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==Introduction== | ==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<ref name="Medsoft2016">{{cite url=http://www.armit.ru/medsoft/2016/conference/prog/ |title=Presentations of the 12th International Forum "MedSoft-2016" |publisher=Association for the Development of Medical Information Technologies |date=2016}}</ref> 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.''<ref name="HandlerGartner07">{{cite web |url=https://www.gartner.com/doc/508592/gartners--criteria-enterprise-cpr |title=Gartner's 2007 Criteria for the Enterprise CPR |author=Handler, T.J.; Hieb, B.R. |publisher=Gartner, Inc |date=2007}}</ref> present the operating paradigm of fifth generation MISs, referred to as “MIS as Mentor.” Malykh ''et al.''<ref name="MalykhActive16">{{cite journal |title=Active MIS |journal=Information Technologies for the Physician |author=Malykh, V.L.; Rudetskiy, S.V.; Hatkevich, M.I. |volume=2016 |issue=6 |year=2016}}</ref> adds one more qualitative characteristic to the above paradigm—“MIS as automated mentor.” | 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<ref name="Medsoft2016">{{cite web |url=http://www.armit.ru/medsoft/2016/conference/prog/ |title=Presentations of the 12th International Forum "MedSoft-2016" |publisher=Association for the Development of Medical Information Technologies |date=2016}}</ref> 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.''<ref name="HandlerGartner07">{{cite web |url=https://www.gartner.com/doc/508592/gartners--criteria-enterprise-cpr |title=Gartner's 2007 Criteria for the Enterprise CPR |author=Handler, T.J.; Hieb, B.R. |publisher=Gartner, Inc |date=2007}}</ref> present the operating paradigm of fifth generation MISs, referred to as “MIS as Mentor.” Malykh ''et al.''<ref name="MalykhActive16">{{cite journal |title=Active MIS |journal=Information Technologies for the Physician |author=Malykh, V.L.; Rudetskiy, S.V.; Hatkevich, M.I. |volume=2016 |issue=6 |year=2016}}</ref> adds one more qualitative characteristic to the above paradigm—“MIS as automated mentor.” | ||
<blockquote>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.<ref name="MalykhActive16" /></blockquote> | <blockquote>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.<ref name="MalykhActive16" /></blockquote> | ||
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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<ref name="KohaneTheTwin09">{{cite journal |title=The twin questions of personalized medicine: who are you and whom do you most resemble? |journal=Genome Medicine |author=Kohane, I.S. |volume=1 |issue=1 |page=4 |year=2009 |doi=10.1186/gm4 |pmid=19348691 |pmc=PMC2651581}}</ref> 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. | 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<ref name="KohaneTheTwin09">{{cite journal |title=The twin questions of personalized medicine: who are you and whom do you most resemble? |journal=Genome Medicine |author=Kohane, I.S. |volume=1 |issue=1 |page=4 |year=2009 |doi=10.1186/gm4 |pmid=19348691 |pmc=PMC2651581}}</ref> 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<ref name="ButkoNew90">{{cite journal |title=New Decision Support Systems in Foreign Healthcare |journal=Automation and Remote Control |author=Butko, S.N.; Olshansky, V.K. |volume=51 |year=1990}}</ref> and Kotov<ref name="KotovNew04">{{cite book |chapter=New Mathematical Approaches to Medical Diagnostics |title=Editorial URSS |author=Kotov, Y.B. |year=2004}}</ref> 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”<ref name="MiottoDeep16">{{cite journal |title=Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records |journal=Scientific Reports |author=Miotto, R.; Li, L.; Kidd, B.A. et al. |volume=6 |page=26094 |year=2016 |doi=10.1038/srep26094}}</ref> was coined, meaning the opportunity to extract increasingly more complete, deep, and valuable [[information]] about patients from big clinical data using deep learning methods. | |||
==References== | ==References== | ||
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| 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) |
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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.
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.
- ↑ 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.
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.
