Clinical Decision Support with Guidelines and Bayesian Networks

Nee, Oliver; Hein, Andreas

doi:10.5772/39394

Cited by 8 publications

(7 citation statements)

References 24 publications

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“…This is especially true given the fact that clinical practices have to deal with a myriad of complexities including different types of diseases and disorders, patient lifestyles, nutrition, level of patient care, and other necessary factors that are of value when it comes to decision making. Nee and Hein (21) describe the use of Bayesian networks on cardiac tele-rehabilitation for patients with myocardial infarction. Here Bayesian networks have been used effectively to reduce the possibility of false alarms.…”

Section: Current State Of Semantic Network For Healthcare Data Manage...mentioning

confidence: 99%

Key observations in terms of management of electronic health records from a mHealth perspective

Gurupur¹

2022

mHealth

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The article is a narrative review that briefly describes some of the recent advances in healthcare data management that will have positive effect on mHealth. The advances described in this article are in fact innovation introduced by the author to the field of data management with respect to electronic health records. The research delineated is transdisciplinary in nature and will potentially have positive impact on healthcare outcomes. Also, the article illustrates the necessity for an out of the box thinking approach to improve mHealth while discussing the current impending issues related to data incompleteness of electronic health records and the much-needed decision support systems for mHealth. It is to be noted that most of the electronic health records are now accessed by patients through mobile devices. These mobile devices will run as clients while much of the heavy computing is performed using servers. Here it is important to discuss some of the important technologies and methods used for decision making. The article attempts to present a discussion on how this myriad of intertwining technologies support this decision making with respect to electronic health records. More importantly it is these processes that assist in decision making and efficiency for both mHealth users and providers. In this respect, the article first provides insights on the complexities of decision making involved with electronic health records. This is followed by a discussion on the problem of data incompleteness of electronic health records. Finally, the author provides some insights into the gravity of the problem of data incompleteness in terms of revenue loss/gain for healthcare providers.

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Section: Current State Of Semantic Network For Healthcare Data Manage...mentioning

confidence: 99%

Key observations in terms of management of electronic health records from a mHealth perspective

Gurupur¹

2022

mHealth

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“…CDSS is built based on Bayesian network [3] but it can be built using neural network or genetic algorithms, too. It would be a part of system designed for cardiac telerehabilitation for patients after infarction.…”

Section: Related Workmentioning

confidence: 99%

“…It would be a part of system designed for cardiac telerehabilitation for patients after infarction. The system replaces a rule-based variant that had been used to control the patient's exercise session, including the generation of alarms [3]. The evaluation shows that the number of false alarm can be reduced by introducing of Bayesian networks.…”

Section: Related Workmentioning

confidence: 99%

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Stroke Prediction Context-Aware Health Care System

Mcheick

Nasser

Dbouk

et al. 2016

2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)

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Abstract-This paper proposes a prediction framework based on ontology and Bayesian Belief Networks BBN to support a medical teams in every daily. We propose a Stroke Prediction System (SPS), a new software component to handle the uncertainty of having a stroke disease by determining the risk score level. This is composed of four layers: acquisition of data, aggregation, reasoning and application. SPS senses, collects, and analyzes data of a patient, then uses wearable sensors and the mobile application to interact with the patient and staffs. When the risk reaches critical limits, SPS notifies all concerned parties; the patient, the doctor, and the emergency department. The patient profile is also updated to reflect this urgent intervention requirement. A Bayesian model is designed and implemented using the Netica tool to prove its efficiency i) by handling patient context remotely and verifying its changes locally and ii) on predicting missing probabilities and calculate the probability of high risk level for emergency cases. The SPS system improves the accuracy of decision making and uses a new ontology of stroke disease inspired from our Parkinson ontology already developed.

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“…Due to their graphical representation, Bayesian networks are relatively easy to understand and to create and can therefore be used, developed, and interpreted by domain experts [9]. They can often be seen as a model of cause-effect relationships [4] whereby their structure and the underlying probability distribution can be learnt from data or be created by hand.…”

Section: Bayesian Network For Automatic Triage Diagnosismentioning

confidence: 99%