If the theory is satisfiable, then there are no direct interactions. Otherwise the theory needs to be revised by applying revision operators. These revision operators capture expert knowledge that is not encoded in CPGs and they are formally defined later in the text. The second phase of the mitigation process aims at identifying and addressing indirect adverse interactions.
Checking the satisfiability of D comb is not sufficient for identifying indirect interactions and additional expert knowledge is required. This knowledge is encoded in form of interaction operators. An interaction operator IO k is formally defined as. Encountered interactions both direct and indirect need to be addressed by applying relevant revision operators to D comb. A revision operator RO k is formally defined as. To ensure that patient information is not modified and general practices of mitigation are not violated as part of the revision process, the operations Op k are only applicable to a subset of the theories that comprise D comb — namely D c p g d 1 , D c p g d 2 and D mit.
Furthermore there are three possible types of operations in Op k - removal , addition , and substitution. In the context of FOL, a consistent combined therapy is a model of D comb that represents an assignment of values to predicates. To ground the theoretical concepts presented above, we use the following case study.
As such, his sugar level is being controlled by a daily insulin dosage of 40 international units. The patient also suffers from relatively mild rheumatoid arthritis RA comorbid condition that is being managed using a maintenance dosage of plaquenil. For the sake of simplicity we only present the relevant interaction and revision operators and omit the details of the FOL-based theories D c p g db 2 and D c p g ra representing CPGs for type 2 diabetes and a rheumatoid arthritis.
In the first scenario we assume there is a sudden relapse of RA accompanied by the onset of severe pain and significantly reduced mobility. Typically in such cases, the patient is initiated on glucocorticoid treatment to control the onset of pain. However, considering that the patient is diabetic, the increased sugar level associated with the administration of glucocorticoids needs to be mitigated with an increased maintenance dosage of insulin. In this clinical scenario, the daily dosage is increased to 48 international units and maintained until sugar level stabilizes or the patient no longer requires glucocorticoid therapy.
Supporting this scenario requires the codification of secondary knowledge describing the use of and interactions with glucocorticoids. The interaction operator IO 1 represents the drug-drug interaction when a DB2 patient, also being treated for RA, is prescribed with glucocorticoids while taking insulin. We note the definition of the operation in Op 1 employs variable X. The following sentence is part of the theory for DB2 prescribing 40 international units of insulin to the patient.
Note the use of variables to support different values for the same expression, rather then creating an expression for each possible value. In this clinical scenario we consider the same patient that was initiated on glucocorticoid therapy supplemented with a calcium antagonist in place of a NSAID that is not recommended for diabetic conditions. The daily insulin dosage was increased as explained in Scenario 1 to manage the elevated sugar level, however the prescribed therapy did not work as expected.
In order to better control the relapse of rheumatoid arthritis, the next therapeutic option is to put the patient on DMARD combination therapy that normally includes cyclosporine. While all immunosuppressive medications are diabetogenic with hyperglycemia being a common adverse event , some like azathioprine proved in clinical trials to be better tolerated by type 2 diabetics.
Therefore, the revised DMARD combination therapy is prescribed for this patient, and it uses azathioprine as a replacement for cyclosporine. As we show below, this revision also maintains the precedence of executed tasks when performing the replacement.
Using patient information represented in D pi above we infer that RO 2 is in fact applicable to this patient encounter and we apply the corresponding logical expressions to the theory. We note here that we are revising the theory for the patient to replace cyclosporine with azathioprine and maintain the order in which tasks are to be executed according to the CPG for RA not shown here due to space limitations.
Similarly to the first clinical scenario, the removal of sentences is done from D c p g ra and sentences are added to D mit. In this paper we described our preliminary research on developing a general theory of mitigation expressed in FOL for concurrently applied CPGs. This research builds upon our foundational work in using the CLP paradigm to handle mitigation and extends it by using a paradigm with greater expressive power FOL to handle temporal relationships such as task precedence.
We used a case study of a patient suffering from type 2 diabetes while being treated for an onset of severe rheumatoid arthritis to illustrate the added benefit of our new approach. Through two simple scenarios we demonstrated the power of our FOL-based approach by applying an adjustment of medication dosage and through the substitution of tasks while maintaining the task execution order as defined by one of the CPGs.
Our proposed FOL-based approach provides an expressive and robust language in order to represent and apply temporal relationships represented in CPGs while also easily capturing common knowledge applicable to all mitigation scenarios. Specifically, we limit the addition, deletion, and substitution of logical sentences to the theories representing CPGs D c p g d i and to the theory representing added mitigation actions D mit. Additionally, the common characteristics of mitigation, applicable to all mitigation instances, are maintained in a single theory D common and cannot be altered by any mitigation operations.
