Computer methods and programs in biomedicine, 105(3):183--193, 2012. Google ScholarDigital Library
- Baek Hwan Cho, Hwanjo Yu, Kwang-Won Kim, Tae Hyun Kim, In Young Kim, and Sun I Kim. Application of irregular and unbalanced data to predict diabetic nephropathy using visualization and feature selection methods. Artificial intelligence in medicine, 42(1):37--53, 2008. Google ScholarDigital Library
- Chih-Lin Chi, W Nick Street, and Marcia M Ward. Building a hospital referral expert system with a prediction and optimization-based decision support system algorithm. Journal of biomedical informatics, 41(2):371--386, 2008. Google ScholarDigital Library
- Naomi Sager, Margaret Lyman, Christine Bucknall, Ngo Nhan, and Leo J Tick. Natural language processing and the representation of clinical data. Journal of the American Medical Informatics Association, 1(2):142--160, 1994.Google ScholarCross Ref
- Serguei Pakhomov, James Buntrock, and Patrick Duffy. High throughput modularized nlp system for clinical text. In Proceedings of the ACL 2005 on Interactive poster and demonstration sessions, pages 25--28. Association for Computational Linguistics, 2005. Google ScholarDigital Library
- Michael E Matheny, Fern FitzHenry, Theodore Speroff, Jennifer K Green, Michelle L Griffith, Eduard E Vasilevskis, Elliot M Fielstein, Peter L Elkin, and Steven H Brown. Detection of infectious symptoms from va emergency department and primary care clinical documentation. International journal of medical informatics, 81(3):143--156, 2012.Google Scholar
- Jean B Owen, Julia R White, Michael J Zelefsky, and J Frank Wilson. Using qrroâĎć survey data to assess compliance with quality indicators for breast and prostate cancer. Journal of the American College of Radiology, 6(6):442--447, 2009.Google ScholarCross Ref
- nim.nih.gov. Common Data Element Dictionary, 2017.Google Scholar
- American Joint Committee on Cancer. American joint committee on cancer.Google Scholar
- Common Terminology Criteria for Adverse Events (CTCAE). Common terminology criteria for adverse events (ctcae).Google Scholar
- Ethan Basch, Stephanie L Pugh, Amylou C Dueck, Sandra A Mitchell, Lawrence Berk, Shannon Fogh, Lauren J Rogak, Marcha Gatewood, Bryce B Reeve, Tito R Mendoza, et al. Feasibility of patient reporting of symptomatic adverse events via the patient-reported outcomes version of the common terminology criteria for adverse events (pro-ctcae) in a chemoradiotherapy cooperative group multi-center clinical trial. International Journal of Radiation Oncology* Biology* Physics, 98(2):409--418, 2017.Google Scholar
- AAPM TG 263 Standardizing Radiation Therapy Nomenclature. Aapm tg 263 - standardizing radiation therapy nomenclature.Google Scholar
- S Trent Rosenbloom, Joshua C Denny, Hua Xu, Nancy Lorenzi, William W Stead, and Kevin B Johnson. Data from clinical notes: a perspective on the tension between structure and flexible documentation. Journal of the American Medical Informatics Association, 18(2):181--186, 2011.Google ScholarCross Ref
- David S Carrell, Scott Halgrim, Diem-Thy Tran, Diana SM Buist, Jessica Chubak, Wendy W Chapman, and Guergana Savova. Using natural language processing to improve efficiency of manual chart abstraction in research: the case of breast cancer recurrence. American journal of epidemiology, 179(6):749--758, 2014.Google Scholar
- Kaihong Liu, William R Hogan, and Rebecca S Crowley. Natural language processing methods and systems for biomedical ontology learning. Journal of biomedical informatics, 44(1):163--179, 2011. Google ScholarDigital Library
- Maria YY Law and Brent Liu. Dicom-rt and its utilization in radiation therapy. Radiographics, 29(3):655--667, 2009.Google ScholarCross Ref
- Maria YY Law, Brent Liu, and Lawrence W Chan. Dicom-rt-based electronic patient record information system for radiation therapy. Radiographics, 29(4):961--972, 2009.Google ScholarCross Ref
- Joel Poder, Johnson Yuen, Andrew Howie, Andrej Bece, and Joseph Bucci. Dose accumulation of multiple high dose rate prostate brachytherapy treatments in two commercially available image registration systems. Physica Medica, 43:43--48, 2017.Google ScholarCross Ref
- Di Yan, Frank Vicini, John Wong, and Alvaro Martinez. Adaptive radiation therapy. Physics in medicine and biology, 42(1):123, 1997.Google Scholar
- Samuel B Park, James I Monroe, Min Yao, Mitchell Machtay, and Jason W Sohn. Composite radiation dose representation using fuzzy set theory. Information Sciences, 187:204--215, 2012. Google ScholarDigital Library
- EH Balagamwala, T Djemil, SA Koyfman, ST Chao, L Angelov, JH Suh, and P Xia. 3d composite dose is necessary to assess cumulative spinal cord dose for retreatment of spinal tumors with stereotactic body radiotherapy. International Journal of Radiation OncologyâĂć BiologyâĂć Physics, 81(2):S647, 2011.Google ScholarCross Ref
- Reid F Thompson. Radonc: an r package for analysis of dose-volume histogram and three-dimensional structural data. Journal of Radiation Oncology Informatics, 6(1):98--110, 2014.Google Scholar
Index Terms
- A smart healthcare portal for clinical decision making and precision medicine
Recommendations
Medical informatics: clinical decision making and beyond
Does Medical Informatics encompass all aspects of computing in health care, or is it limited to information processing in clinical medicine? A panel discussion will present several points of view. This paper advocates a unified view of Medical ...
The U.S. National Library of Medicine’s impact on precision and genomic medicine
Precision medicine offers the potential to improve health through deeper understandings of the lifestyle, biological, and environmental influences on health. Under Dr. Donald A. B. Lindberg’s leadership, the U.S. National Library of Medicine (NLM) ...
Geographic medicine and clinical microbiology
Special issue on biomedical applications of knowledge discovery in databasesDevelopment of a computer program for simulation, diagnosis and informatics in Geographic Medicine and Clinical Microbiology. The GIDEON (Global Infectious Diseases and EpidemiOgy Network) software follows the status of all infectious diseases and ...
Comments