Multiple Data Types Research Articles

Informatics assessment of COVID-19 data collection: an analysis of UK Biobank questionnaire data

BackgroundThere have been many efforts to expand existing data collection initiatives to include COVID-19 related data. One program that expanded is UK Biobank, a large-scale research and biomedical data collection resource that added several COVID-19 related data fields including questionnaires (exposures and symptoms), viral testing, and serological data. This study aimed to analyze this COVID-19 data to understand how COVID-19 data was collected and how it can be used to attribute COVID-19 and analyze differences in cohorts and time periods.MethodsA cohort of COVID-19 infected individuals was defined from the UK Biobank population using viral testing, diagnosis, and self-reported data. Changes over time, from March 2020 to October 2021, in total case counts and changes in case counts by identification source (diagnosis from EHR, measurement from viral testing and self-reported from questionnaire) were also analyzed. For the questionnaires, an analysis of the structure and dynamics of the questionnaires was done which included the amount and type of questions asked, how often and how many individuals answered the questions and what responses were given. In addition, the amount of individuals who provided responses regarding different time segments covered by the questionnaire was calculated along with how often responses changed. The analysis included changes in population level responses over time. The analyses were repeated for COVID and non-COVID individuals and compared responses.ResultsThere were 62 042 distinct participants who had COVID-19, with 49 120 identified through diagnosis, 30 553 identified through viral testing and 934 identified through self-reporting, with many identified in multiple methods. This included vast changes in overall cases and distribution of case data source over time. 6 899 of 9 952 participants completing the exposure questionnaire responded regarding every time period covered by the questionnaire including large changes in response over time. The most common change came for employment situation, which was changed by 74.78% of individuals from the first to last time of asking. On a population level, there were changes as face mask usage increased each successive time period. There were decreases in nearly every COVID-19 symptom from the first to the second questionnaire. When comparing COVID to non-COVID participants, COVID participants were more commonly keyworkers (COVID: 33.76%, non-COVID: 15.00%) and more often lived with young people attending school (61.70%, 45.32%).ConclusionTo develop a robust cohort of COVID-19 participants from the UK Biobank population, multiple types of data were needed. The differences based on time and exposures show the important of comprehensive data capture and the utility of COVID-19 related questionnaire data.

Novel approach to combining interview and register data – Case: social security and pediatric cancer

Abstract A parallel examination of interview data and register data provides a more holistic overall picture of social security needs and use than any one type of data alone. In our study, we aimed to build a comprehensive understanding of the benefit and service needs of parents whose child is diagnosed with cancer. Conducting solely interviews would have provided subjective perceptions of individuals in the midst of a challenging life situation, but the data would have lacked information of the participants’ documented use of social security benefits and services. On the other hand, utilizing solely register data, would have provided an objective overview of actual service use. However, register data alone would not have shed light on whether the benefits and services received were sufficient from the recipients’ point of view. In our study, parents’ interviews and their register data from the Social Insurance Institution of Finland (Kela) offered complementary perspectives on the same life situation. Parents’ interviews provided information on support needs, service experiences as well as service deficits. The same families’ register data was used with their permission producing exact data on the insurance benefits and services that the family had applied for and received. The interviews were analysed as chronologically progressing narratives while the data received from Kela was depicted on timelines. When conjoined, the interview and register data formed a comprehensive picture of the benefit and service needs and use of families, whose child had cancer. In addition, this kind of triangulation revealed service deficits that were acknowledged by the interviewees and identified also through the register data. We argue, that combining subjective interviews together with institutional register data allow researchers to identify issues in the service system, which require improvements from the recipients’ and the system’s perspective. Key messages • The use of multiple types of data allows researchers to construct a holistic understanding of individuals’ social security needs and use. • Combining interview and register data allows researchers to identify issues in the service system, which require improvements.

Dog10K: an integrated Dog10K database summarizing canine multi-omics.

