Background In the era of information overload, are big data analytics the answer to access and better manage available knowledge? Over the last decade, the use of Web-based data in public health issues, that is, infodemiology, has been proven useful in assessing various aspects of human behavior. Google Trends is the most popular tool to gather such information, and it has been used in several topics up to this point, with health and medicine being the most focused subject. Web-based behavior is monitored and analyzed in order to examine actual human behavior so as to predict, better assess, and even prevent health-related issues that constantly arise in everyday life. Objective This systematic review aimed at reporting and further presenting and analyzing the methods, tools, and statistical approaches for Google Trends (infodemiology) studies in health-related topics from 2006 to 2016 to provide an overview of the usefulness of said tool and be a point of reference for future research on the subject. Methods Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for selecting studies, we searched for the term “Google Trends” in the Scopus and PubMed databases from 2006 to 2016, applying specific criteria for types of publications and topics. A total of 109 published papers were extracted, excluding duplicates and those that did not fall inside the topics of health and medicine or the selected article types. We then further categorized the published papers according to their methodological approach, namely, visualization, seasonality, correlations, forecasting, and modeling. Results All the examined papers comprised, by definition, time series analysis, and all but two included data visualization. A total of 23.1% (24/104) studies used Google Trends data for examining seasonality, while 39.4% (41/104) and 32.7% (34/104) of the studies used correlations and modeling, respectively. Only 8.7% (9/104) of the studies used Google Trends data for predictions and forecasting in health-related topics; therefore, it is evident that a gap exists in forecasting using Google Trends data. Conclusions The monitoring of online queries can provide insight into human behavior, as this field is significantly and continuously growing and will be proven more than valuable in the future for assessing behavioral changes and providing ground for research using data that could not have been accessed otherwise.
Saheed O. Sanni, Ajibola A. Bayode, Hendrik G. Brink
et al.
Over the years, the abuse of antibiotics has increased, leading to their presence in the environment. Therefore, a sustainable method for detecting these substances is crucial. Researchers have explored biomass-based carbon dots (CDs) to detect various contaminants, due to their low cost, environmental friendliness, and support of a circular economy. In our study, we reported the synthesis of CDs using pinecones (PCs) and pinebark (PB) through a sustainable microwave method. We characterized the PCCDs and PBCDs using X-ray diffraction, Raman spectroscopy, Transmission Electron Microscope, and Fourier transform infrared, Ultraviolet-visible, and photoluminescence (PL) spectroscopy. The PCCDs and PBCDs were tested for the detection of amoxicillin (AMX) and tetracycline (TC). The results indicated that the sizes of the PCCDs and PBCDs were 19.2 nm and 18.39 nm, respectively, and confirmed the presence of the 002 plane of the graphitic carbon structure. They exhibited excitation wavelength dependence, good stability, and quantum yields ranging from 6% to 11%. PCCDs and PBCDs demonstrated “turn-off” detection for TC and AMX. The limits of detection (LOD) for TC across a broader concentration range were found to be 0.062 µM for PCCDs and 0.2237 µM for PBCDs. For AMX detection, PBCDs presented an LOD of 0.49 µM.
Noisy labels pose significant challenges for AI model training in veterinary medicine. This study examines expert assessment ambiguity in canine auscultation data, highlights the negative impact of label noise on classification performance, and introduces methods for label noise reduction. To evaluate whether label noise can be minimized by incorporating multiple expert opinions, a dataset of 140 heart sound recordings (HSR) was annotated regarding the intensity of holosystolic heart murmurs caused by Myxomatous Mitral Valve Disease (MMVD). The expert opinions facilitated the selection of 70 high-quality HSR, resulting in a noise-reduced dataset. By leveraging individual heart cycles, the training data was expanded and classification robustness was enhanced. The investigation encompassed training and evaluating three classification algorithms: AdaBoost, XGBoost, and Random Forest. While AdaBoost and Random Forest exhibited reasonable performances, XGBoost demonstrated notable improvements in classification accuracy. All algorithms showed significant improvements in classification accuracy due to the applied label noise reduction, most notably XGBoost. Specifically, for the detection of mild heart murmurs, sensitivity increased from 37.71% to 90.98% and specificity from 76.70% to 93.69%. For the moderate category, sensitivity rose from 30.23% to 55.81% and specificity from 64.56% to 97.19%. In the loud/thrilling category, sensitivity and specificity increased from 58.28% to 95.09% and from 84.84% to 89.69%, respectively. These results highlight the importance of minimizing label noise to improve classification algorithms for the detection of canine heart murmurs. Index Terms: AI diagnosis, canine heart disease, heart sound classification, label noise reduction, machine learning, XGBoost, veterinary cardiology, MMVD.
