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Background: Providing care to persons with complex problems is inherently difficult due to several factors, including the impacts of proximal determinants of health, treatment response, the natural emergence of comorbidities, and service system capacity to provide timely required services. Providing visibility into the dynamics of patients' engagement can help to optimize care for patients with complex problems. Method: In a previous work, graph machine learning and NLP methods were used to model the products of service system dynamics as atemporal entities, using a data model that collapsed patient encounter events across time. In this paper, the order of events is put back into the data model to provide topological depictions of the dynamics that are embodied in patients' movement across a complex healthcare system. Result: The results show that directed graphs are well suited to the task of depicting the way that the diverse components of the system are functionally coupled—or remain disconnected—by patient journeys. Conclusion: By setting the resolution on the graph topology visualization, important characteristics can be highlighted, including highly prevalent repeating sequences of service events readily interpretable by clinical subject matter experts. Moreover, this methodology provides a first step in addressing the challenge of locating potential operational problems for patients with complex issues engaging with a complex healthcare service system. [ABSTRACT FROM AUTHOR]

BACKGROUND: In many developing countries, a significant number of breast cancer patients are unable to receive timely treatment due to a large population base, high patient numbers, and limited medical resources. OBJECTIVE: This paper proposes a breast cancer assisted diagnosis system based on electronic medical records. The goal of this system is to address the limitations of existing systems, which primarily rely on structured electronic records and may miss crucial information stored in unstructured records. METHODS: The proposed approach is a breast cancer assisted diagnosis system based on electronic medical records. The system utilizes breast cancer enhanced convolutional neural networks with semantic initialization filters (BC-INIT-CNN). It extracts highly relevant tumor markers from unstructured medical records to aid in breast cancer staging diagnosis and effectively utilizes the important information present in unstructured records. RESULTS: The model's performance is assessed using various evaluation metrics. Such as accuracy, ROC curves, and Precision-Recall curves. Comparative analysis demonstrates that the BC-INIT-CNN model outperforms several existing methods in terms of accuracy and computational efficiency. CONCLUSIONS: The proposed breast cancer assisted diagnosis system based on BC-INIT-CNN showcases the potential to address the challenges faced by developing countries in providing timely treatment to breast cancer patients. By leveraging unstructured medical records and extracting relevant tumor markers, the system enables accurate staging diagnosis and enhances the utilization of valuable information. [ABSTRACT FROM AUTHOR]

Electronic health records are essential and sensitive since they include vital information and are routinely exchanged across several parties, such as hospitals and private clinics. These data must remain accurate, current, secret, and available only to authorized parties. Integrating these data improves the accuracy and cost-effectiveness of the present health data administration framework. Electronic Medical Records (EMRs) are now kept utilizing the structure of the client/server via whom patient data information is maintained in the hospital. Multiple hospitals use the same database to track a single patient. These limitations prevent a custom health system from providing various associated experts and patients with a cohesive, integrated, secure, and confidential medical history. Modern healthcare systems are distinguished by their complexity and expense. However, this may be mitigated by enhanced health record management and Blockchain technology. The Blockchain's data availability, confidence, and security characteristics have a bright future in healthcare services, giving solutions to the issues of the traditional customer/server architecture EMR management platform: intricacy, confidence, dependability, compatibility, and anonymity. An e-health record management based on Internet of Medical Things (EHRM-IoMT) is proposed in this paper. This paper explores and analyzes Blockchain efficiency and customer/server paradigms. The findings show that a patient-centred strategy may achieve remarkable success utilizing Blockchain. Moreover, the immutable and accurate data of persons in Blockchain may enable healthcare practitioners to better forecast and aid with diagnosis utilizing the IoMT via machine learning and artificial intelligence. [ABSTRACT FROM AUTHOR]

The article focuses on the importance of clinical informatics in neonatal care, highlighting its potential to provide critical resources for clinicians. Topics include the specialized data needed for neonatal care, the challenges in transitioning from paper to electronic health records, and the impact of informatics on real-time patient management and research.