The diversity observed in canine breed phenotypes, together with their risk for heritabily disorders of relevance to dogs and humans, makes the species an ideal subject for studies aimed at understanding the genetic basis of complex traits and human biomedical models. Dog10K is an ongoing international collaboration that aims to uncover the genetic basis of phenotypic diversity, disease, behavior, and domestication history of dogs. To best present and make the extensive data accessible and user friendly, we have established the Dog10K (http://dog10k.kiz.ac.cn/) database, a comprehensive-omics resource summarizing multiple types of data. This database integrates single nucleotide variants (SNVs) from 1987 canine genomes, de-novo mutations (DNMs) from 43 dog breeds with>40×sequence, RNA-seq data of 105057 single nuclei from hippocampus, 74067 single cells from leukocytesand 30 blood samples from published canid studies. We provide clear visualization, statistics, browse, searching, and downloading functions for all data. We have integrated three analysis tools, Selscan, LiftOver and AgeConversion, to aid researchers in custom exploration of the comprehensive-omics data. The Dog10K database will serve as a foundational platform for analyzing, presenting and utilizing canine multi-omics data.

Dynamics of Amphibian Pathogen Detection Using Extended Museum Specimens.

Natural history collections have long served as the foundation for understanding our planet's biodiversity, yet they remain a largely untapped resource for wildlife disease studies. Extended specimens include multiple data types and specimen preparations that capture the phenotype and genotype of an organism and its symbionts-but preserved tissues may not always be optimized for downstream detection of various pathogens. Frogs are infected by an array of pathogens including Batrachochytrium dendrobatidis (Bd), Ranavirus (Rv), and Amphibian Perkinsea (Pr), which provides the opportunity to study differences in detection dynamics across tissue types. We used quantitative PCR protocols to screen two tissue types commonly deposited in museum collections, toe clips and liver, from two closely related host species, Rana catesbeiana and Rana clamitans. We compared Bd, Rv, and Pr infection prevalence and intensity between species and tissue types and found no significant difference in prevalence between species, but Bd intensity was higher in R. clamitans than R. catesbeiana. Toe tissue exhibited significantly higher Bd infection loads and was more useful than liver for detecting Bd infections. In contrast, Rv was detected from more liver than toe tissues, but the difference was not statistically significant. Our results support the use of extended specimen collections in amphibian disease studies and demonstrate that broader tissue sampling at the time of specimen preparation can maximize their utility for downstream multipathogen detection.

Interpretable multi-omics integration with UMAP embeddings and density-based clustering.

Integrating high-dimensional cellular multi-omics data is crucial for understanding various layers of biological control. Single 'omic methods provide important insights, but often fall short in handling the complex relationships between genes, proteins, metabolites and beyond. Here, we present a novel, non-linear, and unsupervised method called GAUDI (Group Aggregation via UMAP Data Integration) that leverages independent UMAP embeddings for the concurrent analysis of multiple data types. GAUDI uncovers non-linear relationships among different omics data better than several state-of-the-art methods. This approach not only clusters samples by their multi-omic profiles but also identifies latent factors across each omics dataset, thereby enabling interpretation of the underlying features contributing to each cluster. Consequently, GAUDI facilitates more intuitive, interpretable visualizations to identify novel insights and potential biomarkers from a wide range of experimental designs.

ISubGen generates integrative disease subtypes by pairwise similarity assessment.

There are myriad types of biomedical data-molecular, clinical images, and others. When a group of patients with the same underlying disease exhibits similarities across multiple types of data, this is called a subtype. Existing subtyping approaches struggle to handle diverse data types with missing information. To improve subtype discovery, we exploited changes in the correlation-structure between different data types to create iSubGen, an algorithm for integrative subtype generation. iSubGen can accommodate any feature that can be compared with a similarity metric to create subtypes versatilely. It can combine arbitrary data types for subtype discovery, such as merging genetic, transcriptomic, proteomic, and pathway data. iSubGen recapitulates known subtypes across multiple cancers even with substantial missing data and identifies subtypes with distinct clinical behaviors. It performs equally with or superior to other subtyping methods, offering greater stability and robustness to missing data and flexibility to new data types. It is available at https://cran.r-project.org/web/packages/iSubGen.

