A Medical Informatics Perspective on Decision SupportToward a Unified Research Paradigm Combining Biological vs. Clinical, Empirical vs. Legacy, and Structured vs. Unstructured Data
07 March 2018 (online)
Objectives To summarize current excellent research in the field of decision-support systems.
Methods We provide a synopsis of the articles selected for the IMIA Yearbook 2009, from which we attempt to derive a synthetic overview of the activity and new trends in the field.
Results Five papers from international peer reviewed journals have been selected for the section on decision support. While the state of the research in the field of decision-support systems is illustrated by a set of fairly heterogeneous studies, it is possible to identify trends. Thus, issues related to guidelines processing implementation occupies a central role in today’s field with two alternative directions: 1. broad medical applications, which attempts to assist decision-makers to process large patient sets; 2. narrow clinical applications focused on in-depth real-time signal processing for a specific population or medical specialty.
Conclusions The best paper selection of articles on decision-supports shows examples of excellent research on methods concerning original development as well as quality assurance of reported studies. It is also observed that this year’s selection point directly to more original research areas such as temporal signal processing, although more traditionally related areas, such as information retrieval and/or natural language processing, remain fairly active in the field. Altogether these papers support the idea that more elaborated computer tools, likely to combine together textual and highly structured data, including real-time data contents, are needed.
- 1 Cornalba C, Bellazzi RG, Bellazzi R. Building a Normative Decision Support System for Clinical and Operational Risk Management in Hemodialysis. IEEE Trans Inf Technol Biomed 2008; Sep; 12 (05) 678-86.
- 2 Razavi AR, Gill H, Ahlfeldt H, Shahsavar N. Noncompliance with a Postmastectomy Radiotherapy Guideline: Decision Tree and Cause Analysis. BMC Med Inform Decis Mak 2008; Sep 21; 08: 41.
- 3 German E, Leibowitz A, Shahar Y. An Architecture for Linking Medical Decision-supportApplications to Clinical Databases and Its Evaluation. J Biomed Inform 2009; Apr; 42 (02) 203-18.
- 4 Wright A, Bates DW, Middleton B, Hongsermeier T, Kashyap V, Thomas SM. et al. Creating and Sharing Clinical Decision Support Content with Web 2.0: Issues and Examples. J Biomed Inform 2009; Apr; 42 (02) 334-46.
- 5 Suzuki T, Yokoi H, Fujita S, Takabayashi K. Automatic DPC Code Selection from Electronic Medical Records: Text Mining Trial of Discharge Summary. Methods Inf Med 2008; 47 (06) 541-8.
- 6 Yu W, Clyne M, Dolan SM, Yesupriya A, Wulf A, Khoury MJ. et al. GAPscreener: An Automatic Tool for Screening Human Genetic Association Literature in PubMed Using Support Vector Machine Technique. BMC Bioinformatics 2008; 09: 205.
- 7 Pasche E, Teodoro D, Gobeill J, Ruch P, Lovis C. Automatic Medical Knowledge Acquisition as a Question-Answering Task. Personal communication, work to be presented at MIE. 2009
- 8 Ruch P, Gobeill J, Lovis C, Geissbuhler A. From episodes of care to diagnosis codes: automatic text categorization for medico-economic encoding. AMIA Annu Symp Proc 2008 Nov 6: 636-40.
- 9 Mottaz A, Yip YL, Ruch P, Veuthey AL. Mapping proteins to disease terminologies: from UniProt to MeSH. BMC Bioinformatics. 2008 Apr 29;9 Suppl 5: S3.