By Margarita Sordo, Tonya Hongsermeier (auth.), Prof. Hiro Yoshida, Dr. Ashlesha Jain, Ajita Ichalkaranje, Prof. Lakhmi C. Jain, Dr. Nikhil Ichalkaranje (eds.)
This ebook provides probably the most fresh learn effects at the functions of computational intelligence in healthcare. The contents include:
- Information version for administration of scientific content
- State-based version for administration of style II diabetes
- Case-based reasoning in medicine
- Assessing the standard of care in man made intelligence environment
- Electronic clinical list to check general practitioner decisions
- Multi-agent platforms for the administration of group healthcare
- Assistive wheelchair navigation
- Modelling therapy approaches utilizing details extraction
- Neonatal discomfort detection utilizing face category techniques
- Medical schooling interfaces utilizing digital patients
The e-book is directed to the pc scientists, clinical practitioners, scientists, professors and scholars of overall healthiness technological know-how, desktop technology and similar disciplines.
Read Online or Download Advanced Computational Intelligence Paradigms in Healthcare – 1 PDF
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Extra resources for Advanced Computational Intelligence Paradigms in Healthcare – 1
Sordo et al. Fig. 17. Diagram for KnowledgeRules category. This category consists of Associated Knowledge – a collection of links to additional, supporting knowledge, and Associated Rules – identiﬁers to a repository of decision support rules Fig. 18. Diagram for NotesInstructions category. This category consists of predeﬁned, brief guidelines, recommendations and instructions for procedures and actions. They are not patient speciﬁc Fig. 19. Diagram for OrderGroup category. 9 NotesInstructions Category The NotesInstructions category in Fig.
3) focuses on the long-term management of diabetes, regular monitoring (surveillance) for early detection and management of diabetes-related complication, as well as regular monitoring for diagnosis, treatment, and follow-up of intercurrent illnesses. A State-Based Model for Management of Type II Diabetes 41 PatientHas(Diabetes) = F State: Diabetes Preventive Care State: Preventive Care timeToCheck() = T State: BP State: Screening Risk = Low State: Lipid Management State: Regular entry/order Tests(); /recomend(PrevCare) exit/DocumentSession() Risk = High State: Antiplatelets State: Smoking State: Targeted entry/orderTests(); /recomend(PrevCare) exit/DocumentSession() [ Symptoms = F AND PrevCare= T] exit/DocumentSession() [Symptoms = F]/scheduleFollowUp() State: CHD State: Immunization [ Symptoms = T] exit/DocumentSession(); /scheduleFollowUp() State: Initial Diagnosis State: Medical History State: Physical Exam State: Lab Evaluation State: Referrals exit/PatientHas(Diabetes) = T; /DocumentSession() PatientHas(Diabetes) =T State: Diabetes Long-Term Management State: Management State: Surveillance State: Self blood glucose monitoring State: Nephropathy (UP) State: Diet [TimeUp(Surveillance) = T] State: Physical Activity State: Eye Exam State: Foot care State: Hyperglycemia State: Aspirin EVT(Management) State: BP management State: BP management State: Lipids management State: Immunization exit/SetManagementFollowUp(); /SetSurveillance() EVT(Management) [ timeUp(Management) = T ] State: Intercurrent Illnesses exit/SurveillanceFollowUp(); [Newillness() = T]/ EVT(Intercurrentillness) [Newillness() = F]/ EVT(Management) EVT(Intercurrentillness) State: Diagnose State: Screening entry/OrderTests(); exit/DocumentSession() TimeUp(Intillness) = T State: Follow-up entry/OrderTests(); exit/DocumentSession() entry/reviewTests(); exit/diagnose(); /DocumentSession() [diagnose = T] State: Treatment entry/setupTreatment(); exit/DocumentSession(); /ScheduleFollowUpillness() exit/DocumentSession(); EVT(Management) Fig.
Our ﬁrst step is to deploy the OS Schema so that existing content in the OSs in both BWH and MGH CPOE systems can be successfully extracted and mapped into the proposed schema. In this way, “hardwired” knowledge could be mapped into taxonomies of relevant terms, deﬁnitions and associations, resulting in formalized conceptual models and ontologies with explicit, consistent, user-meaningful relationships among concepts. We envision that the proposed schema, as part of an enterprise-wide knowledge management infrastructure to support collaboration and content management, will promote systematic (a) conversion of reference content into a form that approaches speciﬁcations for decision support content; (b) development and reuse of clinical content while ensuring consistency in the information; and (c) support an open and distributed review process among leadership, content matter experts and end-users.