Length of Stay Prediction Model of Indoor Patients Based on Light Gradient Boosting Machine

Zeng, Xiangrui

doi:10.1155/2022/9517029

Cited by 6 publications

(5 citation statements)

References 45 publications

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“…(A) LightGBM is a gradient-boosting framework based on a decision tree (DT). It uses a node segmentation strategy based on leaves, seeks the leaf with the largest gain among all the current leaves, and finally generates a boosted tree ( 45 , 46 ). The LightGBM algorithm is based on the selection of partition points based on the histogram algorithm and reduces the number of samples and features required in the training and learning processes through two methods, namely, gradient-based one-side sampling (GOSS) and exclusive feature bundling (EFB), to maintain high learning performance and reduce the resource occupation in terms of time and space in the training process ( 47 , 48 ).…”

Section: Methodsmentioning

confidence: 99%

“…Normalization refers to scaling the data so that it falls within a specific interval. Standardization (Sz) is the transformation of data into a normal distribution with a mean of 0 and a standard deviation of 1 (44). Suppose that there are N samples, each sample has n features, and the value of the i-th feature of all N samples is x i1 , x i2 , ..., x in .…”

Section: Data Processing and Feature Fusionmentioning

confidence: 99%

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OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features

Chen

et al. 2023

Front. Neurol.

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BackgroundEarly stroke prognosis assessments are critical for decision-making regarding therapeutic intervention. We introduced the concepts of data combination, method integration, and algorithm parallelization, aiming to build an integrated deep learning model based on a combination of clinical and radiomics features and analyze its application value in prognosis prediction.MethodsThe research steps in this study include data source and feature extraction, data processing and feature fusion, model building and optimization, model training, and so on. Using data from 441 stroke patients, clinical and radiomics features were extracted, and feature selection was performed. Clinical, radiomics, and combined features were included to construct predictive models. We applied the concept of deep integration to the joint analysis of multiple deep learning methods, used a metaheuristic algorithm to improve the parameter search efficiency, and finally, developed an acute ischemic stroke (AIS) prognosis prediction method, namely, the optimized ensemble of deep learning (OEDL) method.ResultsAmong the clinical features, 17 features passed the correlation check. Among the radiomics features, 19 features were selected. In the comparison of the prediction performance of each method, the OEDL method based on the concept of ensemble optimization had the best classification performance. In the comparison to the predictive performance of each feature, the inclusion of the combined features resulted in better classification performance than that of the clinical and radiomics features. In the comparison to the prediction performance of each balanced method, SMOTEENN, which is based on a hybrid sampling method, achieved the best classification performance than that of the unbalanced, oversampled, and undersampled methods. The OEDL method with combined features and mixed sampling achieved the best classification performance, with 97.89, 95.74, 94.75, 94.03, and 94.35% for Macro-AUC, ACC, Macro-R, Macro-P, and Macro-F1, respectively, and achieved advanced performance in comparison with that of methods in previous studies.ConclusionThe OEDL approach proposed herein could effectively achieve improved stroke prognosis prediction performance, the effect of using combined data modeling was significantly better than that of single clinical or radiomics feature models, and the proposed method had a better intervention guidance value. Our approach is beneficial for optimizing the early clinical intervention process and providing the necessary clinical decision support for personalized treatment.

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Section: Methodsmentioning

confidence: 99%

Section: Data Processing and Feature Fusionmentioning

confidence: 99%

OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features

Chen

et al. 2023

Front. Neurol.

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“…For example, [6] studied the impact of delirium on patients' LOS in the ICU and hospital. A prediction model based on a light gradient boosting machine for indoor patients was developed by [7]. A work by [8] addressed the idea that healthcare services might beneft from new technologies like artifcial intelligence (AI), big data and machine learning, and the Internet of Tings (IoT) to fght COVID-19 (coronavirus) and other pandemics.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Approaches to Predict Patient’s Length of Stay in Emergency Department