Type 2 diabetes prevention and treatment can benefit from personalized lifestyle prescriptions. However, the delivery of personalized lifestyle medicine prescriptions is limited by the shortage of trained professionals and the variability in physicians' expertise. We propose an offline contextual bandit approach that learns individualized lifestyle prescriptions from the aggregated NHANES profiles of 119,555 participants by minimizing the Magni glucose risk-reward function. The model encodes patient status and generates lifestyle medicine prescriptions, which are trained using a mixed-action Soft Actor-Critic algorithm. The task is treated as a single-step contextual bandit. The model is validated against lifestyle medicine prescriptions issued by three certified physicians from Xiangya Hospital. These results demonstrate that offline mixed-action SAC can generate risk-aware lifestyle medicine prescriptions from cross-sectional NHANES data, warranting prospective clinical validation.
The medical ecosystem consists of the training of new clinicians and researchers, the practice of clinical medicine, and areas of adjacent research. There are many aspects of these domains that could benefit from the application of task automation and programmatic assistance. Machine learning and artificial intelligence techniques, including large language models (LLMs), have been promised to deliver on healthcare innovation, improving care speed and accuracy, and reducing the burden on staff for manual interventions. However, LLMs have no understanding of objective truth that is based in reality. They also represent real risks to the disclosure of protected information when used by clinicians and researchers. The use of AI in medicine in general, and the deployment of LLMs in particular, therefore requires careful consideration and thoughtful application to reap the benefits of these technologies while avoiding the dangers in each context.
ABSTRACT Background Guidance and principles for involving the public in research or service planning exist but are not specific to the needs of parents of children with life‐limiting conditions or bereaved parents. Aim Review the evidence on involving parents of children with life‐limiting conditions and bereaved parents in research, service planning and advocacy, and use this to develop best practice guidance. Methods Rapid review following the Cochrane Rapid Reviews Methods Group Guidance. MEDLINE and EMBASE were searched for primary studies of any design and literature/systematic reviews, and grey literature searching was conducted. Sources reporting on involving parents of children with life‐limiting conditions or bereaved parents in healthcare, research, or charity work in any setting, were included. Data were charted using the UK standards for public involvement in research (PPI). Two PPI consultation workshops were conducted with parents (n = 13) and healthcare professionals/charity representatives (n = 7). Results Six sources were included. Four reported benefits of parental involvement and two reported burdens. In relation to best practice, two reported on the importance of inclusive opportunities, three on working together, four on support and learning, three on communications, one on impact, and one on governance. PPI consultation workshops highlighted new factors which were not present in the literature around communication and understanding the impact of involvement. Conclusion Organisations working with this group should consider offering inclusive approaches to improve diversity, levelling power imbalances, ensuring flexibility of approach, and appropriate communication and impact. Patient or Public Contribution The study was conducted in collaboration with 13 parents of children with life‐limiting conditions and bereaved parents, and seven palliative care professionals. The group were involved at key stages of the review and contributed to the development of the findings and conduct of the review.
Foundation models (FMs) are large-scale deep learning models trained on massive datasets, often using self-supervised learning techniques. These models serve as a versatile base for a wide range of downstream tasks, including those in medicine and healthcare. FMs have demonstrated remarkable success across multiple healthcare domains. However, existing surveys in this field do not comprehensively cover all areas where FMs have made significant strides. In this survey, we present a comprehensive review of FMs in medicine, focusing on their evolution, learning strategies, flagship models, applications, and associated challenges. We examine how prominent FMs, such as the BERT and GPT families, are transforming various aspects of healthcare, including clinical large language models, medical image analysis, and omics research. Additionally, we provide a detailed taxonomy of FM-enabled healthcare applications, spanning clinical natural language processing, medical computer vision, graph learning, and other biology- and omics- related tasks. Despite the transformative potentials of FMs, they also pose unique challenges. This survey delves into these challenges and highlights open research questions and lessons learned to guide researchers and practitioners. Our goal is to provide valuable insights into the capabilities of FMs in health, facilitating responsible deployment and mitigating associated risks.
