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2. Comparisons Between Hypothesis- and Data-Driven Approaches for Multimorbidity Frailty Index: A Machine Learning Approach

Author

Peng, Li-Ning, Hsiao, Fei-Yuan, Lee, Wei-Ju, Huang, Shih-Tsung, and Chen, Liang-Kung

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundUsing big data and the theory of cumulative deficits to develop the multimorbidity frailty index (mFI) has become a widely accepted approach in public health and health care services. However, constructing the mFI using the most critical determinants and stratifying different risk groups with dose-response relationships remain major challenges in clinical practice. ObjectiveThis study aimed to develop the mFI by using machine learning methods that select variables based on the optimal fitness of the model. In addition, we aimed to further establish 4 entities of risk using a machine learning approach that would achieve the best distinction between groups and demonstrate the dose-response relationship. MethodsIn this study, we used Taiwan’s National Health Insurance Research Database to develop a machine learning multimorbidity frailty index (ML-mFI) using the theory of cumulative diseases/deficits of an individual older person. Compared to the conventional mFI, in which the selection of diseases/deficits is based on expert opinion, we adopted the random forest method to select the most influential diseases/deficits that predict adverse outcomes for older people. To ensure that the survival curves showed a dose-response relationship with overlap during the follow-up, we developed the distance index and coverage index, which can be used at any time point to classify the ML-mFI of all subjects into the categories of fit, mild frailty, moderate frailty, and severe frailty. Survival analysis was conducted to evaluate the ability of the ML-mFI to predict adverse outcomes, such as unplanned hospitalizations, intensive care unit (ICU) admissions, and mortality. ResultsThe final ML-mFI model contained 38 diseases/deficits. Compared with conventional mFI, both indices had similar distribution patterns by age and sex; however, among people aged 65 to 69 years, the mean mFI and ML-mFI were 0.037 (SD 0.048) and 0.0070 (SD 0.0254), respectively. The difference may result from discrepancies in the diseases/deficits selected in the mFI and the ML-mFI. A total of 86,133 subjects aged 65 to 100 years were included in this study and were categorized into 4 groups according to the ML-mFI. Both the Kaplan-Meier survival curves and Cox models showed that the ML-mFI significantly predicted all outcomes of interest, including all-cause mortality, unplanned hospitalizations, and all-cause ICU admissions at 1, 5, and 8 years of follow-up (P

Published
2020
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3. Deep denoising autoencoder-based non-invasive blood flow detection for arteriovenous fistula

Author

Chen, Li-Chin, Lin, Yi-Heng, Peng, Li-Ning, Wang, Feng-Ming, Chen, Yu-Hsin, Huang, Po-Hsun, Yang, Shang-Feng, and Tsao, Yu

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Signal Processing
Abstract

Clinical guidelines underscore the importance of regularly monitoring and surveilling arteriovenous fistula (AVF) access in hemodialysis patients to promptly detect any dysfunction. Although phono-angiography/sound analysis overcomes the limitations of standardized AVF stenosis diagnosis tool, prior studies have depended on conventional feature extraction methods, restricting their applicability in diverse contexts. In contrast, representation learning captures fundamental underlying factors that can be readily transferred across different contexts. We propose an approach based on deep denoising autoencoders (DAEs) that perform dimensionality reduction and reconstruction tasks using the waveform obtained through one-level discrete wavelet transform, utilizing representation learning. Our results demonstrate that the latent representation generated by the DAE surpasses expectations with an accuracy of 0.93. The incorporation of noise-mixing and the utilization of a noise-to-clean scheme effectively enhance the discriminative capabilities of the latent representation. Moreover, when employed to identify patient-specific characteristics, the latent representation exhibited performance by surpassing an accuracy of 0.92. Appropriate light-weighted methods can restore the detection performance of the excessively reduced dimensionality version and enable operation on less computational devices. Our findings suggest that representation learning is a more feasible approach for extracting auscultation features in AVF, leading to improved generalization and applicability across multiple tasks. The manipulation of latent representations holds immense potential for future advancements. Further investigations in this area are promising and warrant continued exploration.

Published
2023

46. Diagnosis and outcomes of cachexia in Asia: Working Consensus Report from the Asian Working Group for Cachexia

Author

Arai, Hidenori, primary, Maeda, Keisuke, additional, Wakabayashi, Hidetaka, additional, Naito, Tateaki, additional, Konishi, Masaaki, additional, Assantachai, Prasert, additional, Auyeung, Wai Tung, additional, Chalermsri, Chalobol, additional, Chen, Wei, additional, Chew, Justin, additional, Chou, Ming‐Yueh, additional, Hsu, Chih‐Cheng, additional, Hum, Allyn, additional, Hwang, In Gyu, additional, Kaido, Toshimi, additional, Kang, Lin, additional, Kamaruzzaman, Shahrul Bahyah, additional, Kim, Miji, additional, Lee, Jenny Shun Wah, additional, Lee, Wei‐Ju, additional, Liang, Chih‐Kuang, additional, Lim, Wee Shiong, additional, Lim, Jae‐Young, additional, Lim, Yen Peng, additional, Lo, Raymond See‐Kit, additional, Ong, Terence, additional, Pan, Wen‐Harn, additional, Peng, Li‐Ning, additional, Pramyothin, Pornpoj, additional, Razalli, Nurul Huda, additional, Saitoh, Masakazu, additional, Shahar, Suzana, additional, Shi, Han Ping, additional, Tung, Heng‐Hsin, additional, Uezono, Yasuhito, additional, von Haehling, Stephan, additional, Won, Chang Won, additional, Woo, Jean, additional, and Chen, Liang‐Kung, additional

Published
2023
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