As future research, we are exploring ways to make the proposed approach more general and robust. As stated at the beginning, our ultimate goal is to develop a general framework of mitigation and towards this end we are studying various clinical situations involving comorbid patients to extract the full set of properties that hold across all mitigation scenarios. Furthermore, we are working on inductive reasoning techniques to automatically infer precedence relationships as logical operations are applied to the theories representing CPGs.
The addition, deletion, and substitution of logical sentences impacts the underlying structure represented by these theories and automating the maintenance of correct precedence relationships goes a long way to realizing our goal of using FOL-based methods to drive a point-of-care clinical decision support system.
National Center for Biotechnology Information , U. Author information Copyright and License information Disclaimer. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose. This article has been cited by other articles in PMC. Abstract Clinical practice guidelines CPGs implement evidence-based medicine designed to help generate a therapy for a patient suffering from a single disease.
Introduction Clinical practice guidelines CPGs , as knowledge-based tools for disease-specific patient management 5 , encapsulate evidence-based practices for devising the most appropriate treatment for patients with regard to relevant patient information and possible diagnoses. Methodology In order to better illustrate the proposed FOL-based mitigation of guidelines, we start with brief introductions of the basic concepts and notation of FOL and theorem proving.
Table 1. Defined predicates. Predicate Explanation node x x is a node in AG disease d CPG is associated with disease d action x x is an action node in AG diagnosed d disease d is diagnosed for the given patient decision x x is a decision node in AG executed x task node x is or has been executed value x, v value v is associated with decision node x dosage x, n task node x is characterized by dosage n directPrec x , y node x directly precedes node y in AG there is an arc from x to y prec x , y node x precedes node y in AG there is a path from x to y.
Open in a separate window. FOL-based Mitigation of Adverse Interactions The process of mitigating identifying and addressing adverse interactions consists of two main phases. Case Study: Management of a Patient with Type 2 Diabetes and an Onset of Severe Rheumatoid Arthritis To ground the theoretical concepts presented above, we use the following case study. Clinical Scenario 1: Managing the Administration of Glucocorticoids In the first scenario we assume there is a sudden relapse of RA accompanied by the onset of severe pain and significantly reduced mobility.
Clinical Scenario 2: Managing Immunosuppressive Medication In this clinical scenario we consider the same patient that was initiated on glucocorticoid therapy supplemented with a calcium antagonist in place of a NSAID that is not recommended for diabetic conditions. Discussion and Conclusions In this paper we described our preliminary research on developing a general theory of mitigation expressed in FOL for concurrently applied CPGs. References 1. Peleg M. Computer-interpretable clinical guidelines: a methodological review.
J Biomed Inform. Agent-based execution of personalised home care treatments. Appl Intell. Mitigation of adverse interactions in pairs of clinical practice guidelines using constraint logic programming. Clinical practice guideline development manual: a quality-driven approach for translating evidence into action.
Otolaryngol Head Neck Surg. Grand challenges in clinical decision support. Delivering clinical decision support services: there is nothing as practical as a good theory. Using constraint logic programming to implement iterative actions and numerical measures during mitigation of concurrently applied clinical practice guidelines; Artificial Intelligence in Medicine, 14th Conference on Artificial Intelligence in Medicine, AIME ; Murcia, Spain.
Citation Type. Has PDF. Publication Type. More Filters. Research Feed. An inductive logic programming approach to statistical relational learning. View 2 excerpts, references background and methods. Logical Settings for Concept-Learning. Highly Influential. View 3 excerpts, references background and methods. Learning First-Order Definitions of Functions. View 1 excerpt, references methods.
Strongly Typed Inductive Concept Learning. View 1 excerpt, references background. Inductive Constraint Logic. View 5 excerpts, references methods and background.
|Order logic case study||336|
|Sample ap essays 9||Entry level it project manager resume|
|Pay to write logic application letter||547|
|Resume format for electronic engineers||Researh essay topics|
|Order logic case study||Business analysis essay example|
|Latex documentclass resume||Using the m-estimate in rule induction. Dzeroski, Corporate entrepreneurship research paper. D common — a common theory that axiomatizes the universal characteristics of CPGs as part of a FOL-based representation for mitigation. Communications of the ACM—, Berger, V. In this clinical scenario, the daily dosage is increased to 48 international units and maintained until sugar level stabilizes or the patient no longer requires glucocorticoid therapy. This problem was identified as one of the major shortcoming of CPG uptake in practice and as such there exists a need for research to address it 1.|
|Order logic case study||Next we present a case study to ground the theory in a clinical example, and we conclude with a discussion of our contributions and potential areas for future work. Using patient information represented in D pi above we infer that RO 2 is in fact applicable to this patient encounter and we apply the corresponding logical expressions to the theory. Olshen, and C. Bergadano and D. De Order logic case study and S. In the context of FOL, a consistent combined therapy is a model of D comb that represents an assignment of values to predicates. National Center for Biotechnology InformationU.|
|Popular critical analysis essay ghostwriting service for mba||120|