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15 pages, 8272 KiB

Open AccessArticle

Model Interpretation Considering Both Time and Frequency Axes Given Time Series Data

by Woonghee Lee, Gayeon Kim, Jeonghyeon Yu and Younghoon Kim

Appl. Sci. 2022, 12(24), 12807; https://0-doi-org.brum.beds.ac.uk/10.3390/app122412807 - 13 Dec 2022

Cited by 2 | Viewed by 1422

Recently, deep learning-based models have emerged in the medical domain. Although those models achieve high performance, it is difficult to directly apply them in practice. Specifically, most models are not considered reliable yet, while they are not interpretable. Therefore, researchers attempt to interpret [...] Read more.

Recently, deep learning-based models have emerged in the medical domain. Although those models achieve high performance, it is difficult to directly apply them in practice. Specifically, most models are not considered reliable yet, while they are not interpretable. Therefore, researchers attempt to interpret their own deep learning applications. However, the interpretation is task specific or only appropriate for image data such as computed tomography (CT) scans and magnetic resonance imaging (MRI). Currently, few works focus on the model interpretation given time series data such as electroencephalography (EEG) and electrocardiography (ECG) using LIME. Because the explanation generated by LIME is from the permutation of the divided input data, the performance of interpretation is highly dependent on the split method. In the medical domain, for the time series data, existing interpretations consider only the time axis, whereas physicians take account of the frequency too. In this work, we propose the model interpretation using LIME considering both time and frequency axes. Our key idea is that we divide the input signal using graph-based image clustering after transforming it using short-time Fourier transform, which is utilized to capture the change of frequency content over time. In our experiments, we utilize real-world data, which is EEG signals recorded from patients during polysomnographic (PSG) studies, as well as prove that ours captures a significantly more critical explanation than the state-of-the-art. In addition, we show that the representation obtained by ours reflects the physician’s standard such as K-complexes and delta waves, which are considered strong evidence of the second sleep stage and a clue of the third sleep stage. We expect that our work can be applied to establish computer-aided diagnosis as well as to measure the reliability of deep learning models taking the time series into them. Full article

(This article belongs to the Special Issue Big Data Intelligence in Healthcare Applications)

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13 pages, 2246 KiB

Open AccessArticle

Modeling Health Data Using Machine Learning Techniques Applied to Financial Management Predictions

by Rafael Leon Sanz and Pilar Leon-Sanz

Appl. Sci. 2022, 12(23), 12148; https://0-doi-org.brum.beds.ac.uk/10.3390/app122312148 - 28 Nov 2022

Cited by 3 | Viewed by 1929

Abstract

Health management has steadily improved in performance and accuracy using IT technology. Hospitals and health institutions hold an enormous number of data in their software applications, which can be used with Big Data methodologies to extract useful information. One of the most challenging [...] Read more.

Health management has steadily improved in performance and accuracy using IT technology. Hospitals and health institutions hold an enormous number of data in their software applications, which can be used with Big Data methodologies to extract useful information. One of the most challenging aspects of health institutional management is financial management; billing prediction is a key aspect to maintain a predictable service level for patients, avoiding unpleasant surprises and anticipating treasury management. Using patient data from public patient databases and applying a machine learning approach, this article offers a model that helps to make more precise and detailed financial plans. Full article

(This article belongs to the Special Issue Big Data Intelligence in Healthcare Applications)

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14 pages, 12819 KiB

Open AccessArticle

Semantic Decomposition and Anomaly Detection of Tympanic Membrane Endoscopic Images

by Dahye Song, In Sik Song, Jaeyoung Kim, June Choi and Yeonjoon Lee

Appl. Sci. 2022, 12(22), 11677; https://0-doi-org.brum.beds.ac.uk/10.3390/app122211677 - 17 Nov 2022

Cited by 1 | Viewed by 2809

Abstract

With the recent development of deep learning, the supervised learning method has been widely applied in otolaryngology. However, its application in real-world clinical settings is difficult because of the inapplicability outside the learning area of the model and difficulty in data collection due [...] Read more.

With the recent development of deep learning, the supervised learning method has been widely applied in otolaryngology. However, its application in real-world clinical settings is difficult because of the inapplicability outside the learning area of the model and difficulty in data collection due to privacy concerns. To solve these limitations, we studied anomaly detection, the task of identifying sample data that do not match the overall data distribution with the Variational Autoencoder (VAE), an unsupervised learning model. However, the VAE makes it difficult to learn complex data, such as tympanic membrane endoscopic images. Accordingly, we preprocess tympanic membrane images using Adaptive Histogram Equalization (AHE) and Canny edge detection for effective anomaly detection. We then had the VAE learn preprocessed data for only normal tympanic membranes and VAE was used to calculate an abnormality score for those differences between the distribution of the normal and abnormal tympanic membrane images. The abnormality score was applied to the K-nearest Neighbor (K-NN) algorithm to classify normal and abnormal tympanic membranes. As a result, we were obtained a total of 1232 normal and abnormal eardrum images, classified with an accuracy of 94.5% using an algorithm that applied only normal tympanic membrane images. Consequently, we propose that unsupervised-learning-based anomaly detection of the tympanic membrane can solve the limitations of existing supervised learning methods. Full article

(This article belongs to the Special Issue Big Data Intelligence in Healthcare Applications)

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