Objectives This systematic review aimed to assess the performance and clinical feasibility of machine learning (ML) algorithms in prediction of in-hospital mortality for medical patients using vital signs at emergency departments (EDs). Design A systematic review was performed. Setting The databases including Medline (PubMed), Scopus and Embase (Ovid) were searched between 2010 and 2021, to extract published articles in English, describing ML-based models utilising vital sign variables to predict in-hospital mortality for patients admitted at EDs. Critical appraisal and data extraction for systematic reviews of prediction modelling studies checklist was used for study planning and data extraction. The risk of bias for included papers was assessed using the prediction risk of bias assessment tool. Participants Admitted patients to the ED. Main outcome measure In-hospital mortality. Results Fifteen articles were included in the final review. We found that eight models including logistic regression, decision tree, K-nearest neighbours, support vector machine, gradient boosting, random forest, artificial neural networks and deep neural networks have been applied in this domain. Most studies failed to report essential main analysis steps such as data preprocessing and handling missing values. Fourteen included studies had a high risk of bias in the statistical analysis part, which could lead to poor performance in practice. Although the main aim of all studies was developing a predictive model for mortality, nine articles did not provide a time horizon for the prediction. Conclusion This review provided an updated overview of the state-of-the-art and revealed research gaps; based on these, we provide eight recommendations for future studies to make the use of ML more feasible in practice. By following these recommendations, we expect to see more robust ML models applied in the future to help clinicians identify patient deterioration earlier.

Background: Healthcare information systems are hindered by delayed data sharing, privacy breaches, and lack of patient control over data. The growing need for secure, privacy-preserved access control interoperable in health informatics technology (HIT) systems appeals to solutions such as Blockchain (BC), which offers a decentralized, transparent, and immutable ledger architecture. However, its current adoption remains limited to conceptual or proofs-of-concept (PoCs), often relying on simulated datasets rather than validated real-world data or scenarios, necessitating further research into its pragmatic applications and their benchmarking. Objective: This systematic literature review (SLR) aims to analyze BC-based healthcare implementations by benchmarking peer-reviewed studies and turning PoCs or production insights into real-world applications and their evaluation metrics. Unlike prior SLRs focusing on proposed or conceptual models, simulations, or limited-scale deployments, this review focuses on validating practical BC real-world applications in healthcare settings beyond conceptual studies and PoCs. Methods: Adhering to PRISMA-2020 guidelines, we systematically searched five major databases (Scopus, Web of Science, PubMed, IEEE Xplore, and ScienceDirect) for high-precision relevant studies using MeSH terms related to BC in healthcare. The designed review protocol was registered with OSF, ensuring transparency in the review process, including study screening by independent reviewers, eligibility, quality assessment, and data extraction and synthesis. Results: In total, 82 original studies fully met the eligibility criteria and narratively reported BC-based healthcare implementations with validated evaluation outcomes. These studies highlight the current challenges addressed by BC in healthcare settings, providing both qualitative and quantitative data synthesis on its effectiveness. Conclusions: BC-based healthcare implementations show both qualitative and quantitative effectiveness, with advancements in areas such as drug traceability (up to 100%) and fraud prevention (95% reduction). We also discussed the recent challenges of focusing more attention in this area, along with a discussion on the mythological consideration of our own work. Our future research should focus on addressing scalability, privacy-preservation, security, integration, and ethical frameworks for widespread BC adoption for data-driven healthcare.

Background: Smoking is a critical risk factor responsible for over eight million annual deaths worldwide. It is essential to obtain information on smoking habits to advance research and implement preventive measures such as screening of high-risk individuals. In most countries, including Denmark, smoking habits are not systematically recorded and at best documented within unstructured free-text segments of electronic health records (EHRs). This would require researchers and clinicians to manually navigate through extensive amounts of unstructured data, which is one of the main reasons that smoking habits are rarely integrated into larger studies. Our aim is to develop machine learning models to classify patients’ smoking status from their EHRs. Methods: This study proposes an efficient natural language processing (NLP) pipeline capable of classifying patients’ smoking status and providing explanations for the decisions. The proposed NLP pipeline comprises four distinct components, which are; (1) considering preprocessing techniques to address abbreviations, punctuation, and other textual irregularities, (2) four cutting-edge feature extraction techniques, i.e. Embedding, BERT, Word2Vec, and Count Vectorizer, employed to extract the optimal features, (3) utilization of a Stacking-based Ensemble (SE) model and a Convolutional Long Short-Term Memory Neural Network (CNN-LSTM) for the identification of smoking status, and (4) application of a local interpretable model-agnostic explanation to explain the decisions rendered by the detection models. The EHRs of 23,132 patients with suspected lung cancer were collected from the Region of Southern Denmark during the period 1/1/2009-31/12/2018. A medical professional annotated the data into ‘Smoker’ and ‘Non-Smoker’ with further classifications as ‘Active-Smoker’, ‘Former-Smoker’, and ‘Never-Smoker’. Subsequently, the annotated dataset was used for the development of binary and multiclass classification models. An extensive comparison was conducted of the detection performance across various model architectures. Results: The results of experimental validation confirm the consistency among the models. However, for binary classification, BERT method with CNN-LSTM architecture outperformed other models by achieving precision, recall, and F1-scores between 97% and 99% for both Never-Smokers and Active-Smokers. In multiclass classification, the Embedding technique with CNN-LSTM architecture yielded the most favorable results in class-specific evaluations, with equal performance measures of 97% for Never-Smoker and measures in the range of 86 to 89% for Active-Smoker and 91–92% for Never-Smoker. Conclusion: Our proposed NLP pipeline achieved a high level of classification performance. In addition, we presented the explanation of the decision made by the best performing detection model. Future work will expand the model’s capabilities to analyze longer notes and a broader range of categories to maximize its utility in further research and screening applications.

