MS-LSTMEA: Predicting Clinical Events for Hypertension Using Multi-Sources LSTM Explainable Approach

Zehraoui, Farida; Sendi, Naziha; Abchiche-Mimouni, Nadia

doi:10.2139/ssrn.4123459

Cited by 1 publication

(1 citation statement)

References 28 publications

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“…The application of deep learning models to predict clinical outcomes using electronic medical records (EMR) data has gained significant attention recently [62,63]. EMR data, which typically represents a patient's history as a sequence of visits with multiple events per visit, is well-suited for such sequence models as RNNs [64,65]. Recent studies indicated that simple-gated RNN models, such as Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs), when finely tuned using Bayesian Optimization, often deliver competitive outcomes [33].…”

Section: Methodsmentioning

confidence: 99%

Unveiling the Enigma: Exploring Risk Factors and Mechanisms for Psychotic Symptoms in Alzheimer’s Disease through Electronic Medical Records with Deep Learning Models

Fan

Miranda

et al. 2023

Pharmaceuticals

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Around 50% of patients with Alzheimer’s disease (AD) may experience psychotic symptoms after onset, resulting in a subtype of AD known as psychosis in AD (AD + P). This subtype is characterized by more rapid cognitive decline compared to AD patients without psychosis. Therefore, there is a great need to identify risk factors for the development of AD + P and explore potential treatment options. In this study, we enhanced our deep learning model, DeepBiomarker, to predict the onset of psychosis in AD utilizing data from electronic medical records (EMRs). The model demonstrated superior predictive capacity with an AUC (area under curve) of 0.907, significantly surpassing conventional risk prediction models. Utilizing a perturbation-based method, we identified key features from multiple medications, comorbidities, and abnormal laboratory tests, which notably influenced the prediction outcomes. Our findings demonstrated substantial agreement with existing studies, underscoring the vital role of metabolic syndrome, inflammation, and liver function pathways in AD + P. Importantly, the DeepBiomarker model not only offers a precise prediction of AD + P onset but also provides mechanistic understanding, potentially informing the development of innovative treatments. With additional validation, this approach could significantly contribute to early detection and prevention strategies for AD + P, thereby improving patient outcomes and quality of life.

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