Multilevel Self-Attention Model and its Use on Medical Risk Prediction

Zeng, Xianlong; Feng, Yunyi; Moosavinasab, Soheil; Lin, Deborah; Lin, Simon; Chang, Liu

doi:10.1142/9789811215636_0011

Cited by 8 publications

(7 citation statements)

References 10 publications

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“…Deep learning models are widely used for mining electronic medical records (EHR), including patient phenotyping [16]- [18], representation learning [19] and disease progression [20]- [23]. Specifically, recurrent neural network-based models are extremely popular for their ability to model the sequential medical data.…”

Section: B Deep Learning In Healthcarementioning

confidence: 99%

Multi-View Deep Learning Framework for Predicting Patient Expenditure in Healthcare

Zeng

Lin

Liu

2021

IEEE Open J. Comput. Soc.

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Accurately predicting patient expenditure in healthcare is an important task with many applications such as provider profiling, accountable care management, and capitated medical payment adjustment. Existing approaches mainly rely on manually designed features and linear regression-based models, which require massive medical domain knowledge and show limited predictive performance. This paper proposes a multi-view deep learning framework to predict future healthcare expenditure at the individual level based on historical claims data. Our multi-view approach can effectively model the heterogeneous information, including patient demographic features, medical codes, drug usages, and facility utilization. We conducted expenditure forecasting tasks on a real-world pediatric dataset that contains more than 450,000 patients. The empirical results show that our proposed method outperforms all baselines for predicting medical expenditure. These findings help toward better preventive care and accountable care in the healthcare domain.INDEX TERMS Administrative claims data, deep learning, electronic health record, expenditure prediction, machine learning.

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Section: B Deep Learning In Healthcarementioning

confidence: 99%

Multi-View Deep Learning Framework for Predicting Patient Expenditure in Healthcare

Zeng

Lin

Liu

2021

IEEE Open J. Comput. Soc.

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“…In recent years, researchers have proposed various deep learning models to garner knowledge from massive EHR and shown their superior ability in medical event predictions [11], [12], [13], [24], [25], [26], [27], [28], [29], [30], [31], [32], currently, a major research topic in healthcare informatics. Previous studies recommend using recurrent neural networks (RNNs) for patient subtyping [27], [33], modelling disease progression [34], and time-series healthcare-data analysis [35].…”

Section: Deep Learning For Ehr Datamentioning

confidence: 99%

MIPO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning

Peng¹,

Long²,

Wang³

et al. 2021

Preprint

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Healthcare representation learning on the Electronic Health Record (EHR) is seen as crucial for predictive analytics in the medical field. Many natural language processing techniques, such as word2vec, RNN and self-attention, have been adapted for use in hierarchical and time stamped EHR data, but fail when they lack either general or task-specific data. Hence, some recent works train healthcare representations by incorporating medical ontology (a.k.a. knowledge graph), by self-supervised tasks like diagnosis prediction, but (1) the small-scale, monotonous ontology is insufficient for robust learning, and (2) critical contexts or dependencies underlying patient journeys are never exploited to enhance ontology learning. To address this, we propose an end-to-end robust Transformer-based solution, Mutual Integration of patient journey and Medical Ontology (MIMO) for healthcare representation learning and predictive analytics. Specifically, it consists of task-specific representation learning and graph-embedding modules to learn both patient journey and medical ontology interactively. Consequently, this creates a mutual integration to benefit both healthcare representation learning and medical ontology embedding. Moreover, such integration is achieved by a joint training of both task-specific predictive and ontology-based disease typing tasks based on fused embeddings of the two modules. Experiments conducted on two real-world diagnosis prediction datasets show that, our healthcare representation model MIMO not only achieves better predictive results than previous state-of-the-art approaches regardless of sufficient or insufficient training data, but also derives more interpretable embeddings of diagnoses.

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“…Johnson et al [10] took a similar approach in their research, using the MIMIC dataset to predict mortality. Zeng et al [28] recently published a study establishing the connection between a hospital visit and medical codes to develop a predictive model for the upcoming diseases. Ghassemi et al [8] introduced a new predictive model in which auto-regression models are employed for unsupervised training.…”

Section: Introductionmentioning

confidence: 99%

Critical Correlation of Predictors for an Efficient Risk Prediction Framework of ICU Patient Using Correlation and Transformation of MIMIC-III Dataset

Khope

Elias

2022

Data Sci. Eng.

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Various predictive frameworks have evolved over the last decade to facilitate the efficient diagnosis of critical diseases in the healthcare sector. Some have been commercialized, while others are still in the research and development stage. An effective early predictive principle must provide more accurate outcomes in complex clinical data and various challenging environments. The open-source database system medical information mart for intensive care (MIMIC) simplifies all of the attributes required in predictive analysis in this regard. This database contains clinical and non-clinical information on a patient’s stay at a healthcare facility, gathered during their duration of stay. Regardless of the number of focused research attempts employing the MIMIC III database, a simplified and cost-effective computational technique for developing the early analysis of critical problems has not yet been found. As a result, the proposed study provides a novel and cost-effective machine learning framework that evolves into a novel feature engineering methodology using the MIMIC III dataset. The core idea is to forecast the risk associated with a patient’s clinical outcome. The proposed study focused on the diagnosis and clinical procedures and found distinct variants of independent predictors from the MIMIC III database and ICD-9 code. The proposed logic is scripted in Python, and the outcomes of three common machine learning schemes, namely Artificial Neural Networks, K-Nearest Neighbors, and Logistic Regression, have been evaluated. Artificial Neural Networks outperform alternative machine learning techniques when accuracy is taken into account as the primary performance parameter over the MIMIC III dataset.

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Multilevel Self-Attention Model and its Use on Medical Risk Prediction

Cited by 8 publications

References 10 publications

Multi-View Deep Learning Framework for Predicting Patient Expenditure in Healthcare

Multi-View Deep Learning Framework for Predicting Patient Expenditure in Healthcare

MIPO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning

Critical Correlation of Predictors for an Efficient Risk Prediction Framework of ICU Patient Using Correlation and Transformation of MIMIC-III Dataset

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