Understanding how cooperation emerges in public goods games is crucial for addressing societal challenges. While optional participation can establish cooperation without identifying cooperators, it relies on specific assumptions -- that individuals abstain and receive a non-negative payoff, or that non-participants cause damage to public goods -- which limits our understanding of its broader role. We generalize this mechanism by considering non-participants' payoffs and their potential direct influence on public goods, allowing us to examine how various strategic motives for non-participation affect cooperation. Using replicator dynamics, we find that cooperation thrives only when non-participants are motivated by individualistic or prosocial values, with individualistic motivations yielding optimal cooperation. These findings are robust to mutation, which slightly enlarges the region where cooperation can be maintained through cyclic dominance among strategies. Our results suggest that while optional participation can benefit cooperation, its effectiveness is limited and highlights the limitations of bottom-up schemes in supporting public goods.
Dongyeop Jang, Tae-Rim Yun, Choong-Yeol Lee
et al.
Traditional Korean medicine (TKM) emphasizes individualized diagnosis and treatment. This uniqueness makes AI modeling difficult due to limited data and implicit processes. Large language models (LLMs) have demonstrated impressive medical inference, even without advanced training in medical texts. This study assessed the capabilities of GPT-4 in TKM, using the Korean National Licensing Examination for Korean Medicine Doctors (K-NLEKMD) as a benchmark. The K-NLEKMD, administered by a national organization, encompasses 12 major subjects in TKM. We optimized prompts with Chinese-term annotation, English translation for questions and instruction, exam-optimized instruction, and self-consistency. GPT-4 with optimized prompts achieved 66.18% accuracy, surpassing both the examination's average pass mark of 60% and the 40% minimum for each subject. The gradual introduction of language-related prompts and prompting techniques enhanced the accuracy from 51.82% to its maximum accuracy. GPT-4 showed low accuracy in subjects including public health & medicine-related law, internal medicine (2) which are localized in Korea and TKM. The model's accuracy was lower for questions requiring TKM-specialized knowledge. It exhibited higher accuracy in diagnosis-based and recall-based questions than in intervention-based questions. A positive correlation was observed between the consistency and accuracy of GPT-4's responses. This study unveils both the potential and challenges of applying LLMs to TKM. These findings underline the potential of LLMs like GPT-4 in culturally adapted medicine, especially TKM, for tasks such as clinical assistance, medical education, and research. But they also point towards the necessity for the development of methods to mitigate cultural bias inherent in large language models and validate their efficacy in real-world clinical settings.
With the growing use of transformer-based language models in medicine, it is unclear how well these models generalize to nuclear medicine which has domain-specific vocabulary and unique reporting styles. In this study, we evaluated the value of domain adaptation in nuclear medicine by adapting language models for the purpose of 5-point Deauville score prediction based on clinical 18F-fluorodeoxyglucose (FDG) PET/CT reports. We retrospectively retrieved 4542 text reports and 1664 images for FDG PET/CT lymphoma exams from 2008-2018 in our clinical imaging database. Deauville scores were removed from the reports and then the remaining text in the reports was used as the model input. Multiple general-purpose transformer language models were used to classify the reports into Deauville scores 1-5. We then adapted the models to the nuclear medicine domain using masked language modeling and assessed its impact on classification performance. The language models were compared against vision models, a multimodal vision language model, and a nuclear medicine physician with seven-fold Monte Carlo cross validation, reported are the mean and standard deviations. Domain adaption improved all language models. For example, BERT improved from 61.3% five-class accuracy to 65.7% following domain adaptation. The best performing model (domain-adapted RoBERTa) achieved a five-class accuracy of 77.4%, which was better than the physician's performance (66%), the best vision model's performance (48.1), and was similar to the multimodal model's performance (77.2). Domain adaptation improved the performance of large language models in interpreting nuclear medicine text reports.
Healthcare applications with the Internet of Things (IoT) are often safety-critical, thus, require extensive testing. Such applications are often connected to smart medical devices from various vendors. System-level testing of such applications requires test infrastructures physically integrating medical devices, which is time and monetary-wise expensive. Moreover, applications continuously evolve, e.g., introducing new devices and users and updating software. Nevertheless, a test infrastructure enabling testing with a few devices is insufficient for testing healthcare IoT systems, hence compromising their dependability. In this paper, we propose a model-based approach for the creation and operation of digital twins (DTs) of medicine dispensers as a replacement for physical devices to support the automated testing of IoT applications at scale. We evaluate our approach with an industrial IoT system with medicine dispensers in the context of Oslo City and its industrial partners, providing healthcare services to its residents. We study the fidelity of DTs in terms of their functional similarities with their physical counterparts: medicine dispensers. Results show that the DTs behave more than 92% similar to the physical medicine dispensers, providing a faithful replacement for the dispenser.
