Multitask Deep Learning for Cost-Effective Prediction of Patient's Length of Stay and Readmission State Using Multimodal Physical Activity Sensory Data

Ali, Sajid; El–Sappagh, Shaker; Ali, Farman; Imran, Muhammad; Abuhmed, Tamer

doi:10.1109/jbhi.2022.3202178

Cited by 19 publications

(6 citation statements)

References 52 publications

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“…When it comes to intelligent healthcare systems as well as patient care, artificial intelligence can help experts or doctors [7][8][9][10][11]. Machine learning approaches are becoming an emerging tool for the diagnosis of disease [12][13][14][15]. There are many methods used in the literature for the diagnosis of liver fibrosis disease.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic-Based System for Liver Fibrosis Disease

Alkhalifah¹,

Singla²,

Alurise³

2023

CSSE

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The diagnosis of liver fibrosis (LF) is crucial as it is a deadly and life-threatening disease. Artificial intelligence techniques aid doctors by using the previous data on health and making a diagnostic system, which helps to take decisions about patients' health as experts can. The historical data of a patient's health can have vagueness, inaccurate, and can also have missing values. The fuzzy logic theory can deal with these issues in the dataset. In this paper, a multilayer fuzzy expert system is developed to diagnose LF. The model is created by using multiple layers of the fuzzy logic approach. This system aids in classifying the health of patients into different classes. The proposed method has two layers, i.e., layer 1 and layer 2. The input variables used in layer 1 for diagnosing liver fibrosis are Appetite, Jaundice, Ascites, Age, and Fatigue. Similarly, in layer 2, the input variables are Platelet count, White blood cell count, spleen, SGPT ALT (Serum Glutamic Pyruvic Transaminase Alanine Aminotransferase), SGOT ALT (Serum Glutamicoxalacetic Transaminase Alanine Aminotransferase), Serum bilirubin, and Serum albumin. The output variables for this developed system are no damage, minimal damage, significant damage, severe damage, and cirrhosis. This research work also presents the examination of results based on performance parameters. The proposed system achieves a classification accuracy of 95%. Moreover, other performance parameters such as sensitivity, specificity, and precision are calculated as 97.14%, 92%, and 94.44%, respectively.

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Section: Introductionmentioning

confidence: 99%

Fuzzy Logic-Based System for Liver Fibrosis Disease

Alkhalifah¹,

Singla²,

Alurise³

2023

CSSE

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“…The field of artificial intelligence is developing rapidly. Data from multisensory data and imaging CT and MRI data are already being used extensively in medical decisionmaking [56][57][58]. In the field of diabetic foot ulcers, we look forward to incorporating such data in our follow-up work to make more accurate monitoring and timely treatment of patients.…”

Section: Plos Onementioning

confidence: 99%

Machine learning for the prediction of minor amputation in University of Texas grade 3 diabetic foot ulcers

Wang

Zhu

et al. 2022

PLoS ONE

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Minor amputations are performed in a large proportion of patients with diabetic foot ulcers (DFU) and early identification of the outcome of minor amputations facilitates medical decision-making and ultimately reduces major amputations and deaths. However, there are currently no clinical predictive tools for minor amputations in patients with DFU. We aim to establish a predictive model based on machine learning to quickly identify patients requiring minor amputation among newly admitted patients with DFU. Overall, 362 cases with University of Texas grade (UT) 3 DFU were screened from tertiary care hospitals in East China. We utilized the synthetic minority oversampling strategy to compensate for the disparity in the initial dataset. A univariable analysis revealed nine variables to be included in the model: random blood glucose, years with diabetes, cardiovascular diseases, peripheral arterial diseases, DFU history, smoking history, albumin, creatinine, and C-reactive protein. Then, risk prediction models based on five machine learning algorithms: decision tree, random forest, logistic regression, support vector machine, and extreme gradient boosting (XGBoost) were independently developed with these variables. After evaluation, XGBoost earned the highest score (accuracy 0.814, precision 0.846, recall 0.767, F1-score 0.805, and AUC 0.881). For convenience, a web-based calculator based on our data and the XGBoost algorithm was established (https://dfuprediction.azurewebsites.net/). These findings imply that XGBoost can be used to develop a reliable prediction model for minor amputations in patients with UT3 DFU, and that our online calculator will make it easier for clinicians to assess the risk of minor amputations and make proactive decisions.

