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    Data from University of Michigan Update Knowledge in Artificial Intelligence (Ef fect of Ambient Voice Technology, Natural Language Processing, and Artificial In telligence on the Patient-Physician Relationship)

    67-67页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on artificial intelligenc e is the subject of a new report. According to news reporting out of Wyoming, Un ited States, by NewsRx editors, research stated, "The method of documentation du ring a clinical encounter may affect the patient-physician relationship. Evaluat e how the use of ambient voice recognition, coupled with natural language proces sing and artificial intelligence (DAX™ affects the patient-physician relationsh ip is not known." The news correspondents obtained a quote from the research from University of Mi chigan: "A prospective observational study within a community teaching health sy stem. The primary aim was evaluating any difference on the PDQR-9 scale between primary care encounters in which DAX™was utilized for documentation as compared to those that did not. A signal arm open-label phase was also performed to quer y direct feedback from patients. A total of 288 patients were include in the ope n-label arm and 304 patients were included in the masked phase of the study comp aring encounters with and without DAX™use. In the open label phase patients str ongly agreed that the provider was more focused on them, spent less time typing and made the encounter feel more personable. In the masked phase of the study no difference was seen in the rank order of the total PDQR-9 score between patient s whose encounters used DAX™(median 45 [IQR 8] ) and those which did not (median 45 [IQR 3.5] ; p=0.31). The adjusted odds ratio for DAX™use was 0.8 (95% CI 0. 48-1.34) for the patient reporting complete satisfaction on how well their clini cian listened to them during their encounter."
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    General Hospital of Northern Theater Command Reports Findings in Artificial Inte lligence (Integrated multi-omics and artificial intelligence to explore new neut rophils clusters and potential biomarkers in sepsis with experimental validation )

    68-69页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news originating from Liaoning, Peopl e's Republic of China, by NewsRx correspondents, research stated, "Sepsis, causi ng serious organ and tissue damage and even death, has not been fully elucidated . Therefore, understanding the key mechanisms underlying sepsis-associated immun e responses would lead to more potential therapeutic strategies." Our news journalists obtained a quote from the research from the General Hospita l of Northern Theater Command, "Single-cell RNA data of 4 sepsis patients and 2 healthy controls in the GSE167363 data set were studied. The pseudotemporal traj ectory analyzed neutrophil clusters under sepsis. Using the hdWGCNA method, key gene modules of neutrophils were explored. Multiple machine learning methods wer e used to screen and validate hub genes for neutrophils. SCENIC was then used to explore transcription factors regulating hub genes. Finally, quantitative rever se transcription-polymerase chain reaction was to validate mRNA expression of hu b genes in peripheral blood neutrophils of two mice sepsis models. We discovered two novel neutrophil subtypes with a significant increase under sepsis. These t wo neutrophil subtypes were enriched in the late state during neutrophils differ entiation. The hdWGCNA analysis of neutrophils unveiled that 3 distinct modules (Turquoise, brown, and blue modules) were closely correlated with two neutrophil subtypes. 8 machine learning methods revealed 8 hub genes with high accuracy an d robustness (ALPL, ACTB, CD177, GAPDH, SLC25A37, S100A8, S100A9, and STXBP2). T he SCENIC analysis revealed that APLP, CD177, GAPDH, S100A9, and STXBP2 were sig nificant associated with various transcriptional factors. Finally, ALPL, CD177, S100A8, S100A9, and STXBP2 significantly up regulated in peripheral blood neutro phils of CLP and LPS-induced sepsis mice models. Our research discovered new clu sters of neutrophils in sepsis."
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    Military Institute of Medicine Reports Findings in Mesenchymal Stem Cells (Adipo se-Derived Mesenchymal Stem Cells' adipogenesis chemistry analyzed by FTIR and R aman metrics)

