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In the era of digitalization, the use of data analytics in healthcare has become a major facilitator of improved patient care, operational efficiency, and evidence-based clinical decision-making. The aim of this review article is to provide an overview of healthcare data analytics, particularly for healthcare practitioners, clinicians, and medical administrators across various specialties, who may not have a formal background in data science who plan to collaborate with digital technologies more precisely, big data analytics and data science. It provides an introduction to the theoretical and practical concepts in healthcare data analytics, outlining its benefits, associated challenges, adoption strategies, and practical applications of data analytics and artificial intelligence (AI) in medicine. The review highlights how advanced analytical techniques such as predictive modelling, machine learning, and AI are revolutionising healthcare delivery by enabling pre-emptive interventions and personalized treatment plans. Lastly, the article identifies promising areas of research in medical data analytics, and it outlines how emerging technologies can be used to enhance treatment outcomes, evidence-based decision-making, and the delivery of quality healthcare services.

The new Coronavirus disease 2019 (COVID-19) is rapidly affecting the world population with statistics quickly falling out of date. Due to the limited availability of annotated Coronavirus X-ray and CT images, the detection of COVID-19 remains the biggest challenge in diagnosing this disease. This paper provides a promising solution by proposing a COVID-19 detection system based on deep learning. The proposed deep learning modalities are based on convolutional neural network (CNN) and convolutional long short-term memory (ConvLSTM). Two different datasets are adopted for the simulation of the proposed modalities. The first dataset includes a set of CT images, while the second dataset includes a set of X-ray images. Both of these datasets consist of two categories: COVID-19 and normal. In addition, COVID-19 and pneumonia image categories are classified in order to validate the proposed modalities. The proposed deep learning modalities are tested on both X-ray and CT images as well as a combined dataset that includes both types of images. They achieved an accuracy of 100% and an F1 score of 100% in some cases. The simulation results reveal that the proposed deep learning modalities can be considered and adopted for quick COVID-19 screening.

Feature selection (FS) is considered to be a hard optimization problem in data mining and some artificial intelligence fields. It is a process where rather than studying all of the features of a whole dataset, some associated features of a problem are selected, the aim of which is to increase classification accuracy and reduce computational time. In this paper, a recent optimization algorithm, the monarch butterfly optimization (MBO) algorithm, is implemented with a wrapper FS method that uses the k-nearest neighbor (KNN) classifier. Experiments were implemented on 18 benchmark datasets. The results showed that, in comparison with four metaheuristic algorithms (WOASAT, ALO, GA and PSO), MBO was superior, giving a high rate of classification accuracy of, on average, 93% for all datasets as well as reducing the selection size significantly. Therefore, the use of the MBO to solve the FS problems has been proven through the results obtained to be effective and highly efficient in this field, and the results have also proven the strength of the balance between global and local search of MBO.

With greater impetus on broad postmarket surveillance, the Voice of the Customer (VoC) has emerged as an important source of information for understanding consumer experiences and identifying potential issues. In organizations, risk management groups are increasingly interested in working with their information technology teams to develop robust VoC listening platforms. Two key challenges have impeded success. First, prior work has leveraged diverse sets of channels, adverse event types, and modeling methods, resulting in diverging conclusions regarding the viability and efficacy of various user-generated channels and accompanying modeling methods. Second, many existing detection methods rely on “mention models” that have low detection rates, have high false positives, and lack timeliness. Following the information systems design science approach, in this research note we propose a framework for examining key design elements for VoC listening platforms. As part of our framework, we also develop a novel heuristic-based method for detecting adverse events. We evaluate our framework and method on two large test beds, each encompassing millions of tweets, forums postings, and search query logs pertaining to hundreds of adverse events related to the pharmaceutical and automotive industries. With greater impetus on broad postmarket surveillance, the Voice of the Customer (VoC) has emerged as an important source of information for understanding consumer experiences and identifying potential issues. In organizations, risk management groups are increasingly interested in working with their information technology teams to develop robust VoC listening platforms. Two key challenges have impeded success. First, prior work has leveraged diverse sets of channels, adverse event types, and modeling methods, resulting in diverging conclusions regarding the viability and efficacy of various user-generated channels and accompanying modeling methods. Second, many existing detection methods rely on “mention models” that have low detection rates, have high false positives, and lack timeliness. Following the information systems design science approach, in this research note we propose a framework for examining key design elements for VoC listening platforms. As part of our framework, we also develop a novel heuristic-based method for detecting adverse events. We evaluate our framework and method on two large test beds each encompassing millions of tweets, forums postings, and search query logs pertaining to hundreds of adverse events related to the pharmaceutical and automotive industries. The results shed light on the interplay between user-generated channels and event types, as well as the potential for more robust event modeling methods that go beyond basic mention models. Our analysis framework reveals that user-generated content channels can facilitate timelier detection of adverse events: on average, two to three years or earlier than commonly used databases. The inclusion of negative sentiment polarity in the models can further reduce false-positive rates. Additionally, we find social media channels provide higher detection rates but lower precision than do search-based signals. The search and web forum channels are timelier than Twitter. The proposed heuristic-based method attains markedly better results than do existing methods—with earlier detection rates of 50%–80% and far fewer false positives across an array of VoC channels and event types. The heuristic method is also well suited for signal fusion across channels. Our note makes several contributions to research. The results also have important implications for various practitioner groups, including regulatory agencies and risk management teams at product manufacturing firms.