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“…This holistic approach improves ML and DL models' accuracy, robustness, and generalization capabilities [32]. Data fusion significantly impacts various fields, including healthcare [33], [34], and environmental monitoring [33]. In healthcare, for example, combining clinical data, medical images, genetic information, and patient records allows for more accurate disease diagnosis, personalized treatment recommendations, and monitoring of patient outcomes.…”

Section: Introductionmentioning

confidence: 99%

Explainable Artificial Intelligence of Multi-Level Stacking Ensemble for Detection of Alzheimer’s Disease Based on Particle Swarm Optimization and the Sub-Scores of Cognitive Biomarkers

AlMohimeed,

Saad,

Mostafa

et al. 2023

IEEE Access

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Alzheimer's disease (AD) is a progressive neurological disorder characterized by memory loss and cognitive decline, affecting millions worldwide. Early detection is crucial for effective treatment, as it can slow disease progression and improve quality of life. Machine learning has shown promise in AD detection using various medical modalities. In this paper, we propose a novel multi-level stacking model that combines heterogeneous models and modalities to predict different classes of AD. The modalities include cognitive sub-scores (e.g., clinical dementia rating -sum of boxes, Alzheimer's disease assessment scale) from the Alzheimer's Disease Neuroimaging Initiative dataset. In the proposed approach, in level 1, we used six base models (Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), K-nearest Neighbors (KNN), and Native Bayes (NB)to train each modality (ADAS, CDR, and FQA). Then, we build stacking training that combines the outputs of each base model for the training set and staking testing that combines the outcomes of each model for the testing set. In level 2, three stacking models are produced for each modality that trains and evaluates based on the output of 6 base models based on (RF, LR, DT, SVM, KNN, and NB) are combined in training stacking for the training set and testing stacking for the testing set. Stacking training is used to train meta-learners (RF), and stacking testing is used to evaluate meta-learners (RF). Finally, in level 3, the output prediction of the stacking model from each modality (ADAS, CDR, and FQA) in the training and testing datasets is merged to build a new dataset, which is staking training and stacking testing. Training stacking is used to train the meta-learner, and the testing set is used to evaluate the meta-learner and produce the final prediction. Our research also aims to provide model explanations, ensuring efficiency, effectiveness, and trust through explainable artificial intelligence (XAI). Feature selection optimization based on Particle Swarm Optimization is used to select the most appropriate sub-scores. The proposed model shows significant potential for improving early disease diagnosis. The results demonstrate that the multi-modality approach outperforms single-modality approaches. Moreover, the proposed multi-level stacking models achieve the highest performance with selected features compared to regular ML classifiers and stacking models using full multi-modalities, achieving accuracy, precision, recall, and F1-scores of 92.08%, 92.07%, 92.08%, and 92.01% for two classes, and 90.03%, 90.19%, 90.03%, and 90.05% for three classes, respectively.

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Multitask Deep Learning for Cost-Effective Prediction of Patient's Length of Stay and Readmission State Using Multimodal Physical Activity Sensory Data

Cited by 19 publications

References 52 publications

Fuzzy Logic-Based System for Liver Fibrosis Disease

Fuzzy Logic-Based System for Liver Fibrosis Disease

Machine learning for the prediction of minor amputation in University of Texas grade 3 diabetic foot ulcers

Explainable Artificial Intelligence of Multi-Level Stacking Ensemble for Detection of Alzheimer’s Disease Based on Particle Swarm Optimization and the Sub-Scores of Cognitive Biomarkers

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