    69-69页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Stem Cell Research-M esenchymal Stem Cells is the subject of a report. According to news reporting ou t of Warszawa, Poland, by NewsRx editors, research stated, "The full understandi ng of molecular mechanisms of cell differentiation requires a holistic view. Her e we combine label-free FTIR and Raman hyperspectral imaging with data mining to detect the molecular cell composition enabling noninvasive monitoring of cell d ifferentiation and identifying biochemical heterogeneity." Our news journalists obtained a quote from the research from the Military Instit ute of Medicine, "Mouse adipose-derived mesenchymal stem cells (AD-MSCs) undergo ing adipogenesis were followed by Raman and FT-IR imaging, Oil Red, and immunofl uorescence. A workflow of the data analysis (IRRSmetrics4stem) was designed to i dentify spectral predictors of adipogenesis and test machine-learning (ML) metho ds (hierarchical clustering, PCA, PLSR) for the control of the AD-MSCs different iation degree. IRRSmetrics4stem provided insights into the chemism of adipogenes is. With single-cell tracking, we established IRRS metrics for lipids, proteins, and DNA variations during AD-MSCs differentiation. The over 90% p redictive efficiency of the selected ML methods proved the high sensitivity of t he IRRS metrics. Importantly, the IRRS metrics unequivocally recognize a switch from proliferation to differentiation."
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    Studies from Beijing Institute of Technology Further Understanding of Machine Le arning (State of Health Analysis of Batteries At Different Stages Based On Real- world Vehicle Data and Machine Learning)

    70-70页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing have been published. According to news reporting originating from Beijing, P eople's Republic of China, by NewsRx correspondents, research stated, "The capac ity and performance of batteries decay over time. How to deal with retired batte ries is a major challenge at present." Funders for this research include Jilin Scientific and Technological Development Program, National Natural Science Foundation of China (NSFC), Beijing Municipal Science & Technology Commission, Opening Foundation of Key Labora tory of Advanced Manufacture Technology for Automobile Parts, Ministry of Educat ion, Fundamental Research Funds for the Central Universities. Our news editors obtained a quote from the research from the Beijing Institute o f Technology, "This study proposes a health state assessment method for retired batteries. The Forgetting Factor Recursive Least Square is used for parameter id entification based on the operation data of new energy vehicles at different mil eage periods. Ohmic internal resistance is extracted and used as a characteristi c parameter to characterize the state of health. The internal resistances of the vehicles at different driving cycles are compared and their variations are deri ved. Parameters highly correlated with the battery ohmic internal resistance are selected as input parameters for the long and short-term memory neural network. The accurate state of health prediction model is obtained after parameter tunin g. The root-mean-square error of the predicted results is less than 0.01 Omega. This shows that the proposed method can effectively assess the power battery sta te of health."
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    University Hospital Careggi Reports Findings in Gastrectomy [Surgical Techniques and Related Perioperative Outcomes After Robotassisted Mini mally Invasive Gastrectomy (RAMIG): Results From the Prospective Multicenter Int ernational Ugira Gastric ...]

