Semantic Search Personalized Data as Special Method of Processing Medical Information

Melnykova, Nataliia

doi:10.1007/978-3-319-45991-2_22

Cited by 8 publications

(6 citation statements)

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“…The Euclidean distance is usually used as a measure of proximity. Using this method the number of clusters is unknown and selected by the researcher, and the quality of the clustering depends on the primary separating by elbow method [22,23,24].…”

Section: Discussionmentioning

confidence: 99%

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Data Driven Analytics for Personalized Medical Decision Making

Melnykova¹,

Shakhovska²,

ml.³

et al. 2020

Preprint

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The study was conducted on applying machine learning and data mining methods to personalizing the treatment. This allows investigating individual patient characteristics. Personalization is built on the clustering method and associative rules. It was suggested to determine the average distance between instances for optimal performance metrics finding. The formalization of the medical data pre-processing stage for finding personalized solutions based on current standards and pharmaceutical protocols is proposed. The model of patient data is built. The paper presents the novel approach to clustering built on ensemble of cluster algorithm with better than k-means algorithm Hopkins metrics. The personalized treatment usually is based on decision tree. Such approach requires a lot of computation time and cannot be paralyzed. Therefore, it is proposed to classify persons by conditions, to determine deviations of parameters from the normative parameters of the group, as well as the average parameters. This made it possible to create a personalized approach to treatment for each patient based on long-term monitoring. According to the results of the analysis, it becomes possible to predict the optimal conditions for a particular patient and to find the medicaments treatment according to personal characteristics.

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Section: Discussionmentioning

confidence: 99%

“…A, B are the parameter matrices consist of object parameters [23,24]. So, the medical data is represented of multidimensional dataset.…”

Section: Model Of Patient Datamentioning

confidence: 99%

Data Driven Analytics for Personalized Medical Decision Making

Melnykova¹,

Shakhovska²,

ml.³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The total sum of the square value was 43.5%. A and B are the parameter matrices, which consist of object parameters [29].…”

Section: Analysis Based On the Existing Clustering Methodsmentioning

confidence: 99%

Data-Driven Analytics for Personalized Medical Decision Making

et al. 2020

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The study was conducted by applying machine learning and data mining methods to treatment personalization. This allows individual patient characteristics to be investigated. The personalization method was built on the clustering method and associative rules. It was suggested to determine the average distance between instances in order to find the optimal performance metrics. The formalization of the medical data preprocessing stage was proposed in order to find personalized solutions based on current standards and pharmaceutical protocols. The patient data model was built using time-dependent and time-independent parameters. Personalized treatment is usually based on the decision tree method. This approach requires significant computation time and cannot be parallelized. Therefore, it was proposed to group people by conditions and to determine deviations of parameters from the normative parameters of the group, as well as the average parameters. The novelty of the paper is the new clustering method, which was built from an ensemble of cluster algorithms, and the usage of the new distance measure with Hopkins metrics, which were 0.13 less than for the k-means method. The Dunn index was 0.03 higher than for the BIRCH (balanced iterative reducing and clustering using hierarchies) algorithm. The next stage was the mining of associative rules provided separately for each cluster. This allows a personalized approach to treatment to be created for each patient based on long-term monitoring. The correctness level of the proposed medical decisions is 86%, which was approved by experts.

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“…Головною особливістю машинного перекладу є різна довжина для введення та виведення. Можливість працювати з різною довжиною входу та виходу надає нам підхід рекурентних нейронних мереж [1][2][3][4][5].…”

Section: постановка проблеми у загальному вигляді та її зв'язок із ва...unclassified

“…З точки зору програмування в таких мережах є аналог циклічного виконання, а з точки зору системи -такі мережі еквівалентні кінцевому автомату. RNN зазвичай використовуються для обробки послідовності слів у обробці природної мови [4][5][6][7]. Зазвичай для обробки послідовності слів використовують приховану модель Маркова (HMM) і модель мови N-програм.…”

Section: постановка проблеми у загальному вигляді та її зв'язок із ва...unclassified

Multimodal Speech Recognition Based on Audio and Text Data

BASYSTIUK¹,

Melnykova²

2022

Herald KhmNU.TS

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Systems of machine translation of texts from one language to another simulate the work of a human translator. Their performance depends on the ability to understand the grammar rules of the language. In translation, the basic units are not individual words, but word combinations or phraseological units that express different concepts. Only by using them, more complex ideas can be expressed through the translated text. The main feature of machine translation is different length for input and output. The ability to work with different lengths of input and output provides us with the approach of recurrent neural networks. A recurrent neural network (RNN) is a class of artificial neural network that has connections between nodes. In this case, a connection refers to a connection from a more distant node to a less distant node. The presence of connections allows the RNN to remember and reproduce the entire sequence of reactions to one stimulus. From the point of view of programming, such networks are analogous to cyclic execution, and from the point of view of the system, such networks are equivalent to a state machine. RNNs are commonly used to process word sequences in natural language processing. Usually, a hidden Markov model (HMM) and an N-program language model are used to process a sequence of words. Deep learning has completely changed the approach to machine translation. Researchers in the deep learning field has created simple solutions based on machine learning that outperform the best expert systems. In this paper was reviewed the main features of machine translation based on recurrent neural networks. The advantages of systems based on RNN using the sequence-to-sequence model against statistical translation systems are also highlighted in the article. Two machine translation systems based on the sequence-to-sequence model were constructed using Keras and PyTorch machine learning libraries. Based on the obtained results, libraries analysis was done, and their performance comparison.

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Semantic Search Personalized Data as Special Method of Processing Medical Information

Cited by 8 publications

References 1 publication

Data Driven Analytics for Personalized Medical Decision Making

Data Driven Analytics for Personalized Medical Decision Making

Data-Driven Analytics for Personalized Medical Decision Making

Multimodal Speech Recognition Based on Audio and Text Data

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