Sidharth Sidharth, Ashish Abraham Samuel, Ranjana H
et al.
The detection of emotions using an Electroencephalogram (EEG) is a crucial area in brain-computer interfaces and has valuable applications in fields such as rehabilitation and medicine. In this study, we employed transfer learning to overcome the challenge of limited data availability in EEG-based emotion detection. The base model used in this study was Resnet50. Additionally, we employed a novel feature combination in EEG-based emotion detection. The input to the model was in the form of an image matrix, which comprised Mean Phase Coherence (MPC) and Magnitude Squared Coherence (MSC) in the upper-triangular and lower-triangular matrices, respectively. We further improved the technique by incorporating features obtained from the Differential Entropy (DE) into the diagonal, which previously held little to no useful information for classifying emotions. The dataset used in this study, SEED EEG (62 channel EEG), comprises three classes (Positive, Neutral, and Negative). We calculated both subject-independent and subject-dependent accuracy. The subject-dependent accuracy was obtained using a 10-fold cross-validation method and was 93.1%, while the subject-independent classification was performed by employing the leave-one-subject-out (LOSO) strategy. The accuracy obtained in subject-independent classification was 71.6%. Both of these accuracies are at least twice better than the chance accuracy of classifying 3 classes. The study found the use of MSC and MPC in EEG-based emotion detection promising for emotion classification. The future scope of this work includes the use of data augmentation techniques, enhanced classifiers, and better features for emotion classification.
Mycobacterium intracellulare is the most common cause of nontuberculous mycobacterial lung disease, with a rapidly growing prevalence worldwide. In this study, we performed comparative genomic analysis and antimicrobial susceptibility characteristics analysis of 117 clinical M. intracellulare strains in China. Phylogenetic analysis showed that clinical M. intracellulare strains had high genetic diversity and were not related to the geographical area. Notably, most strains (76.07%, 89/117) belonged to Mycobacterium paraintracellulare (MP) and Mycobacterium indicus pranii (MIP) in the genome, and we named them MP-MIP strains. These MP-MIP strains may be regarded as a causative agent of chronic lung disease. Furthermore, our data demonstrated that clarithromycin, amikacin, and rifabutin showed strong antimicrobial activity against both M. intracellulare and MP-MIP strains in vitro. Our findings also showed that there was no clear correlation between the rrs, rrl, and DNA gyrase genes (gyrA and gyrB) and the aminoglycosides, macrolides, and moxifloxacin resistance, respectively. In conclusion, this study highlights the high diversity of M. intracellulare in the clinical setting and suggests paying great attention to the lung disease caused by MP-MIP.
Martina Focardi, Barbara Gualco, Vilma Pinchi
et al.
Many studies have examined the genetic contribution to suicide. However, data on suicide in the Italian population are scarce. We therefore aimed to address this gap by investigating a cohort of 111 Italians for whom a verdict of suicide had been declared in court in Florence, Italy between 2007 and 2017. This cohort included 86 men and 25 women. DNA samples were obtained from tissues or blood, and 22 genes from multiple neurobiological pathways previously shown to be associated with the pathogenesis of suicide were analysed. Next-generation sequencing was used to compare these gene sequences with those from a large, normal population. In this study, we identified 19 gene variants that were present at significantly lower frequencies in our Italian cohort than in the general population. In addition, four missense mutations were identified in four different genes: Monoamine Oxidase A (MAOA), 5-Hydroxytryptamine Receptor 2 A (HTR2A), Sodium Voltage-Gated Channel Alpha Subunit 8 (SCN8A), and Nitric Oxide Synthase 3 (NOS3). Our study identified several potential genetic links with suicide in a cohort of Italians and supports a relationship between specific genetic variants and suicidal behaviour in this population. Key pointsThis study shows a genetic analysis of suicide.This study examines a cohort of 111 Italians for whom a verdict of suicide had been declared in court in Florence, Italy between 2007 and 2017. DNA samples were obtained from tissues or blood, and 22 genes from multiple neurobiological pathways were investigated.The study supports a relationship between specific genetic variants and suicidal behaviour.
Criminal law and procedure, Public aspects of medicine