User:Shawndouglas/sandbox/sublevel8
 |
This is sublevel1 of my sandbox, where I play with features and test MediaWiki code. If you wish to leave a comment for me, please see my discussion page instead. |
Sandbox begins below
| Full article title | Approaches to Medical Decision-Making Based on Big Clinical Data |
|---|---|
| Journal | Journal of Healthcare Engineering |
| Author(s) | Malykh, V.L.; Rudetskiy, S.V. |
| Author affiliation(s) | Ailamazyan Program Systems Institute of RAS |
| Primary contact | Email: mvl at interin dot ru |
| Year published | 2018 |
| Volume and issue | 2018 |
| Page(s) | 3917659 |
| DOI | 10.1155/2018/3917659 |
| ISSN | 2040-2309 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://www.hindawi.com/journals/jhe/2018/3917659/ |
| Download | http://downloads.hindawi.com/journals/jhe/2018/3917659.pdf (PDF) |
 |
This article should not be considered complete until this message box has been removed. This is a work in progress. |
Abstract
The paper discusses different approaches to building a clinical decision support system based on big data. The authors sought to abstain from any data reduction and apply universal teaching and big data processing methods independent of disease classification standards. The paper assesses and compares the accuracy of recommendations among three options: case-based reasoning, simple single-layer neural network, and probabilistic neural network. Further, the paper substantiates the assumption regarding the most efficient approach to solving the specified problem.
Introduction
Providing support to clinical decision-making is one of the most urgent issues in healthcare automation. It has been repeatedly noted in different articles, reports, and forum discussions[1] both in Russia and abroad that medical information system (MIS) introduction requires a considerable extra effort from users/doctors in the first place—to enter primary data into the system. Naturally, doctors expect practical intelligent outcomes from big clinical data accumulated by modern MISs. Handler et al.[2] present the operating paradigm of fifth generation MISs, referred to as “MIS as Mentor.” Malykh et al.[3] adds one more qualitative characteristic to the above paradigm—“MIS as automated mentor.”
It is advisable to abandon the practice of active user dialogs typical of expert systems, involving requests for data that the system considers missing from the user, and substitute the dialog with an automated nonintrusive algorithm that draws its own logical conclusions and generates recommendations in a completely automated manner based on available data, without involving the user in the process. The user may either accept or ignore the system’s prompts and recommendations; however, they will not provoke rejection in users if delivered automatically without requiring a dialog with the system.[3]
To provide a brief qualitative description of this increasing subjectivity of MISs, we have proposed the new term “active MIS” that emphasizes a certain degree of independence from users or subjectivity of the cyber system. Kohane[4] presents the most “balanced” definition of personalized medicine: “personalized medicine is the practice of clinical decision-making such that the decisions made maximize the outcomes that the patient most cares about and minimize those that the patient fears the most, on the basis of as much knowledge about the individual’s state as is available.” This perception of personal medicine is focused on clinical decision-making and once again exhibits the urgency and importance of scientific research in the area. Therefore, building an automated active mentor-type system that provides recommendations regarding treatment and diagnostic activities to the doctor is an urgent practical task.
Butko and Olshansky[5] and Kotov[6] provide a retrospective overview of approaches to building clinical decision support systems. The applied approaches were restricted in many respects by the abilities of computers at that time. Accordingly, there was no such problem as processing big medical data. Technologies have evolved to the point where big medical data (both on individuals and the population in general) collection and accumulation is finally feasible. At the same time, big data processing and intelligent system learning methods were evolving as well. Along with “deep learning,” the term “deep patient”[7] was coined, meaning the opportunity to extract increasingly more complete, deep, and valuable information about patients from big clinical data using deep learning methods.
Malykh et al.[8] mention the possibility of creating national-scale clinical data banks. Herrett et al.[9] provide an example of a database (DB) containing anonymous medical records on primary healthcare services provided. This DB was created by a joint effort of 674 general practitioners and covers over 11.3 million patients in Great Britain.
