Novosti
|
This journal is indexed in Scopus |
---|
Year 2014 Vol. 22 No 1
REVIEWS
A.A. LITVIN, V.A. LITVIN
CLINICAL DECISION SUPPORT SYSTEMS FOR SURGERY
E “Gomel Regional Clinical Hospital”1,
EE “Gomel State Medical University”2, Gomel
Belarusian State University3, Minsk, Belarus
This article presents a literature review of decision support systems (DSS) use for surgery. A decision support system (DSS) is a computer-based information system that collects, organizes and analyzes the large amounts of clinical data and medical knowledge that can effectively influence on the decision-making processes to generate case-specific advice. The problem of providing computer support decision-making in medicine is thought to be actual due to the increasing information load on the physician and the development of computer technology. In surgery while making medical decisions the lack of time, high dynamics of disease course, high price of medical errors, etc are considered to be specific.
DSS consists of the following computerized procedures: collection, processing, analysis of medical data, mathematical modeling, elaboration of alternatives and selection of the optimal method of diagnosis or treatment. Currently the assisting DSS in clinical practice, the test and opposing DSS in training and advanced training, analytical DSS in the scientific researches have been singed out.
DSS in surgery can be used as a source of medical knowledge for decision making in a diagnosis or treatment process to assist the physician in the diagnostic decision-making process, evaluate the effectiveness of treatment, analysis of the pathological process dynamics, assess of a patient's condition in real-time regime. Medical computer systems permit surgeons not only to test their own predictive and diagnostic assumptions, but to use artificial intelligence technologies in complex clinical situations.
- Greenes RA. Clinical decision support: the road ahead. Boston, US: Elsevier Academic Press; 2007. 581 p.
- Andreichikov AV, Andreichikova ON. Intellektual'nye informatsionnye sistemy [Intelligent information systems]. Moscow, RF: Finansy i Statistika; 2006. 364 p.
- Simankov VS, Khalafian AA. Sistemnyi analiz i sovremennye informatsionnye tekhnologii v meditsinskikh sistemakh podderzhki priniatiia reshenii [System analysis and current information technology in clinical decision support systems]. Moscow, RF: BinomPress; 2009. 362 p.
- Glotko VL. Avtomatizirovannye informatsionno-intellektual'nye sredstva podderzhki professional'noi deiatel'nosti vrachei spetsialistov voenno-meditsinskikh uchrezhdenii [Automated information and intellectual resources to support the professional activities of physicians of military medical institutions]. Vestn Novykh Med Tekhnologii. 2005;(3-4):103–4.
- Kobrinskii BA, Zarubina TV. Meditsinskaia informatika [Medical informatics]: uchebnik. Moscow, RF: Akademiia; 2009. 192 p.
- Khalafian AA. Sovremennye statisticheskie metody meditsinskikh issledovanii [The current statistical methods of medical research]. Moscow, RF: URSS : LKI; 2008. 316 p.
- Egorov AA, Mikshina BC. Model' priniatiia resheniia khirurga [Model of decision-making by the surgeon]. Vestn Novykh Med Tekhnologii. 2011;7(4):178–81.
- Miller RA. Medical diagnostic decision support systems--past, present, and future: a threaded bibliography and brief commentary. J Am Med Inform Assoc. 1994 Jan-Feb;1(1):8-27.
- De Dombal FT, Leaper DJ, Staniland JR, McCann AP, Horrocks JC. Computer-aided diagnosis of acute abdominal pain. Br Med J. 1972 Apr 1;2(5804):9-13.
- Belle A, Kon MA, Najarian K. Biomedical informatics for computer-aided decision support systems: a survey. Scientific World Journal. 2013;2013:769639.
- Van Ginneken B, ter Haar Romeny BM, Viergever MA. Computer-aided diagnosis in chest radiography: a survey. IEEE Trans Med Imaging. 2001 Dec;20(12):1228–41.
- Chen W, Cockrell C, Ward KR, Najarian K. Intracranial pressure level prediction in traumatic brain injury by extracting features from multiple sources and using machine learning methods. Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM '10). December 2010. – P. 510-15.
- Davuluri P, Wu J, Ward KR, Cockrell CH, Najarian K, Hobson RS. An automated method for hemorrhage detection in traumatic pelvic injuries. Conf Proc IEEE Eng Med Biol Soc. 2011;2011:5108-11.
- Stivaros SM, Gledson A, Nenadic G, Zeng XJ, Keane J, Jackson A. Decision support systems for clinical radiological practice - towards the next generation. Br J Radiol. 2010 Nov;83(995):904–14.
- Ji SY, Smith R, Huynh T, Najarian K. A comparative analysis of multi-level computer-assisted decision making systems for traumatic injuries. BMC Med Inform Decis Mak. 2009 Jan 14;9:2.
- Frize M, Walker R. Clinical decision-support systems for intensive care units using case-based reasoning. Med Eng Phys. 2000 Nov;22(9):671-7.
- Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J, Sam J, Haynes RB. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA. 2005 Mar 9;293(10):1223–38.
- Polat K, Akdemir B, Günes S. Computer aided diagnosis of ECG data on the least square support vector machine. Digit Signal Process. 2008;18:25–32
- Shandilya S, Ward K, Kurz M, Najarian K. Non-linear dynamical signal characterization for prediction of defibrillation success through machine learning. MC Med Inform Decis Mak. 2012 Oct 15;12:116.
- Lisboa PJ, Taktak AF. The use of artificial neural networks in decision support in cancer: a systematic review. Neural Netw. 2006 May;19(4):408–15.
- Mofidi R, Duff MD, Madhavan KK, Garden OJ, Parks RW.Identification of severe acute pancreatitis using an artificial neural network. Surgery. 2007 Jan;141(1):59–66.
- Andersson B, Andersson R, Ohlsson M, Nilsson J. Prediction of severe acute pancreatitis at admission to hospital using artificial neural networks. Pancreatology. 2011;11(3):328-35.
- Mironov PI, Medvedev OI, Ishmukhametov IKh, Bulatov RD. Prognozirovanie techeniia i iskhodov tiazhelogo ostrogo pankreatita [Forecasting of the course and outcomes of severe acute pancreatitis]. Fund Issledovaniia. 2011;(10):319–23.
- Korenevskii HA, Shekhine MT, Pekhov DA, Tarasov OP. Prognozirovanie, ranniaia diagnostika i otsenka stepeni tiazhesti ostrogo kholetsistita na osnove nechetkoi logiki priniatiia reshenii [Forecasting, early diagnosis and assessment of acute cholecystitis severity based on indistinct logic decision-making]. Vestn Voronezh Gos Tekhn Un-ta. 2009;5(11):150–55.
- Kobrinskii BA. Sistemy podderzhki priniatiia reshenii v zdravookhranenii i obuchenii [Decision support systems in health care and education]. Vrach i Inform Tekhnologii. 2010;(2):39–45.
246029, Respublika Belarus', g. Gomel', ul. Brat'ev Liziukovykh, d. 5, U «Gomel'skaia oblastnaia klinicheskaia bol'nitsa»,
e-mail: aalitvin@gmail.com, Litvin Andrei Antonovich
Litvin A.A. PhD, a deputy chief for surgery of ME “Gomel Regional Clinical Hospital”, an associate professor of the surgical diseases chair ¹1, EE “Gomel State Medical University”.
Litvin V.A. A student of the radiophysics and computer technologies faculty, Belarusian State University.