Deep Learning and Explainable AI in Healthcare Using EHR

Khedkar, Sujata; Gandhi, Priyanka; Shinde, Gayatri; Subramanian, Vignesh

doi:10.1007/978-3-030-33966-1_7

Cited by 30 publications

(16 citation statements)

References 2 publications

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“…In [ 35 ], RF is introduced as one useful machine learning tool for healthcare domain, especially for COVID-19 modeling. Khedkar et al [ 36 ] use Patients Electronic Health Records for predicting the heart failure risks by RF. Hane et al [ 37 ] propose a model for prediction of the dissolution behaviour of a wide variety of oxide glasses.…”

Section: Related Workmentioning

confidence: 99%

“…At each instant n, the action probability vector pi(n) is updated by the linear learning algorithm given in equation ( 13) if the chosen action ai(k) is rewarded by the environment, and it is updated according to equation ( 14) if the chosen action is penalized [104]. [11], [12], [13] Global problem [26], [27], [28] Healthcare [32], [33], [34], [35], [36], [41], [98], [37], [39], [40], [42], [43], [45], [46], [47], [48], [49], [50], [51], Industrial [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62] Network [63], [67], [68], [69], [99], [100] Physics [71], [72] Text processing …”

Section: Learning Automatamentioning

confidence: 99%

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A New Random Forest Algorithm Based on Learning Automata

Savargiv

Masoumi

Keyvanpour

2021

Computational Intelligence and Neuroscience

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The goal of aggregating the base classifiers is to achieve an aggregated classifier that has a higher resolution than individual classifiers. Random forest is one of the types of ensemble learning methods that have been considered more than other ensemble learning methods due to its simple structure, ease of understanding, as well as higher efficiency than similar methods. The ability and efficiency of classical methods are always influenced by the data. The capabilities of independence from the data domain, and the ability to adapt to problem space conditions, are the most challenging issues about the different types of classifiers. In this paper, a method based on learning automata is presented, through which the adaptive capabilities of the problem space, as well as the independence of the data domain, are added to the random forest to increase its efficiency. Using the idea of reinforcement learning in the random forest has made it possible to address issues with data that have a dynamic behaviour. Dynamic behaviour refers to the variability in the behaviour of a data sample in different domains. Therefore, to evaluate the proposed method, and to create an environment with dynamic behaviour, different domains of data have been considered. In the proposed method, the idea is added to the random forest using learning automata. The reason for this choice is the simple structure of the learning automata and the compatibility of the learning automata with the problem space. The evaluation results confirm the improvement of random forest efficiency.

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Section: Related Workmentioning

confidence: 99%

Section: Learning Automatamentioning

confidence: 99%

A New Random Forest Algorithm Based on Learning Automata

Savargiv

Masoumi

Keyvanpour

2021

Computational Intelligence and Neuroscience

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“…As noted in [16], interpretability of a machine learning model is essential in medical applications as it helps build trust between medical personnel and predictive models. However, getting precise and intuitive interpretations of deep neural networks is a challenge [36].…”

Section: Interpretability Analysismentioning

confidence: 99%

TLSurv

Jiang

Alford

Ketchum

et al. 2020

Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

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“…Lastly on the list within this section, Khedkar et al [46] proposed an explainable DL system for healthcare using electronic health records. The use of a responsiveness mechanisms and recurrent neural network on a human epidermal receptor is deliberated for forecasting myocardial infarction of patients and the distinguishments that have steered to the extrapolation.…”

Section: Role Of DL In Improvingmentioning

confidence: 99%

A 5G Beam Selection Machine Learning Algorithm for Unmanned Aerial Vehicle Applications

Shafik

Matinkhah

Ahmad

2020

Wireless Communications and Mobile Computing

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The unmanned aerial vehicles (UAVs) emerged into a promising research trend within the recurrent year where current and future networks are to use enhanced connectivity in these digital immigrations in different fields like medical, communication, and search and rescue operations among others. The current technologies are using fixed base stations to operate onsite and off-site in the fixed position with its associated problems like poor connectivity. This open gate for the UAV technology is to be used as a mobile alternative to increase accessibility with fifth-generation (5G) connectivity that focuses on increased availability and connectivity. There has been less usage of wireless technologies in the medical field. This paper first presents a study on deep learning to medical field application in general and provides detailed steps that are involved in the multiarmed bandit (MAB) approach in solving the UAV biomedical engineering technology device and medical exploration to exploitation dilemma. The paper further presents a detailed description of the bandit network applicability to achieve close optimal performance and efficiency of medical engineered devices. The simulated results depicted that a multiarmed bandit problem approach can be applied in optimizing the performance of any medical networked device issue compared to the Thompson sampling, Bayesian algorithm, and ε-greedy algorithm. The results obtained further illustrated the optimized utilization of biomedical engineering technology systems achieving thus close optimal performance on the average period through deep learning of realistic medical situations.

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Deep Learning and Explainable AI in Healthcare Using EHR

Cited by 30 publications

References 2 publications

A New Random Forest Algorithm Based on Learning Automata

A New Random Forest Algorithm Based on Learning Automata

TLSurv

A 5G Beam Selection Machine Learning Algorithm for Unmanned Aerial Vehicle Applications

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