Decision-making in hospitals has evolved from being opinion-based to being based on sound scientific evidence. This decision-making is recognized as evidence-based practice. Perpetual publication of new evidence combined with the demands of everyday practice makes it difficult for health professionals to keep up to date.M[10]
A large number of publications are devoted to clinical decision support systems (DSSs), including publications in specialized scientific journals (Artificial Intelligence in Medicine, BMC Medical Informatics and Decision Making, International Journal of Medical Informatics, Medical Decision Making, etc.). The work does not aim to give an overview of different approaches to making of decision support systems, referring readers to the original reviews.[11][12][13] We can give a few definitions for decision support system from Wikipedia: “Clinical Decision Support systems link health observations with health knowledge to influence health choices by clinicians for improved health care” and “active knowledge systems, which use two or more items of patient data to generate case-specific advice.” No one doubts the feasibility of such systems and that they have a positive impact on professional practice, patient outcomes, length of hospital stay, and hospital costs. The main problem is to find effective approaches to building such systems.
A number of contemporary approaches to clinical decision support system development are listed by Malykh et al.[14] The first one of these approaches involves provision of relevant data sources to doctors, helping them make decisions independently. The system does not recommend any final solutions—instead, it suggests data sources to study and find answers to current questions (e.g., UpToDate).
The second approach is to use clinical pathways. Clinical pathways represent prescriptive models of the standard healthcare procedures that need to be undertaken for a specific patient population. Instances of the clinical pathways (also known as cases) describe the actual diagnostic-therapeutic cycle of an individual patient.[15] But even in the case of the use of clinical pathways, the process of clinical decision-making has high complexity. While the medical knowledge used in the decision process comes partially from published research contributions and widespread medical guidelines (with various kinds of evidence levels), it is generally accepted that the decision process is profoundly influenced by the expertise and experiences of the involved medical experts.[15]
The third approach involves development of a large number of individual narrow-focused decision support systems. This approach helps achieve top quality when solving isolated problems[6][12]; however, it is almost impossible to apply it to big clinical data.
The fourth approach that claims to have a global scope of application is focused on building a cognitive system capable of self-learning and knowledge digestion directly from nonformalized text sources (e.g., IBM Watson).
None of the reviewed approaches is immaculate. All of them require efforts of experts and regular updates of knowledge bases. Moreover, each of the approaches is in fact tailored to specific purposes.
The latest Russian-language review[12] noted that clinical decision support systems have not become widespread in Russia. This is due to the complexity of the development of such systems, the specific character of the systems already developed, and the need to involve high-class experts in the development.
In this paper, we review general approaches to decision support system development based on nonreduced big clinical data. The main expectations related to application of general approaches ensue from the case-based nature of decision-making in healthcare, and the assumption that big clinical data already contain enough knowledge for efficient decision-making.
There are two other factors that draw attention to systems based on machine learning or precedent approach.
First of them is that there are trends in the development of our civilization, which include an explosive development of information technologies (among them machine to machine (M2M), big data, and the internet of things (IoT), their strong need for formalized knowledge, and practical absence of qualified experts who could formalize that knowledge. The chief editor of the Rational Enterprise Management (REM) magazine (Russia) holds regular discussions on a wide range of problems including the above-mentioned ones. Results of the discussions are published in the REM editor’s column. The guests of a recent discussion[16] included Igor Rudym (Intel), Dmitriy Tameev (PTC), Alexander Belotserkovskiy (Microsoft), Igor Girkin (Cisco), and Igor Kulinitchev (IBM). All the participants agreed that, nowadays, the key challenge of IT development is not associated with hardware or software, but it needs breakthrough approaches to data analysis.
As for the second factor, it is obvious that, nowadays, there are no qualified experts in the field of knowledge even in key branches. The actual situation is even more critical as the experts who are able to solve at least a part of these problems are not able to cope with ever increasing information flow. From this point of view, precedent-based DSSs practically need no experts. Experts may be needed for enhancing or optimizing existing medical databases and knowledge bases.[14]
References
- ↑ "Presentations of the 12th International Forum "MedSoft-2016"". Association for the Development of Medical Information Technologies. 2016. http://www.armit.ru/medsoft/2016/conference/prog/.