Primary care physicians (PCPs) play an indispensable role in providing comprehensive care and referring patients for specialty care and other medical services. As the COVID-19 outbreak disrupts patient access to care, understanding the quality of primary care is critical at this unprecedented moment to support patients with complex medical needs in the primary care setting and inform policymakers to redesign our primary care system. The traditional way of collecting information from patient surveys is time-consuming and costly, and novel data collection and analysis methods are needed. In this review paper, we describe the existing algorithms and metrics that use the real-world data to qualify and quantify primary care, including the identification of an individual's likely PCP (identification of plurality provider and major provider), assessment of process quality (for example, appropriate-care-model composite measures), and continuity and regularity of care index (including the interval index, variance index and relative variance index), and highlight the strength and limitation of real world data from electronic health records (EHRs) and claims data in determining the quality of PCP care. The EHR audits facilitate assessing the quality of the workflow process and clinical appropriateness of primary care practices. With extensive and diverse records, administrative claims data can provide reliable information as it assesses primary care quality through coded information from different providers or networks. The use of EHRs and administrative claims data may be a cost-effective analytic strategy for evaluating the quality of primary care. [ABSTRACT FROM AUTHOR]

As information communication technology (ICT) has advanced, the healthcare industry has embraced it to reduce medical costs, improve health outcomes, and increase patient satisfaction. Healthcare ICT revolutionizes pediatric healthcare. This study aimed to categorize and synthesize findings from the literature regarding the application of ICT in pediatric patients. This systematic review is based on a comprehensive search of Embase, MEDLINE, and Google Scholar. Study selection and coding were performed independently by 2 researchers, followed by narrative categorization. To reflect current trends in ICT for pediatrics, we adopted the Hype cycle technology classification developed by the advisory and information technology firm, Gartner, and the classification of digital health interventions by the World Health Organization. This study included a total of 135 studies. The analysis revealed 7 main types of ICT for pediatrics: (1) telehealth (39 papers), (2) precision medicine (2 papers), (3) automated decision support systems (17 papers), (4) electronic health records (7 papers), (5) patient portals (7 papers), (6) artificial intelligence (AI) (39 papers), and (7) mobile and wearable technologies (20 papers). In particular, we consistently found references to ICT for pediatrics as well as changing and improving healthcare for children. Further studies are required to determine how we can improve ICT productivity for pediatrics, particularly through AI. This study's results will help healthcare delivery organizations and technology companies consider the future direction of pediatric healthcare. [ABSTRACT FROM AUTHOR]

Despite improved ancillary investigations in epilepsy care, patients' narratives remain indispensable for diagnosing and treatment monitoring. This wealth of information is typically stored in electronic health records and accumulated in medical journals in an unstructured manner, thereby restricting complete utilization in clinical decision‐making. To this end, clinical researchers increasing apply natural language processing (NLP)—a branch of artificial intelligence—as it removes ambiguity, derives context, and imbues standardized meaning from free‐narrative clinical texts. This systematic review presents an overview of the current NLP applications in epilepsy and discusses the opportunities and drawbacks of NLP alongside its future implications. We searched the PubMed and Embase databases with a "natural language processing" and "epilepsy" query (March 4, 2022) and included original research articles describing the application of NLP techniques for textual analysis in epilepsy. Twenty‐six studies were included. Fifty‐eight percent of these studies used NLP to classify clinical records into predefined categories, improving patient identification and treatment decisions. Other applications of NLP had structured clinical information retrieval from electronic health records, scientific papers, and online posts of patients. Challenges and opportunities of NLP applications for enhancing epilepsy care and research are discussed. The field could further benefit from NLP by replicating successes in other health care domains, such as NLP‐aided quality evaluation for clinical decision‐making, outcome prediction, and clinical record summarization. [ABSTRACT FROM AUTHOR]