The number of deaths caused by alcohol-related diseases may be reduced by predicting alcohol use disorder (AUD). Many researchers have worked on AUD prediction using machine learning (ML) techniques. However, to the best of our knowledge, there is a lack of a comprehensive systematic literature review (SLR) that summarizes the existing studies on AUD prediction using ML in the last ten years. To address this knowledge gap, this article provides an SLR of academic articles on AUD prediction using ML techniques dated from January 2010 to July 2021. This SLR highlights technical decision analysis related to five aspects: data collection site, characteristics, and type of dataset; data sampling and data pre-processing techniques; feature types and feature engineering techniques; and characteristics of ML techniques and evaluation metrics. Six bibliographic databases were searched, and the identified studies were rigorously reviewed based on the above five aspects. In the selected studies, public datasets were not used very often for AUD prediction. Given that, the current paper identified two different types of data collection sites for review. Imbalanced class distribution in datasets was the primary focus of the pre-processing and sampling steps. Various features, including demographics, family history, drinking behaviour, and electronic health records, were introduced as the more widely used AUD prediction features. The filter, wrapper, and embedded methods were identified as the primary feature selection methods. Support vector machine was the most widely employed algorithm for predicting AUD; however, the lack of deep neural network techniques is evident in this field. Moreover, considering gender disparities, early detection of AUD, and identifying trajectories towards AUD are suggested for future work. For the purpose of evaluating the performance of the prediction approaches, most studies considered the overall accuracy and the area under the receiver operating characteristic curve. Nevertheless, external validation was not performed in any of the selected studies. This paper also discusses challenges and open issues of AUD prediction for future research. This SLR represents a valuable resource for scholars investigating the prediction of AUD.

This study aims to improve the understanding of explainability in deep learning (DL) architectures utilized for heart disease (HD) classification through electrocardiogram (ECG) data. It offers a systematic review of methodological choices, analyses their impacts on model interpretability, and highlights significant challenges in this domain while suggesting opportunities for future research. A systematic literature review (SLR) was conducted based on Kitchenham and Charters guidelines. To address the proposed research questions, this article provides an SLR of academic articles on Explainable AI (XAI)-based DL for the classification of HDs using ECGs dated from January 2018 to September 2024. Results were synthesized by identifying the valuable insights into the datasets, preprocessing methods, and techniques employed in the development of XAI-based DL models for the classification of ECG-based HDs. This study identified 6448 primary studies that utilized machine learning and DL techniques for the classification of HD based on ECG data, of which 51 used an XAI-based DL architecture. Our finding indicates that there are 25 different datasets that were used, 16 different DL architectures were introduced, and eight novel XAI techniques were introduced, while most of the selected studies used the conventional XAI approaches such as SHAP, Saliency Maps, Grad-Cam, and LIME. A considerable amount of XAI-based DL architectures were identified for the classification of ECG-based HD. The techniques frequently selected by these studies have potential limitations such as data standardization, inconsistent explainability, temporal dependency visualization, lack of XAI benchmarking, lack of standardized metrics, and many more, which are presented with proposed future research directions.

Walking ability of elderly individuals, who suffer from walking difficulties, is limited, which restricts their mobility independence. The physical health and well-being of the elderly population are affected by their level of physical activity. Therefore, monitoring daily activities can help improve the quality of life. This becomes especially a huge challenge for those, who suffer from dementia and Alzheimer’s disease. Thus, it is of great importance for personnel in care homes/rehabilitation centers to monitor their daily activities and progress. Unlike normal subjects, it is required to place the sensor on the back of this group of patients, which makes it even more challenging to detect walking from other activities. With the latest advancements in the field of health sensing and sensor technology, a huge amount of accelerometer data can be easily collected. In this study, a Machine Learning (ML) based algorithm was developed to analyze the accelerometer data collected from patients with walking difficulties, who live in one of the municipalities in Denmark. The ML algorithm is capable of accurately classifying the walking activity of these individuals with different walking abnormalities. Various statistical, temporal, and spectral features were extracted from the time series data collected using an accelerometer sensor placed on the back of the participants. The back sensor placement is desirable in patients with dementia and Alzheimer’s disease since they may remove visible sensors to them due to the nature of their diseases. Then, an evolutionary optimization algorithm called Particle Swarm Optimization (PSO) was used to select a subset of features to be used in the classification step. Four different ML classifiers such as k-Nearest Neighbors (kNN), Random Forest (RF), Stacking Classifier (Stack), and Extreme Gradient Boosting (XGB) were trained and compared on an accelerometry dataset consisting of 20 participants. These models were evaluated using the leave-one-group-out cross-validation (LOGO-CV) technique. The Stack model achieved the best performance with average sensitivity, positive predictive values (precision), F₁-score, and accuracy of 86.85%, 93.25%, 88.81%, and 93.32%, respectively, to classify walking episodes. In general, the empirical results confirmed that the proposed models are capable of classifying the walking episodes despite the challenging sensor placement on the back of the patients, who suffer from walking disabilities.