Harnessing uncertainty: A deep mechanistic approach for cautious diagnostic and forecast of Bovine Respiratory Disease

Bovine Respiratory Disease (BRD) is a prevalent infectious disease of respiratory tract in cattle, presenting challenges in accurate diagnosis and forecasting due to the complex interactions of multiple risk factors. Common methods, including mathematical epidemiological models and data-driven approaches such as machine learning models, face limitations such as difficult parameter estimation or the need for data across all potential outcomes, which is challenging given the scarcity and noise in observing BRD processes. In response to these challenges, we introduce a novel approach known as the Bayesian Deep Mechanistic method. This method couples a data-driven model with a mathematical epidemiological model while accounting for uncertainties within the processes. By utilising 265 lung ultrasound videos as sensor data from 163 animals across 9 farms in France, we trained a Bayesian deep learning model to predict the infection status (infected or non-infected) of an entire batch of 12 animals, also providing associated confidence levels. These predictions, coupled with their confidence levels, were used to filter out highly uncertain diagnoses and diffuse uncertainties into the parameterisation of a mathematical epidemiological model to forecast the progression of infected animals. Our findings highlight that considering the confidence levels (or uncertainties) of predictions enhances both the diagnosis and forecasting of BRD. Uncertainty-aware diagnosis reduced errors to 32 %, outperforming traditional automatic diagnosis. Forecast relying on veterinarian diagnoses, considered the most confident, had a 23 % error, whilst forecast taking into account diagnosis uncertainties had a close 27.2 % error. Building upon uncertainty-awareness, our future research could explore integrating multiple types of sensor data, such as video surveillance, audio recordings, and environmental parameters, to provide a comprehensive evaluation of animal health, employing multi-modal methods for processing this diverse data.

Modeling movements improves capture–recapture estimates for mobile species with sparse data: Polar bears (Ursus maritimus) in Viscount Melville sound

AbstractWildlife management requires estimates of demographic parameters that are difficult to obtain for mobile species at low densities. Biased parameter estimates often result from capture–recapture (CR) studies due to small sample sizes and unequal recapture probabilities, the latter of which can be caused by animal movements with respect to the sampling area. We developed a multistate CR model designed to minimize biases by including multiple data types (capture, harvest, natural mortality, and telemetry) and accounting for temporary emigration. We applied the model to data collected intensively from 2012 to 2014, and intermittently since the 1970s, for the Viscount Melville (VM) subpopulation of polar bears (Ursus maritimus) in the Canadian Arctic. The number of bears within the VM subpopulation boundary likely increased from an average of 145 (Bayesian 95% credible interval [CRI] [109, 221]) in 1989–1992 to 235 (95% CRI [148, 569]) in 2012–2014. Survival probability increased for all sex and age classes except adult females, for which estimates declined due to unknown reasons. Polar bear movements exhibited Markovian dependence with approximately 28% of the subpopulation located outside of the sampling area each spring. This contributed to inaccurate parameter estimates when using a simpler, single‐state CR model that only included capture data. Although the interpretation of demographic status was complicated by statistical uncertainty and changes in study design, our findings suggest that—as of 2014—the VM polar bear subpopulation had likely recovered from an earlier period of overharvest, was stable, and had not exhibited detectable negative effects of climate warming.

Abundance measurements reveal the balance between lysis and lysogeny in the human gut microbiome.

The human gut contains diverse communities of bacteriophage, whose interactions with the broader microbiome and potential roles in human health are only beginning to be uncovered. Here, we combine multiple types of data to quantitatively estimate gut phage population dynamics and lifestyle characteristics in human subjects. Unifying results from previous studies, we show that an average human gut contains a low ratio of phage particles to bacterial cells (~1:100), but a much larger ratio of phage genomes to bacterial genomes (~4:1), implying that most gut phage are effectively temperate (e.g., integrated prophage, phage-plasmids, etc.). By integrating imaging and sequencing data with a generalized model of temperate phage dynamics, we estimate that phage induction and lysis occurs at a low average rate (~0.001-0.01 per bacterium per day), imposing only a modest fitness burden on their bacterial hosts. Consistent with these estimates, we find that the phage composition of a diverse synthetic community in gnotobiotic mice can be quantitatively predicted from bacterial abundances alone, while still exhibiting phage diversity comparable to native human microbiomes. These results provide a foundation for interpreting existing and future studies on links between the gut virome and human health.