Background: In recent years, with the development of artificial intelligence, the use of deep learning technology for clinical information extraction has become a new trend. Clinical Event Detection (CED) as its subtask has attracted the attention from academia and industry. However, directly applying the advancements in deep learning to CED task often yields unsatisfactory results. The main reasons are due to the following two points: (1) A great number of obscure professional terms in the electronic medical record leads to poor recognition performance of model. (2) The scarcity of datasets required for the task leads to poor model robustness. Therefore, it is urgent to solve these two problems to improve model performance.Methods: This paper proposes a combining data augmentation and domain information with TENER Model for Clinical Event Detection.Results: We use two evaluation metrics to compare the overall performance of the proposed model with the existing model on the 2012 i2b2 challenge dataset. Experimental results demonstrate that our proposed model achieves the best F1-score of 80.26%, type accuracy of 93% and Span F1-score of 90.33%, and outperforms the state-of-the-art approaches.Conclusions: This paper proposes a multi-granularity information fusion encoder-decoder framework, which applies the TENER model to the CED task for the first time. It uses the pre-trained language model (BioBERT) to generate word-level features, solving the problem of a great number of obscure professional terms in the electronic medical record lead to poor recognition performance of model. In addition, this paper proposes a new data augmentation method for sequence labeling tasks, solving the problem of the scarcity of datasets required for the task leads to poor model robustness. [ABSTRACT FROM AUTHOR]

Background: This study aimed to explore whether explainable Artificial Intelligence methods can be fruitfully used to improve the medical management of patients suffering from complex diseases, and in particular to predict the death risk in hospitalized patients with SARS-Cov-2 based on admission data. Methods: This work is based on an observational ambispective study that comprised patients older than 18 years with a positive SARS-Cov-2 diagnosis that were admitted to the hospital Azienda Ospedaliera "SS Antonio e Biagio e Cesare Arrigo", Alessandria, Italy from February, 24 2020 to May, 31 2021, and that completed the disease treatment inside this structure. The patients'medical history, demographic, epidemiologic and clinical data were collected from the electronic medical records system and paper based medical records, entered and managed by the Clinical Study Coordinators using the REDCap electronic data capture tool patient chart. The dataset was used to train and to evaluate predictive ML models. Results: We overall trained, analysed and evaluated 19 predictive models (both supervised and unsupervised) on data from 824 patients described by 43 features. We focused our attention on models that provide an explanation that is understandable and directly usable by domain experts, and compared the results against other classical machine learning approaches. Among the former, JRIP showed the best performance in 10-fold cross validation, and the best average performance in a further validation test using a different patient dataset from the beginning of the third COVID-19 wave. Moreover, JRIP showed comparable performances with other approaches that do not provide a clear and/or understandable explanation. Conclusions: The ML supervised models showed to correctly discern between low-risk and high-risk patients, even when the medical disease context is complex and the list of features is limited to information available at admission time. Furthermore, the models demonstrated to reasonably perform on a dataset from the third COVID-19 wave that was not used in the training phase. Overall, these results are remarkable: (i) from a medical point of view, these models evaluate good predictions despite the possible differences entitled with different care protocols and the possible influence of other viral variants (i.e. delta variant); (ii) from the organizational point of view, they could be used to optimize the management of health-care path at the admission time. [ABSTRACT FROM AUTHOR]

Development of artificial intelligence methods in medicine requires large volumes of input data available. The source of this data is electronic health records. Extraction of data from health records is accompanied by a number of difficulties, mainly associated with their being filled out in any form and doctors using various abbreviations when putting down the information. The paper describes a method of processing electronic health records which allows extracting the necessary information from them - the one needed for building work algorithms of artificial intelligence software complexes and their learning. The method was developed and tested out on electronic health records filled out in Russian. For working with medical documents filled out in other languages, it needs no special adaptation. For this, it is sufficient to change teaching data and perform complete learning of all models. [ABSTRACT FROM AUTHOR]