Breast cancer is the most prevailing cancer in the world and each year affecting millions of women. It is also the cause of largest number of deaths in women dying in cancers. During the last few years, researchers are proposing different convolutional neural network models in order to facilitate diagnostic process of breast cancer. Convolutional neural networks are showing promising results to classify cancers using image datasets. There is still a lack of standard models which can claim the best model because of unavailability of large datasets that can be used for models’ training and validation. Hence, researchers are now focusing on leveraging the transfer learning approach using pre-trained models as feature extractors that are trained over millions of different images. With this motivation, this paper considers eight different fine-tuned pre-trained models to observe how these models classify breast cancers applying on ultrasound images. We also propose a shallow custom convolutional neural network that outperforms the pre-trained models with respect to different performance metrics. The proposed model shows 100% accuracy and achieves 1.0 AUC score, whereas the best pre-trained model shows 92% accuracy and 0.972 AUC score. In order to avoid biasness, the model is trained using the fivefold cross validation technique. Moreover, the model is faster in training than the pre-trained models and requires a small number of trainable parameters. The Grad-CAM heat map visualization technique also shows how perfectly the proposed model extracts important features to classify breast cancers.

The goal of this study is to present a DEA-based fuzzy multi-criteria decision making model for firms in the health care industry in order to enhance their business performance. The study demonstrates a real-life use of the proposed model, mainly designed for hospitals. Data envelopment analysis enhanced with fuzzy analytic hierarchy process are collectively utilized to quantify the data and structure the model in decision-making. The juxtaposition of the two methods is used to compile a ranked list of multiple proxies containing diverse input and output variables which occur in two stages. This hybrid model provides several benefits, one of which is the ability to make the most appropriate decision considering the value of the weights determined by the data from the hybrid model.

Mitigating preventable readmissions, where patients are readmitted for the same primary diagnosis within 30 days, poses a significant challenge to the delivery of high-quality healthcare. Toward this end, we develop a novel, predictive analytics model, termed as the beta geometric Erlang-2 (BG/EG) hurdle model, which predicts the propensity, frequency, and timing of readmissions of patients diagnosed with congestive heart failure (CHF). This unified model enables us to answer three key questions related to the use of predictive analytics methods for patient readmissions: whether a readmission will occur, how often readmissions will occur, and when a readmission will occur. We test our model using a unique data set that tracks patient demographic, clinical, and administrative data across 67 hospitals in North Texas over a four-year period. We show that our model provides superior predictive performance compared to extant models such as the logit, BG/NBD hurdle, and EG hurdle models. Our model also allows us to study the association between hospital usage of health information technologies (IT) and readmission risk. We find that health IT usage, patient demographics, visit characteristics, payer type, and hospital characteristics, are significantly associated with patient readmission risk. We also observe that implementation of cardiology information systems is associated with a reduction in the propensity and frequency of future readmissions, whereas administrative IT systems are correlated with a lower frequency of future readmissions. Our results indicate that patient profiles derived from our model can serve as building blocks for a predictive analytics system to identify CHF patients with high readmission risk.

