MCPL-Based FT-LSTM: Medical Representation Learning-Based Clinical Prediction Model for Time Series Events

Wang, Lutong; Wang, Hong; Song, Yongsoo; Wang, Qian

doi:10.1109/access.2019.2919683

Cited by 12 publications

(4 citation statements)

References 24 publications

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“…Consequently, in our future work, we will utilize known side-effects to predict modes of action of drugs and to further enrich the action mode information. We will also extend the datasets of drugs by mining from electronic medical records [37].…”

Section: Discussionmentioning

confidence: 99%

Drug Side-Effect Prediction Via Random Walk on the Signed Heterogeneous Drug Network

Hong

2019

Molecules

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Drug side-effects have become a major public health concern as they are the underlying cause of over a million serious injuries and deaths each year. Therefore, it is of critical importance to detect side-effects as early as possible. Existing computational methods mainly utilize the drug chemical profile and the drug biological profile to predict the side-effects of a drug. In the utilized drug biological profile information, they only focus on drug–target interactions and neglect the modes of action of drugs on target proteins. In this paper, we develop a new method for predicting potential side-effects of drugs based on more comprehensive drug information in which the modes of action of drugs on target proteins are integrated. Drug information of multiple types is modeled as a signed heterogeneous information network. We propose a signed heterogeneous information network embedding framework for learning drug embeddings and predicting side-effects of drugs. We use two bias random walk procedures to obtain drug sequences and train a Skip-gram model to learn drug embeddings. We experimentally demonstrate the performance of the proposed method by comparison with state-of-the-art methods. Furthermore, the results of a case study support our hypothesis that modes of action of drugs on target proteins are meaningful in side-effect prediction.

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Section: Discussionmentioning

confidence: 99%

Drug Side-Effect Prediction Via Random Walk on the Signed Heterogeneous Drug Network

Hong

2019

Molecules

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“…Therefore, it is important to predict the operation status of the fused magnesium furnace in the future. At present, the time series prediction method has achieved good results in the fields of machinery, [31] medical treatment, [32,33] and power systems. [34] Its principle lies in, on the one hand, that it recognizes the continuity of the development of things, and can infer the development trend of things by using the historical data for statistical analysis.…”

Section: Operation Status Prediction For a Fused Magnesium Furnacementioning

confidence: 99%

Self‐optimization for smelting process of fused magnesium furnace based on operation status assessment

2021

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Based on operation status assessment and self‐optimizing regulation, an optimization strategy for the smelting process of a fused magnesium furnace is proposed to improve the benefits of smelting magnesia. In the online evaluation stage, according to the qualitative information, the data is divided by the fuzzy model recognition method, and then the posterior probability of online data in different Gaussian mixture models is calculated to obtain the evaluation results. When the evaluation results are not optimal, the non‐optimal variables are obtained by calculating the contribution rate, and then the self‐optimizing adjustment is carried out by case search. In order to improve the recovery rate of self‐optimizing regulation, a time series prediction method is used to predict the operation status, and the corresponding operation schemes for different prediction results are proposed. Experimental results show that the proposed method is accurate and effective.

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“…The use of Artificial Intelligence (AI) models in prognosis studies has gained traction increasingly in recent years due to its ability to handle large amounts of messy data (16), to learn from different types of data (17), and to facilitate clinical management of patients (18). Researchers have incorporated AI models in prognosis in clinical cancer research, such as breast cancer with Support Vector Machine (SVM) (19), colorectal cancer with Long Short-Term Memory (LSTM) (20), and glioblastoma with Prognosis Enhanced Neural Network (PENN) (21).…”

Section: Introductionmentioning

confidence: 99%

Profiling hearing aid users through big data explainable artificial intelligence techniques

Iliadou

Kikidis

et al. 2022

Front. Neurol.

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Debilitating hearing loss (HL) affects ~6% of the human population. Only 20% of the people in need of a hearing assistive device will eventually seek and acquire one. The number of people that are satisfied with their Hearing Aids (HAids) and continue using them in the long term is even lower. Understanding the personal, behavioral, environmental, or other factors that correlate with the optimal HAid fitting and with users' experience of HAids is a significant step in improving patient satisfaction and quality of life, while reducing societal and financial burden. In SMART BEAR we are addressing this need by making use of the capacity of modern HAids to provide dynamic logging of their operation and by combining this information with a big amount of information about the medical, environmental, and social context of each HAid user. We are studying hearing rehabilitation through a 12-month continuous monitoring of HL patients, collecting data, such as participants' demographics, audiometric and medical data, their cognitive and mental status, their habits, and preferences, through a set of medical devices and wearables, as well as through face-to-face and remote clinical assessments and fitting/fine-tuning sessions. Descriptive, AI-based analysis and assessment of the relationships between heterogeneous data and HL-related parameters will help clinical researchers to better understand the overall health profiles of HL patients, and to identify patterns or relations that may be proven essential for future clinical trials. In addition, the future state and behavioral (e.g., HAids Satisfiability and HAids usage) of the patients will be predicted with time-dependent machine learning models to assist the clinical researchers to decide on the nature of the interventions. Explainable Artificial Intelligence (XAI) techniques will be leveraged to better understand the factors that play a significant role in the success of a hearing rehabilitation program, constructing patient profiles. This paper is a conceptual one aiming to describe the upcoming data collection process and proposed framework for providing a comprehensive profile for patients with HL in the context of EU-funded SMART BEAR project. Such patient profiles can be invaluable in HL treatment as they can help to identify the characteristics making patients more prone to drop out and stop using their HAids, using their HAids sufficiently long during the day, and being more satisfied by their HAids experience. They can also help decrease the number of needed remote sessions with their Audiologist for counseling, and/or HAids fine tuning, or the number of manual changes of HAids program (as indication of poor sound quality and bad adaptation of HAids configuration to patients' real needs and daily challenges), leading to reduced healthcare cost.

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MCPL-Based FT-LSTM: Medical Representation Learning-Based Clinical Prediction Model for Time Series Events

Cited by 12 publications

References 24 publications

Drug Side-Effect Prediction Via Random Walk on the Signed Heterogeneous Drug Network

Drug Side-Effect Prediction Via Random Walk on the Signed Heterogeneous Drug Network

Self‐optimization for smelting process of fused magnesium furnace based on operation status assessment

Profiling hearing aid users through big data explainable artificial intelligence techniques

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