Hardware optimization for effective switching power reduction during data compression in GOLOMB rice coding.

Loss-less data compression becomes the need of the hour for effective data compression and computation in VLSI test vector generation and testing in addition to hardware AI/ML computations. Golomb code is one of the effective technique for lossless data compression and it becomes valid only when the divisor can be expressed as power of two. This work aims to increase compression ratio by further encoding the unary part of the Golomb Rice (GR) code so as to decrease the amount of bits used, it mainly focuses on optimizing the hardware for encoding side. The algorithm was developed and coded in Verilog and simulated using Modelsim. This code was then synthesised in Cadence Encounter RTL Synthesiser. The modifications carried out show around 6% to 19% reduction in bits used for a linearly distributed data set. Worst-case delays have been reduced by 3% to 8%. Area reduction varies from 22% to 36% for different methods. Simulation for Power consumption shows nearly 7% reduction in switching power. This ideally suggest the usage of Golomb Rice coding technique for test vector compression and data computation for multiple data types, which should ideally have a geometrical distribution.

Aardvark: Composite Visualizations of Trees, Time-Series, and Images.

How do cancer cells grow, divide, proliferate, and die? How do drugs infuence these processes? These are diffcult questions that we can attempt to answer with a combination of time-series microscopy experiments, classifcation algorithms, and data visualization. However, collecting this type of data and applying algorithms to segment and track cells and construct lineages of proliferation is error-prone; and identifying the errors can be challenging since it often requires cross-checking multiple data types. Similarly, analyzing and communicating the results necessitates synthesizing different data types into a single narrative. State-of-the-art visualization methods for such data use independent line charts, tree diagrams, and images in separate views. However, this spatial separation requires the viewer of these charts to combine the relevant pieces of data in memory. To simplify this challenging task, we describe design principles for weaving cell images, time-series data, and tree data into a cohesive visualization. Our design principles are based on choosing a primary data type that drives the layout and integrates the other data types into that layout. We then introduce Aardvark, a system that uses these principles to implement novel visualization techniques. Based on Aardvark, we demonstrate the utility of each of these approaches for discovery, communication, and data debugging in a series of case studies.

Does combining numerous data types in multi-omics data improve or hinder performance in survival prediction? Insights from a large-scale benchmark study

BackgroundPredictive modeling based on multi-omics data, which incorporates several types of omics data for the same patients, has shown potential to outperform single-omics predictive modeling. Most research in this domain focuses on incorporating numerous data types, despite the complexity and cost of acquiring them. The prevailing assumption is that increasing the number of data types necessarily improves predictive performance. However, the integration of less informative or redundant data types could potentially hinder this performance. Therefore, identifying the most effective combinations of omics data types that enhance predictive performance is critical for cost-effective and accurate predictions.MethodsIn this study, we systematically evaluated the predictive performance of all 31 possible combinations including at least one of five genomic data types (mRNA, miRNA, methylation, DNAseq, and copy number variation) using 14 cancer datasets with right-censored survival outcomes, publicly available from the TCGA database. We employed various prediction methods and up-weighted clinical data in every model to leverage their predictive importance. Harrell’s C-index and the integrated Brier Score were used as performance measures. To assess the robustness of our findings, we performed a bootstrap analysis at the level of the included datasets. Statistical testing was conducted for key results, limiting the number of tests to ensure a low risk of false positives.ResultsContrary to expectations, we found that using only mRNA data or a combination of mRNA and miRNA data was sufficient for most cancer types. For some cancer types, the additional inclusion of methylation data led to improved prediction results. Far from enhancing performance, the introduction of more data types most often resulted in a decline in performance, which varied between the two performance measures.ConclusionsOur findings challenge the prevailing notion that combining multiple omics data types in multi-omics survival prediction improves predictive performance. Thus, the widespread approach in multi-omics prediction of incorporating as many data types as possible should be reconsidered to avoid suboptimal prediction results and unnecessary expenditure.

