A Comparative Study of Machine Learning Algorithms as Expert Systems in Medical Diagnosis (Asthma)

Prasad, Bhanu; Prasad, P. E. S. N. Krishna; Yeruva, Sagar

doi:10.1007/978-3-642-17857-3_56

Cited by 20 publications

(9 citation statements)

References 4 publications

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“…Thus, data-driven approaches leveraging large EMR data with machine learning have begun to supplement or supplant traditional expert-based approaches to asthma studies. Machine learning models have been applied in various asthma studies to ascertain asthma status [39, 51, 50], detect airway obstruction in asthma [1], distinguish asthma phenotypes [20], predict subgroups of asthma and eczema [40], and predict asthma exacerbation [16, 52].…”

Section: Related Workmentioning

confidence: 99%

Modeling asynchronous event sequences with RNNs

Liu

Sohn

et al. 2018

Journal of Biomedical Informatics

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Sequences of events have often been modeled with computational techniques, but typical preprocessing steps and problem settings do not explicitly address the ramifications of timestamped events. Clinical data, such as is found in electronic health records (EHRs), typically comes with timestamp information. In this work, we define event sequences and their properties: synchronicity, evenness, and co-cardinality; we then show how asynchronous, uneven, and multi-cardinal problem settings can support explicit accountings of relative time. Our evaluation uses the temporally sensitive clinical use case of pediatric asthma, which is a chronic disease with symptoms (and lack thereof) evolving over time. We show several approaches to explicitly incorporating relative time into a recurrent neural network (RNN) model that improve the overall classification of patients into those with no asthma, those with persistent asthma, those in long-term remission, and those who have experienced relapse. We also compare and contrast these results with those in an inpatient intensive care setting.

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Section: Related Workmentioning

confidence: 99%

Modeling asynchronous event sequences with RNNs

Liu

Sohn

et al. 2018

Journal of Biomedical Informatics

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“…Prasad et al [18] conducted a comparative study of AutoAssociative Memory Neural Networks, ID3 (Iterative Dichotomized3), C4.5, and Bayesian networks algorithms. The comparison aims at identifying the best technique for diagnosing Asthma diseases.…”

Section: Classical Data Mining (Dm) Techniques For Diseases Diagnosismentioning

confidence: 99%

Electronic Health Records: Applications, Techniques and Challenges

Zaied¹,

Elmogy²,

Elkader³

2015

IJCA

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With the fast evolution of information technology, traditional healthcare is moving towards a more electronic stage. As a result, the e-Health term appears, and Electronic Health Records (EHRs) become a critical application of e-Health management systems. These computerized records have been widely used by clinicians, healthcare providers, patients, and health insurance companies for the purpose of creating, managing, and accessing the health information of patients everywhere. Moreover, these information resources can be shared by different healthcare parties for monitoring the patients' health, delivering effective treatments, and decreasing costs. In this paper, we present a survey of various EHR applications in e-Health systems. These applications include using EHRs for diagnosing, monitoring diseases, and selecting the most efficient paths of treatments. In addition, we discuss the usage of EHRs as a source for building a knowledge base for Clinical Decision Support Systems (CDSS). Finally, the challenges of EHR implementations in the healthcare environment and current research topics will be highlighted.

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“…Prasad et al ( [15]) used Machine learning algorithms such as Auto-associative memory neural networks, Bayesian networks, ID3 and C4.5 for diagnosing the Asthma .They presented a comparative study among these algorithms with the use medical expert systems on patient data. They gathered the clinical signs and symptoms of asthma of patients from various resources.…”

Section: Papers That Used Id3 Decision Treementioning

confidence: 99%

Labeling Customers Using Discovered Knowledge Case Study: Automobile Insurance Industry

Azadmanesh¹,

Tarokh

2012

IJMVSC

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In this paper, we used the knowledge discovery in databases and data mining, one of the data-based decision support techniques to help labeling customers in the automobile insurance industry. In most data mining application cases, major tasks including data preparation, data preprocessing, data transformation, data mining, interpretation, application and evaluation, are Keywords labeling customers, discovered knowledge, decision tree, ID3 IntroductionCustomer relationship management (CRM) includes a set of processes and it enables systems to support a business strategy to build long term, profitable relationships with specific customers [11]. Customer data and information technology (IT) tools form the foundation of any successful CRM strategy. Also, the rapid growth of the Internet and its connected technologies has extremely increased the opportunities for marketing and transformed the way that relationships between companies and their customers are managed [14].From the architecture point of view, the CRM framework can be classified into operational and analytical ([1]; [8]; [21]). Operational CRM apply to the automation of business processes, while analytical CRM refers to the analysis of customer characteristics and behaviors to support the organization's customer management strategies. Analytical CRM could help an organization to better separate and more effectively allocate resources to the most profitable group of customers. Data mining tools are a popular means of analyzing customer data within the analytical CRM framework. Many organizations have collected and stored a wealth of data about their current customers, potential customers, suppliers and business partners. However, the inability to explore valuable information hidden in the data prevents the organizations from transforming these data into valuable and useful knowledge [1]. In order to effectively reveal information in handling data, information technology has introduced the development and application of the techniques of knowledge discovery (KD) and data mining (DM). KD/DM is basically a dataoriented method that merges database management, knowledge representation, and machine

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A Comparative Study of Machine Learning Algorithms as Expert Systems in Medical Diagnosis (Asthma)

Cited by 20 publications

References 4 publications

Modeling asynchronous event sequences with RNNs

Modeling asynchronous event sequences with RNNs

Electronic Health Records: Applications, Techniques and Challenges

Labeling Customers Using Discovered Knowledge Case Study: Automobile Insurance Industry

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