Shbool,

S. Arabeyyat,

Al-Bazi

et al. 2023

Applied Computational Intelligence and Soft Computing

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As the COVID-19 pandemic has afflicted the globe, health systems worldwide have also been significantly affected. This pandemic has impacted many sectors, including health in the Kingdom of Jordan. Crises that put heavy pressure on the health systems’ shoulders include the emergency departments (ED), the most demanded hospital resources during normal conditions, and critical during crises. However, managing the health systems efficiently and achieving the best planning and allocation of their EDs’ resources becomes crucial to improve their capabilities to accommodate the crisis’s impact. Knowing critical factors affecting the patient length of stay prediction is critical to reducing the risks of prolonged waiting and clustering inside EDs. That is, by focusing on these factors and analyzing the effect of each. This research aims to determine the critical factors that predict the outcome: the length of stay, i.e., the predictor variables. Therefore, patients’ length of stay in EDs across waiting time duration is categorized as (low, medium, and high) using supervised machine learning (ML) approaches. Unsupervised algorithms have been applied to classify the patient’s length of stay in local EDs in the Kingdom of Jordan. The Arab Medical Centre Hospital is selected as a case study to justify the performance of the proposed ML model. Data that spans a time interval of 22 months, covering the period before and after COVID-19, is used to train the proposed feedforward network. The proposed model is compared with other ML approaches to justify its superiority. Also, comparative and correlation analyses are conducted on the considered attributes (inputs) to help classify the LOS and the patient’s length of stay in the ED. The best algorithms to be used are the trees such as the decision stump, REB tree, and Random Forest and the multilayer perceptron (with batch sizes of 50 and 0.001 learning rate) for this specific problem. Results showed better performance in terms of accuracy and easiness of implementation.

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“…Machine learning algorithms were developed to predict reduced health-related quality of life (HRQoL) with high accuracy in patients with benign or low-grade brain tumors, suggesting they can predict symptoms and global HRQoL decline up to 60 months post-surgery [ 30 ]. The previous study aimed to determine if machine learning (ML) algorithms could predict HRQOL improvements after stroke sensorimotor rehabilitation.…”

Section: Background and Introductionmentioning

confidence: 99%

Light gradient boosting-based prediction of quality of life among oral cancer-treated patients

Ramalingam,

Yadalam,

Ramani

et al. 2024

BMC Oral Health

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Background and introduction Statisticians rank oral and lip cancer sixth in global mortality at 10.2%. Mouth opening and swallowing are challenging. Hence, most oral cancer patients only report later stages. They worry about surviving cancer and receiving therapy. Oral cancer severely affects QOL. QOL is affected by risk factors, disease site, and treatment. Using oral cancer patient questionnaires, we use light gradient Boost Tree classifiers to predict life quality. Methods DIAS records were used for 111 oral cancer patients. The European Organisation for Research and Treatment of Cancer’s QLQ-C30 and QLQ-HN43 were used to document the findings. Anyone could enroll, regardless of gender or age. The IHEC/SDC/PhD/OPATH-1954/19/TH-001 Institutional Ethical Clearance Committee approved this work. After informed consent, patients received the EORTC QLQ-C30 and QLQ-HN43 questionnaires. Surveys were in Tamil and English. Overall, QOL ratings covered several domains. We obtained patient demographics, case history, and therapy information from our DIAS (Dental Information Archival Software). Enrolled patients were monitored for at least a year. After one year, the EORTC questionnaire was retaken, and scores were recorded. This prospective analytical exploratory study at Saveetha Dental College, Chennai, India, examined QOL at diagnosis and at least 12 months after primary therapy in patients with histopathologically diagnosed oral malignancies. We measured oral cancer patients’ quality of life using data preprocessing, feature selection, and model construction. A confusion matrix was created using light gradient boosting to measure accuracy. Results Light gradient boosting predicted cancer patients’ quality of life with 96% accuracy and 0.20 log loss. Conclusion Oral surgeons and oncologists can improve planning and therapy with this prediction model.

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Length of Stay Prediction Model of Indoor Patients Based on Light Gradient Boosting Machine

Cited by 6 publications

References 45 publications

OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features

OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features

Machine Learning Approaches to Predict Patient’s Length of Stay in Emergency Department

Light gradient boosting-based prediction of quality of life among oral cancer-treated patients

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