Multimodal Transformers and Their Applications in Drug Target Discovery for Aging and Age-Related Diseases.

Given the unprecedented rate of global aging, advancing aging research and drug discovery to support healthy and productive longevity is a pressing socioeconomic need. Holistic models of human and population aging that account for biomedical background, environmental context, and lifestyle choices are fundamental to address these needs, but integration of diverse data sources and large data sets into comprehensive models is challenging using traditional approaches. Recent advances in artificial intelligence and machine learning, and specifically multimodal transformer-based neural networks, have enabled the development of highly capable systems that can generalize across multiple data types. As such, multimodal transformers can generate systemic models of aging that can predict health status and disease risks, identify drivers, or breaks of physiological aging, and aid in target discovery against age-related disease. The unprecedented capacity of transformers to extract and integrate information from large and diverse data modalities, combined with the ever-increasing availability of biological and medical data, has the potential to revolutionize healthcare, promoting healthy longevity and mitigating the societal and economic impacts of global aging.

Comments: Celebrating a Legacy: 100 Years of Innovation and 75 Years of JPT at the 2024 ATCE

_ Are you ready to join us for SPE’s Annual Technical Conference and Exhibition (ATCE) in New Orleans, 23–25 September? As SPE celebrates a century of innovation at the 2024 ATCE, we also mark the 75th anniversary of the Journal of Petroleum Technology (JPT). These twin milestones stand as a testament to the achievements that have driven our field forward—innovation, pushing boundaries, setting new standards, and fueling the energy that powers the world. Through the highs of technological breakthroughs and the lows of economic challenges, the oil and gas industry—and ATCE and JPT—have continually evolved, proving their resilience and adaptability. When considering the innovations in our industry, key advancements that often stand out include hydraulic fracturing, horizontal drilling, deepwater exploration, enhanced oil recovery, digitalization and automation, LNG technology, FPSOs, subsea systems, and decarbonization and emissions mitigation. However, the incremental steps that collectively contributed to the broadly defined “wow” factors above are equally notable. To mention only a sampling: enhanced drill-bit designs, advancements in onshore and offshore drilling rigs, real-time data collection, development of 4D seismic technology, integration of multiple data types (e.g., seismic, well logs, production data) for more-comprehensive reservoir characterization, materials-science advancements (polymers, nanocoatings), development of blowout preventers, improved leak-detection systems, and advancements in produced-water treatment and recycling technologies. Less often recognized are the failures that were essential prerequisites for eventual success. The "fail fast, fail often" approach allowed for rapid feedback and necessary adjustments, though it also contributed to the downfall of some startups over the years. As individuals, embracing failure and viewing it as a stepping stone to success is not always our natural response. Presentations at industry events on "lessons learned" vary in transparency, often influenced by differing definitions of failure, stakeholder sensitivities, and legal considerations. As a result, many of the hard knocks that paved the way for the industry's "wow" factors remain largely unknown. ATCE’s theme, “Powering the Future of Energy with a Century of Innovation,” honors the legacy of past breakthroughs while celebrating their ongoing role in shaping a resilient and sustainable future. Olivier Houzé will be named 2025 SPE President with the official passing of the baton from 2024 President Terry Palisch at ATCE. In this issue of JPT, Houzé shares his insights on the challenges facing our Society and its members in his inaugural vodcast and column, “A Roadmap for a Very Busy Year.” Visit the SPE Pavilion (Booth 1449) in the exhibition hall for a variety of SPE Pavilion Talks, including the JPT editors on Tuesday, 24 September, at 1:30 p.m. We’ll highlight the milestones of JPT’s coverage over 75 years and their significance for the future. Visit JPT to learn more about our commemorative features written to date about topics ranging from the Kurdistan Region of Iraq, Azerbaijan, the HSE journey, data science, artificial lift, and a closer look at the past 25 years of innovations in fracturing, drilling, and reservoir engineering. New This Year at ATCE - Strategic Panels will offer a platform for panelists to discuss the current and future challenges and opportunities facing the energy industry. - As an SPE member, you get a complimentary pass to the exhibition hall. If you are not a member yet, you can join now. This pass does not include access to the technical program or conference proceedings. Learn more about the free access pass here. - Non-members registered at the full conference rate will receive a post-show email with a complimentary 1-year SPE membership offer. This offer is valid for new members only, and individuals must meet membership qualifications to join. Whether you’re a seasoned professional or new to the field, ATCE offers an opportunity to connect, learn, and share. Here's to a century of ingenuity and three-quarters of a century of chronicling the journey!