Approximately one billion individuals suffer from mental health disorders, such as depression, bipolar disorder, schizophrenia, and anxiety. Mental health professionals use various assessment tools to detect and diagnose these disorders. However, these tools are complex, contain an excessive number of questions, and require a significant amount of time to administer, leading to low participation and completion rates. Additionally, the results obtained from these tools must be analyzed and interpreted manually by mental health professionals, which may yield inaccurate diagnoses. To this extent, this research utilizes advanced analytics and artificial intelligence to develop a decision support system (DSS) that can efficiently detect and diagnose various mental disorders. As part of the DSS development process, the Network Pattern Recognition (NEPAR) algorithm is first utilized to build the assessment tool and identify the questions that participants need to answer. Then, various machine learning models are trained using participants’ answers to these questions and other historical data as inputs to predict the existence and the type of their mental disorder. The results show that the proposed DSS can automatically diagnose mental disorders using only 28 questions without any human input, to an accuracy level of 89%. Furthermore, the proposed mental disorder diagnostic tool has significantly fewer questions than its counterparts; hence, it provides higher participation and completion rates. Therefore, mental health professionals can use this proposed DSS and its accompanying assessment tool for improved clinical decision-making and diagnostic accuracy.

Electroencephalographic (EEG) recordings can be of great help in decoding the open/close hand’s motion preparation. To this end, cortical EEG source signals in the motor cortex (evaluated in the 1-s window preceding movement onset) are extracted by solving inverse problem through beamforming. EEG sources epochs are used as source-time maps input to a custom deep convolutional neural network (CNN) that is trained to perform 2-ways classification tasks: pre-hand close (HC) versus resting state (RE) and pre-hand open (HO) versus RE. The developed deep CNN works well (accuracy rates up to 89.65 ± 5.29 % for HC versus RE and 90.50 ± 5.35 % for HO versus RE), but the core of the present study was to explore the interpretability of the deep CNN to provide further insights into the activation mechanism of cortical sources during the preparation of hands’ sub-movements. Specifically, occlusion sensitivity analysis was carried out to investigate which cortical areas are more relevant in the classification procedure. Experimental results show a recurrent trend of spatial cortical activation across subjects. In particular, the central region (close to the longitudinal fissure) and the right temporal zone of the premotor together with the primary motor cortex appear to be primarily involved. Such findings encourage an in-depth study of cortical areas that seem to play a key role in hand’s open/close preparation.

Advanced data analytics are increasingly being employed in healthcare research to improve patient classification and personalize medicinal therapies. In this paper, we focus on the critical problem of clustering electronic health record (EHR) data to enable appropriate patient categorization. In the era of personalized medicine, optimizing patient classification is critical to healthcare analytics. This research presents a comparative assessment of different clustering algorithms for Electronic Health Record (EHR) data, with the goal of improving the efficacy and productivity of patient clustering methods. Our study focuses on Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) as a Multi-Criteria Decision-Making (MCDM) strategy, includes an in-depth assessment of eight clustering algorithms: K-Means, DBSCAN, Hierarchical Clustering, Mean Shift, Affinity Propagation, Spectral Clustering, Gaussian Mixture Models (GMM), as well as Self-Organizing Maps. The evaluation factors used for evaluation in this research are Cluster Quality Metrics, Scalability, Robustness to Noise, Cluster Shape and Density, Interpretability, Cluster Number, Dimensionality, and Consistency and Stability. These criteria and alternatives were chosen after conducting a thorough assessment of the literature and consulting with domain experts. All participated specialists actively engaged in the decision-making process, bringing unique insights into the best clustering algorithms for healthcare data. The results of this study illustrate each algorithm’s strengths and weaknesses in the setting of patient stratification, providing insight into their performance across multiple dimensions. The fuzzy TOPSIS MCDM strategy is a reliable instrument for synthesizing expert opinions and methodically evaluating the found clustering alternatives. This study advances healthcare analytics by giving practitioners and researchers with informative perspectives on the selection of clustering algorithms designed to address the unique problems of patient stratification utilizing EHR data.