- ↑ Handler, T.J.; Hieb, B.R. (2007). "Gartner's 2007 Criteria for the Enterprise CPR". Gartner, Inc. https://www.gartner.com/doc/508592/gartners--criteria-enterprise-cpr.
- ↑ 3.0 3.1 Malykh, V.L.; Rudetskiy, S.V.; Hatkevich, M.I. (2016). "Active MIS". Information Technologies for the Physician 2016 (6).
- ↑ Kohane, I.S. (2009). "The twin questions of personalized medicine: who are you and whom do you most resemble?". Genome Medicine 1 (1): 4. doi:10.1186/gm4. PMC PMC2651581. PMID 19348691. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2651581.
- ↑ Butko, S.N.; Olshansky, V.K. (1990). "New Decision Support Systems in Foreign Healthcare". Automation and Remote Control 51.
- ↑ 6.0 6.1 Kotov, Y.B. (2004). "New Mathematical Approaches to Medical Diagnostics". Editorial URSS.
- ↑ Miotto, R.; Li, L.; Kidd, B.A. et al. (2016). "Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records". Scientific Reports 6: 26094. doi:10.1038/srep26094.
- ↑ Malykh, V.L.; Belyshev, D.V. (2015). "Case-based reasoning in clinical processes using clinical data banks". Proceedings from the 2015 International Conference on Biomedical Engineering and Computational Technologies: 211-216. doi:10.1109/SIBIRCON.2015.7361885.
- ↑ Herrett, E.; Gallagher, A.M.; Bhaskaran, K. et al. (2015). "Data Resource Profile: Clinical Practice Research Datalink (CPRD)". International Journal of Epidemiology 44 (3): 827-36. doi:10.1093/ije/dyv098. PMC PMC4521131. PMID 26050254. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4521131.
- ↑ Rotter, T.; Kinsman, L.; James, E. et al. (2010). "Clinical pathways: Effects on professional practice, patient outcomes, length of stay and hospital costs". Cochrane Database of Systematic Reviews (3): CD006632. doi:10.1002/14651858.CD006632.pub2. PMID 20238347.
- ↑ Berner, E.S. (June 2009). "Clinical Decision Support Systems: State of the Art". Agency for Healthcare Research and Quality. https://healthit.ahrq.gov/health-it-tools-and-resources/health-it-bibliography/clinical-decision-support-systems-cdss/clinic-0.
- ↑ 12.0 12.1 12.2 Efimenko, I.V.; Khoroshevsky, V.F. (2017). "Intelligent decision support systems in medicine: State of the art and beyond". Proceedings from Open Semantic Technologies for Intelligent Systems OSTIS-2017: 251-260. https://libeldoc.bsuir.by/handle/123456789/12259.
- ↑ "Clinical decision support system". Wikipedia. https://en.wikipedia.org/wiki/Clinical_decision_support_system.
- ↑ 14.0 14.1 Malykh, V.L.; Kononenko, I.N.; Rudetskiy, S.V. (2016). "Estimation of accuracy of recommended diagnostic and treatment actions based on precedent approach". Proceedings of the IADIS International Conference e-Health 2016: 52-8. http://www.iadisportal.org/digital-library/estimation-of-accuracy-of-recommended-diagnostic-and-treatment-actions-based-on-precedent-approach.
- ↑ 15.0 15.1 Caron, F.; Vanthienen, J.; Baesens, B. (2013). "Healthcare Analytics: Examining the Diagnosis–treatment Cycle". Procedia Technology 9: 996-1004. doi:10.1016/j.protcy.2013.12.111.
- ↑ Vasilyeva, E. (2015). "Industrial Internet of Things (IoT)". Rational Enterprise Management.
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.