Leveraging multiple data types for improved compound-kinase bioactivity prediction

Machine learning provides efficient ways to map compound-kinase interactions. However, diverse bioactivity data types, including single-dose and multi-dose-response assay results, present challenges. Traditional models utilize only multi-dose data, overlooking information contained in single-dose measurements. Here, we propose a machine learning methodology for compound-kinase activity prediction that leverages both single-dose and dose-response data. We demonstrate that our two-stage approach yields accurate activity predictions and significantly improves model performance compared to training solely on dose-response labels. This superior performance is consistent across five diverse machine learning methods. Using the best performing model, we carried out extensive experimental profiling on a total of 347 selected compound-kinase pairs, achieving a high hit rate of 40% and a negative predictive value of 78%. We show that these rates can be improved further by incorporating model uncertainty estimates into the compound selection process. By integrating multiple activity data types, we demonstrate that our approach holds promise for facilitating the development of training activity datasets in a more efficient and cost-effective way.

Association of Genetically Predicted Skipping of COL4A4 Exon 27 with Hematuria and Albuminuria.

Hematuria is an established sign of glomerular disease and can be associated with kidney failure, but there has been limited scientific study of this trait. Here, we combined genetic data from the UK Biobank with predicted gene expression and splicing from GTEx kidney cortex samples (n = 65) in a transcriptome-wide association study (TWAS) to identify additional potential biological mechanisms influencing hematuria. The TWAS using kidney cortex identified significant associations for 5 genes in terms of expression and 3 significant splicing events. Notably, we identified an association between the skipping of COL4A4 exon 27, which is genetically predicted by intronic rs11898094 (minor allele frequency 13%), and hematuria. Association between this variant was also found with urinary albumin excretion. We found independent evidence supporting the same variant predicting this skipping event in glomeruli-derived mRNA transcriptomics data (n = 245) from NEPTUNE. The functional significance of loss of exon 27 was demonstrated using the split NanoLuc-based α3α4α5(IV) heterotrimer assay, in which type IV collagen heterotrimer formation was quantified by luminescence. The causal splicing variant for this skipping event is yet to be identified. In summary, by integrating multiple data types, we identify a potential splicing event associated with hematuria and albuminuria.

Smccnet 2.0: a comprehensive tool for multi-omics network inference with shiny visualization

SummarySparse multiple canonical correlation network analysis (SmCCNet) is a machine learning technique for integrating omics data along with a variable of interest (e.g., phenotype of complex disease), and reconstructing multi-omics networks that are specific to this variable. We present the second-generation SmCCNet (SmCCNet 2.0) that adeptly integrates single or multiple omics data types along with a quantitative or binary phenotype of interest. In addition, this new package offers a streamlined setup process that can be configured manually or automatically, ensuring a flexible and user-friendly experience.AvailabilityThis package is available in both CRAN: https://cran.r-project.org/web/packages/SmCCNet/index.html and Github: https://github.com/KechrisLab/SmCCNet under the MIT license. The network visualization tool is available at https://smccnet.shinyapps.io/smccnetnetwork/.

Artificial intelligence in individualized retinal disease management.