Artificial intelligence has the potential to revolutionize modern society in all its aspects. Encouraged by the variety and vast amount of data that can be gathered from patients (e.g., medical images, text, and electronic health records), researchers have recently increased their interest in developing AI solutions for clinical care. Moreover, a diverse repertoire of methods can be chosen towards creating performant models for use in medical applications, ranging from disease prediction, diagnosis, and prognosis to opting for the most appropriate treatment for an individual patient. In this respect, the present paper aims to review the advancements reported at the convergence of AI and clinical care. Thus, this work presents AI clinical applications in a comprehensive manner, discussing the recent literature studies classified according to medical specialties. In addition, the challenges and limitations hindering AI integration in the clinical setting are further pointed out. [ABSTRACT FROM AUTHOR]

• Heterogeneous feature learning. Exploiting the complementarity of numerical data and text data to address the issue of data sparsity. • Novel multi-task model. A Shared-bottom layer is introduced between the MoE layer and the input layer of MMoE to reduce feature dimension. • Practical application. The output layer of Shared-MMOE is modified to multi-classification mode, enabling the diagnosis of 17 kinds of diseases and pests and the recommendation of 32 kinds of medicines. • Sequential dependence handling for cascaded task. In order to improve the recommendation effect of medication tasks, the output of diagnostic task is taken as the input of medication task in the form of probability weighting. Research on diagnosis of crop diseases and pests becomes a hot topic of the application of artificial intelligence technology in smart agriculture. Plant electronic medical records (PEMRs) formed by Beijing Plant Clinic provides a new idea for the diagnosis and prevention of crop diseases and pests. PEMRs are stored in the form of heterogeneous data, containing a wealth of plant information, disease and pest information, and environmental information. Therefore, it is urgent to mine the information in PEMRs and employ it to assist in intelligent prescription recommendation. This paper divides prescription recommendation into two sub-tasks, diagnosis and medication, and transforms this problem into a recommendation problem based on multi-task learning, with the goal of establishing a single model to realize learning multi-task simultaneously. Firstly, the correlation analysis of tasks and features is carried out using methods such as knowledge graph. Further, according to the sequential dependency between tasks, a novel cascaded multi-task crop prescription recommendation method based on Shared-Bottom and MMoE (Shared-MMoE) model is proposed, and each task is optimized by gating network. A PEMRs dataset containing 8 diseases, 9 pests and 32 medicines was constructed for model verification. Compared with the baseline model, the experiments showed that Shared-MMoE could significantly improve the quality and accuracy of prescription recommendation. The AUC of diagnosis task and medication task reached 96.33% and 95.36%, respectively. In conclusion, our study preliminarily explored the potential application of artificial intelligence in the research of crop diseases and pests based on PEMRs and multi-task learning. [ABSTRACT FROM AUTHOR]

Purpose: Prescriptive and predictive analytics and artificial intelligence (AI) provide tools to analyze data with objectivity. In this paper, we provide an overview of how these techniques can improve nursing care, and we detail a quantitative model to afford managerial insights about care management in a Hospital in Colombia. Our main purpose is to provide tools to improve key performance indicators for the care management of inpatients which includes the nurse workload. Methods: The optimal nurse‐to‐patient assignment problem is addressed using analytics, lean health care, and AI. Also, we propose a new mathematical model to optimize the nurse‐to‐patient assignment decisions considering several variables about the patient state such as the Barthel index, their risks, the complexity of the care, and the mental state. Findings: Our results show that there are several processes inherent to compassionate nursing care that can be improved using technology. By using data analytics, we can also provide insights about the high variability of the care requirements and, by using models, find nurse‐to‐patient assignments that are nearly perfectly balanced. Conclusions: We illustrated this improvement with a pilot test that makes the equitable distribution of nursing workload the functionality of this strategy. The findings can be useful in highly complex hospitals in Latin America. Clinical Relevance: The proposed model presents an opportunity to make near perfectly balanced nurse‐to‐patient assignments according to the number of patients and their health conditions using technology. [ABSTRACT FROM AUTHOR]

