A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges

An, Angela; Rahman, Saifur; Zhou, Jingwen; Kang, James Jin

doi:10.3390/s23094178

Cited by 91 publications

(28 citation statements)

References 81 publications

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“…With the advent of natural language processing (NLP) and machine learning (ML) models in healthcare since the late 1990s [13], delirium risk can now be predicted with 94.1% accuracy in geriatric internal medicines inpatients [14]. Building accurate predictive models based on logistic regression algorithm is widely used in the medical field [15] with Generative AI now offering an opportunity to augment and accompany the output compared against these logistic regression models [16,17]. As a result, several logistic regressions, machine learning-based delirium prediction models have been developed since 2018 [18][19][20][21][22][23][24].…”

Section: Evolution Of Llmsmentioning

confidence: 99%

Novel opportunities for clinical pharmacy research: development of a machine learning model to identify medication related causes of delirium in different patient groups

Weidmann,

Watson

2024

Int J Clin Pharm

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The advent of artificial intelligence (AI) technologies has taken the world of science by storm in 2023. The opportunities of this easy to access technology for clinical pharmacy research are yet to be fully understood. The development of a custom-made large language model (LLM) (DELSTAR) trained on a wide range of internationally recognised scientific publication databases, pharmacovigilance sites and international product characteristics to help identify and summarise medication related information on delirium, as a proof-of-concept model, identified new facilitators and barriers for robust clinical pharmacy practice research. This technology holds great promise for the development of much more comprehensive prescribing guidelines, practice support applications for clinical pharmacy, increased patient and prescribing safety and resultant implications for healthcare costs. The challenge will be to ensure its methodologically robust use and the detailed and transparent verification of its information accuracy.

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Section: Evolution Of Llmsmentioning

confidence: 99%

Novel opportunities for clinical pharmacy research: development of a machine learning model to identify medication related causes of delirium in different patient groups

Weidmann,

Watson

2024

Int J Clin Pharm

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“…In the last decade, machine learning (ML) algorithms have been widely used in clinical prediction models to aid in early disease prediction, severity grading, classification, and prognostication of patients. Compared with conventional logistic regression, ML can effectively handle complex linear and nonlinear relationships among variables in large datasets, resulting in superior predictive performance (14)(15)(16)(17). While several studies have investigated the risk factors and prediction models for IFI (18)(19)(20)(21)(22)(23)(24)(25)(26), existing models often suffer from limitations such as small sample sizes, focusing on a single fungal infection, or utilizing features that are difficult to obtain in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

Interpretable machine learning for predicting risk of invasive fungal infection in critically ill patients in the intensive care unit: A retrospective cohort study based on MIMIC-IV database

Cao,

Li,

Wang

et al. 2024

Shock

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The delayed diagnosis of invasive fungal infection (IFI) is highly correlated with poor prognosis in patients. Early identification of high-risk patients with invasive fungal infections and timely implementation of targeted measures is beneficial for patients. The objective of this study was to develop a machine learning-based predictive model for invasive fungal infection in patients during their Intensive Care Unit (ICU) stay. Retrospective data was extracted from adult patients in the MIMIC-IV database who spent a minimum of 48 hours in the ICU. Feature selection was performed using LASSO regression, and the dataset was balanced using the BL-SMOTE approach. Predictive models were built using six machine learning algorithms. The Shapley additive explanation (SHAP) algorithm was employed to assess the impact of various clinical features in the optimal model, enhancing interpretability. The study included 26,346 ICU patients, of whom 379 (1.44%) were diagnosed with invasive fungal infection. The predictive model was developed using 20 risk factors, and the dataset was balanced using the borderline-SMOTE (BL-SMOTE) algorithm. The BL-SMOTE random forest model demonstrated the highest predictive performance (AUC 0.88, 95% CI: 0.84-0.91). SHAP analysis revealed that the three most influential clinical features in the BL-SMOTE random forest model were dialysis treatment, APSIII scores, and liver disease. The machine learning model provides a reliable tool for predicting the occurrence of IFI in ICU patients. The BL-SMOTE random forest model, based on 20 risk factors, exhibited superior predictive performance and can assist clinicians in early assessment of IFI occurrence in ICU patients. Importance Invasive fungal infections are characterized by high incidence and high mortality rates characteristics. In this study, we developed a clinical prediction model for invasive fungal infections in critically ill patients based on machine learning algorithms. The results show that the machine learning model based on 20 clinical features has good predictive value.

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“…Although traditional machine learning techniques have gradually become the core means of mining the deep value of medical big data, and have made breakthroughs in the accurate diagnosis of diseases 1 , prospective prediction of patient treatment response, and formulation of individualised treatment strategies 2 . However, conventional machine learning methods have revealed a series of inherent limitations when applied to complex medical data like hepatitis C. An et al 3 found that conventional machine learning techniques are difficult to effectively mine the non-linear, high-dimensional pathophysiological patterns hidden in highly complex medical data containing multiple clinical indicators and biomarker information. Meanwhile, Rahman et al 4 suggested the prevalent category imbalance problem in medical datasets leads traditional machine learning models to be ineffective in dealing with rare and early-stage conditions, and to face significant challenges in terms of robustness and generalisation when coping with situations such as high noise, large amounts of missing data and outliers.…”

Section: Introductionmentioning

confidence: 99%

Investigation of the effectiveness of a classification method based on improved DAE feature extraction for hepatitis C prediction

Zhang,

Wang,

Chang

et al. 2024

Sci Rep

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Hepatitis C, a particularly dangerous form of viral hepatitis caused by hepatitis C virus (HCV) infection, is a major socio-economic and public health problem. Due to the rapid development of deep learning, it has become a common practice to apply deep learning to the healthcare industry to improve the effectiveness and accuracy of disease identification. In order to improve the effectiveness and accuracy of hepatitis C detection, this study proposes an improved denoising autoencoder (IDAE) and applies it to hepatitis C disease detection. Conventional denoising autoencoder introduces random noise at the input layer of the encoder. However, due to the presence of these features, encoders that directly add random noise may mask certain intrinsic properties of the data, making it challenging to learn deeper features. In this study, the problem of data information loss in traditional denoising autoencoding is addressed by incorporating the concept of residual neural networks into an enhanced denoising autoencoder. In our experimental study, we applied this enhanced denoising autoencoder to the open-source Hepatitis C dataset and the results showed significant results in feature extraction. While existing baseline machine learning methods have less than 90% accuracy and integrated algorithms and traditional autoencoders have only 95% correctness, the improved IDAE achieves 99% accuracy in the downstream hepatitis C classification task, which is a 9% improvement over a single algorithm, and a nearly 4% improvement over integrated algorithms and other autoencoders. The above results demonstrate that IDAE can effectively capture key disease features and improve the accuracy of disease prediction in hepatitis C data. This indicates that IDAE has the potential to be widely used in the detection and management of hepatitis C and similar diseases, especially in the development of early warning systems, progression prediction and personalised treatment strategies.

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A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges

Cited by 91 publications

References 81 publications

Novel opportunities for clinical pharmacy research: development of a machine learning model to identify medication related causes of delirium in different patient groups

Novel opportunities for clinical pharmacy research: development of a machine learning model to identify medication related causes of delirium in different patient groups

Interpretable machine learning for predicting risk of invasive fungal infection in critically ill patients in the intensive care unit: A retrospective cohort study based on MIMIC-IV database

Investigation of the effectiveness of a classification method based on improved DAE feature extraction for hepatitis C prediction

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