Owing to the rapid development of modern computer technologies, artificial intelligence (AI) has emerged as an essential instrument for intelligent analysis across a range of fields. AI has been proven to be highly effective in ophthalmology, where it is frequently used for identifying, diagnosing, and typing retinal diseases. An increasing number of researchers have begun to comprehensively map patients' retinal diseases using AI, which has made individualized clinical prediction and treatment possible. These include prognostic improvement, risk prediction, progression assessment, and interventional therapies for retinal diseases. Researchers have used a range of input data methods to increase the accuracy and dependability of the results, including the use of tabular, textual, or image-based input data. They also combined the analyses of multiple types of input data. To give ophthalmologists access to precise, individualized, and high-quality treatment strategies that will further optimize treatment outcomes, this review summarizes the latest findings in AI research related to the prediction and guidance of clinical diagnosis and treatment of retinal diseases.

What was the source of the nonseismic tsunami that occurred in Toyama Bay during the 2024 Noto Peninsula earthquake

In recent years, there have been worldwide reports of massive tsunamis, drawing attention to how tsunamis are intensified by submarine landslides triggered by earthquakes. However, precise data on tsunamis caused by submarine landslides are scarce, leading to insufficient information for a thorough discussion of the characteristics of such tsunamis. On the other hand, during the Noto Peninsula earthquake (Mw7.5) that occurred in Japan on January 1, 2024, a nonseismic tsunami distinct from those originating from fault ruptures were observed. To investigate its characteristics, we analyzed tide/wave gauge records, video footage, and tsunami trace heights along the coast of Toyama Bay. Furthermore, we validated scenarios capable of reproducing the observed records using an integrated landslide–tsunami model. It was found that assuming the existence of 5 submarine landslides along the underwater canyons of Toyama Bay enabled the precise explanation of multiple types of data. Additionally, our study revealed that submarine landslides occurred approximately 50 s after the earthquake, coinciding with the peak ground shaking in Toyama Bay. Compared to the seismic tsunami originating solely from the Noto Peninsula offshore fault rupture, the subsequent tsunami triggered by submarine landslides amplified the tsunami height by approximately 30% along Toyama Bay.

The Role of Artificial Intelligence in Treatment and Diagnosis in Healthcare

Technology, specifically artificial intelligence (AI) is gradually but progressively creeping into the health sector and it’s perhaps the one that has been revolutionised most in diagnosis and treatment. This review brings out discussions on the practices of AI technologies in medical, the pros and the cons. First of all, an endeavour is made to elucidate the meaning of the term AI and its utilization in the field of healthcare. The specific AI techniques are described comprehensively focusing on the machine learning, deep learning, and natural language processing methods to be used in the project The role of multiple types of data in AI includes the EHR, medical images, and genomics data. Self-diagnosis: AI is improving the diagnosis approaches in the radiology and pathology fields and predicting the early-stage disease with better results in most of the cases, and enhancing the identification of genetic diseases. As for treatment, the enhancement of the use of AI has had an impact on issues such as; Prescribing and recommending drugs according to the characteristics of the patients, smart drug administration and management, robotic surgeries and simulations. Discussions are made using concrete and successful implementation of AI in cancer, cardiovascular, neurological and infectious diseases for the purpose of elucidating particular results. This also has to do with the ethical and legal problems like who has the liability to determine in the instance of complicated problems, patients’ information discretion, data privacy, and other legalities. In this article, we briefly mention the prosaic matters of AI, which deals with the engineering aspects of establishing AI such as the aspect of data and the ways and means of checking them and the interdisciplinary character of it. Concerning future developments, additional technologies like AI and connected devices in the field of health care, interdisciplinary at national and international level as well as data sharing is emphasized. Thus. AI has a very great perspective in healthcare, particularly in diagnostics and treatment of diseases due to the probability of increasing the level of accuracy, efficacy, and personalization. Despite these, they are tangible objectives with major challenges and require cooperation between nations with proper handling of Artificial Intelligence to practice clinical medication.