The explosion of medical information demands a thorough reconsideration of medical education, including what we teach and assess, how we educate, and whom we educate. Physicians of the future will need to be self-aware, self-directed, resource-effective team players who can synthesize and apply summarized information and communicate clearly. Training in metacognition, data science, informatics, and artificial intelligence is needed. Education programs must shift focus from content delivery to providing students explicit scaffolding for future learning, such as the Master Adaptive Learner model. Additionally, educators should leverage informatics to improve the process of education and foster individualized, precision education. Finally, attributes of the successful physician of the future should inform adjustments in recruitment and admissions processes. This paper explores how member schools of the American Medical Association Accelerating Change in Medical Education Consortium adjusted all aspects of educational programming in acknowledgment of the rapid expansion of information. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI)‐based technologies employing deep‐learning (DL) approaches have proven effective in supporting decisions in many medical specialties, including radiology, cardiology, oncology, dermatology, ophthalmology, and others. For example, AI/DL algorithms have been shown to reduce waiting times, improve medication adherence, customize insulin dosages, and help interpret magnetic resonance images. The number of AI life‐science papers listed in PubMed increased from 596 in 2010 to 12 422 in 2019. The number of papers on the use of AI in the field of ophthalmology also has increased dramatically. Moreover, human cognitive capacity to effectively manage information is often exceeded by the quantity of data generated, and unlike humans, who have bad days and emotions, and who get tired, with subsequent decreases in performance and accuracy, AI works 24/7 without vacations or complaints. AI/DL algorithms have been used to detect diseases based on image analysis, with fundus photos and optical coherence tomography (OCT) scans analysed for retinal diseases, chest radiographs assessed for lung diseases, and skin photos analysed for skin disorders. Retinal photos have also been used to identify risk factors related to cardiovascular disorders, including blood pressure, smoking, and body mass index. One of many challenges in the field of AI is determining what constitutes evidence of impact and benefit for AI medical devices and who should assess the evidence. The majority of AI studies are conducted in experimental conditions and based on preselected data. They might provide inadequate insight into the use of AI applications in heterogeneous, real‐world care settings. One of the potential hazards of the clinical use of algorithms identified was the risk of applying an algorithm trained on a particular demographic group to a population that differs in factors such as ethnicity, age, and sex. Moreover, many studies of algorithms developed with AI have excluded low‐quality images, treating them as ungradable images, and patients with comorbid eye diseases, making them less reflective of real‐world conditions. The future development of ophthalmology depends on better and possibly unlimited access to the medical data stored within electronic health records. However, this access cannot be allowed to compromise of privacy of this very sensitive data. There is a need for effective regulations that will set a balance between individual protection and the common good. A recent report from the National Academy of Medicine highlights some important challenges in the further development of AI applications in health care. The authors advocate the use of openly accessible, standardized, population‐representative data; addressing explicit and implicit biases related to AI; developing and deploying appropriate training and educational programs for health workers to support health‐care AI; and balancing innovation and safety through the use of regulation and legislation to promote trust. [ABSTRACT FROM AUTHOR]

Hospital-acquired pressure injuries (HAPIs) constitute a significant challenge harming thousands of people worldwide yearly. While various tools and methods are used to identify pressure injuries, artificial intelligence (AI) and decision support systems (DSS) can help to reduce HAPIs risks by proactively identifying patients at risk and preventing them before harming patients. This paper comprehensively reviews AI and DSS applications for HAPIs prediction using Electronic Health Records (EHR), including a systematic literature review and bibliometric analysis. A systematic literature review was conducted through PRISMA and bibliometric analysis. In February 2023, the search was performed using four electronic databases: SCOPIS, PubMed, EBSCO, and PMCID. Articles on using AI and DSS in the management of PIs were included. The search approach yielded 319 articles, 39 of which have been included and classified into 27 AI-related and 12 DSS-related categories. The years of publication varied from 2006 to 2023, with 40% of the studies taking place in the US. Most studies focused on using AI algorithms or DSS for HAPIs prediction in inpatient units using various types of data such as electronic health records, PI assessment scales, and expert knowledge-based and environmental data to identify the risk factors associated with HAPIs development. There is insufficient evidence in the existing literature concerning the real impact of AI or DSS on making decisions for HAPIs treatment or prevention. Most studies reviewed are solely hypothetical and retrospective prediction models, with no actual application in healthcare settings. The accuracy rates, prediction results, and intervention procedures suggested based on the prediction, on the other hand, should inspire researchers to combine both approaches with larger-scale data to bring a new venue for HAPIs prevention and to investigate and adopt the suggested solutions to the existing gaps in AI and DSS prediction methods. • Systematic review of ML methods for predicting hospital-acquired pressure injuries. • Systematic review of Decision Support systems for predicting hospital-acquired pressure injuries. • Bibliometric review on HAPI prediction: trends in keywords, top-cited studies, and publishing countries. • Summary of the study's input data, time span, results, and key contributions. • Gaps and solutions for ML and DSS models in HAPI prediction. [ABSTRACT FROM AUTHOR]

Background: Early detection and prediction of suicidal behaviour are key factors in suicide control. In conjunction with recent advances in the field of artificial intelligence, there is increasing research into how machine learning can assist in the detection, prediction and treatment of suicidal behaviour. Therefore, this study aims to provide a comprehensive review of the literature exploring machine learning techniques in the study of suicidal behaviour prediction.Methods: A search of four databases was conducted: Web of Science, PubMed, Dimensions, and Scopus for research papers dated between January 2016 and September 2021. The search keywords are 'data mining', 'machine learning' in combination with 'suicidal behaviour', 'suicide', 'suicide attempt', 'suicidal ideation', 'suicide plan' and 'self-harm'. The studies that used machine learning techniques were synthesized according to the countries of the articles, sample description, sample size, classification tasks, number of features used to develop the models, types of machine learning techniques, and evaluation of performance metrics.Results: Thirty-five empirical articles met the criteria to be included in the current review. We provide a general overview of machine learning techniques, examine the feature categories, describe methodological challenges, and suggest areas for improvement and research directions. Ensemble prediction models have been shown to be more accurate and useful than single prediction models.Conclusions: Machine learning has great potential for improving estimates of future suicidal behaviour and monitoring changes in risk over time. Further research can address important challenges and potential opportunities that may contribute to significant advances in suicide prediction. [ABSTRACT FROM AUTHOR]

Background: The recent, rapid development of digital technologies offers new possibilities for more efficient implementation of electronic health record (EHR) and personal health record (PHR) systems. A growing volume of healthcare data has been the hallmark of this digital transformation. The large healthcare datasets' complexity and their dynamic nature pose various challenges related to processing, analysis, storage, security, privacy, data exchange, and usability.Materials and Methods: We performed a systematic review of systematic reviews to assess technological progress in EHR and PHR systems. We searched MEDLINE, Cochrane, Web of Science, and Scopus for systematic literature reviews on technological advancements that support EHR and PHR systems published between January 1, 2010, and October 06, 2020.Results: The searches resulted in a total of 2,448 hits. Of these, we finally selected 23 systematic reviews. Most of the included papers dealt with information extraction tools and natural language processing technology (n = 10), followed by studies that assessed the use of blockchain technology in healthcare (n = 8). Other areas of digital technology research included EHR and PHR systems in austere settings (n = 1), de-identification methods (n = 1), visualization techniques (n = 1), communication tools within EHR and PHR systems (n = 1), and methodologies for defining Clinical Information Models that promoted EHRs and PHRs interoperability (n = 1).Conclusions: Technological advancements can improve the efficiency in the implementation of EHR and PHR systems in numerous ways. Natural language processing techniques, either rule-based, machine-learning, or deep learning-based, can extract information from clinical narratives and other unstructured data locked in EHRs and PHRs, allowing secondary research (i.e., phenotyping). Moreover, EHRs and PHRs are expected to be the primary beneficiaries of the blockchain technology implementation on Health Information Systems. Governance regulations, lack of trust, poor scalability, security, privacy, low performance, and high cost remain the most critical challenges for implementing these technologies. [ABSTRACT FROM AUTHOR]