    71-72页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Surgery-Gastrectomy is the subject of a report. According to news reporting from Florence, Italy, by NewsRx journalists, research stated, "To gain insight into the global practice of robot-assisted minimally invasive gastrectomy (RAMIG) and evaluate perioperat ive outcomes using an international registry. The techniques and perioperative o utcomes of RAMIG for gastric cancer vary substantially in the literature." The news correspondents obtained a quote from the research from University Hospi tal Careggi, "Prospectively registered RAMIG cases for gastric cancer ( 10 per c enter) were extracted from 25 centers in Europe, Asia, and South-America. Techni ques for resection, reconstruction, anastomosis, and lymphadenectomy were analyz ed and related to perioperative surgical and oncological outcomes. Complications were uniformly defined by the Gastrectomy Complications Consensus Group. Betwee n 2020 and 2023, 759 patients underwent total (n=272), distal (n=465), or proxim al (n=22) gastrectomy (RAMIG). After total gastrectomy with Roux-en-Y-reconstruc tion, anastomotic leakage rates were 8% with handsewn (n=9/111) a nd 6% with linear stapled anastomoses (n=6/100). After distal gast rectomy with Roux-en-Y (67%) or Billroth-II-reconstruction (31% ), anastomotic leakage rates were 3% with linear stapled (n=11/433 ) and 0% with hand-sewn anastomoses (n=0/26). Extent of lymphadene ctomy consisted of D1+ (28%), D2 (59%), or D2+ (12% ). Median nodal harvest yielded 31 nodes (interquartile range: 21-47) after tota l and 34 nodes (interquartile range: 24-47) after distal gastrectomy. R0 resecti on rates were 93% after total and 96% distal gastrec tomy. The hospital stay was 9 days after total and distal gastrectomy, and was m edian 3 days shorter without perianastomotic drains versus routine drain placeme nt. Postoperative 30-day mortality was 1%. This large multicenter s tudy provided a worldwide overview of current RAMIG techniques and their respect ive perioperative outcomes."
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    Recent Findings from University of Bayreuth Provides New Insights into Artificia l Intelligence (How Artificial Intelligence Challenges Tailorable Technology Des ign)

    72-73页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on Artificial Intelligence is now available. According to news reporting from Bayreuth, Germany, by NewsRx journalists, research stated, "Artificial intelligence (AI) has significantly a dvanced healthcare and created unprecedented opportunities to enhance patient-ce nteredness and empowerment. This progress promotes individualized medicine, wher e treatment and care are tailored to each patient's unique needs and characteris tics." Financial supporters for this research include Universitt Bayreuth (3145), Ethic s Committee of the University of Bayreuth. The news correspondents obtained a quote from the research from the University o f Bayreuth, "The Theory of Tailorable Technology Design has considerable potenti al to contribute to individualized medicine as it focuses on information systems (IS) that users can modify and redesign in the context of use. While the theory accounts for both the designer and user perspectives in the lifecycle of an IS, it does not reflect the inductive learning and autonomy of AI throughout the ta iloring process. Therefore, this study posits the conjecture that current knowle dge about tailorable technology design does not effectively account for IS that incorporate AI. To investigate this conjecture and challenge the Theory of Tailo rable Technology Design, a revelatory design study of an AI-enabled individual I S in the domain of bladder monitoring is conducted. Based on the empirical evide nce from the design study, the primary contribution of this work lies in three p ropositions for the design of tailorable technology, culminating in a Revised Th eory of Tailorable Technology Design. As the outcome of the design study, the se condary contribution of this work is concrete design knowledge for AI-enabled in dividualized bladder monitoring systems that empower patients with neurogenic lo wer urinary tract dysfunction (NLUTD)."
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    University of Massachusetts Chan Medical School Reports Findings in Machine Lear ning (Future of neurocritical care: Integrating neurophysics, multimodal monitor ing, and machine learning)

    73-73页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Machine Learning is the subject o f a report. According to news reporting from Worcester, Massachusetts, by NewsRx editors, the research stated, "Multimodal monitoring (MMM) in the intensive car e unit (ICU) has become increasingly sophisticated with the integration of neuro physical principles. However, the challenge remains to select and interpret the most appropriate combination of neuromonitoring modalities to optimize patient o utcomes." The news correspondents obtained a quote from the research from the University o f Massachusetts Chan Medical School, "This manuscript reviewed current neuromoni toring tools, focusing on intracranial pressure, cerebral electrical activity, m etabolism, and invasive and noninvasive autoregulation monitoring. In addition, the integration of advanced machine learning and data science tools within the I CU were discussed. Invasive monitoring includes analysis of intracranial pressur e waveforms, jugular venous oximetry, monitoring of brain tissue oxygenation, th ermal diffusion flowmetry, electrocorticography, depth electroencephalography, a nd cerebral microdialysis. Noninvasive measures include transcranial Doppler, ty mpanic membrane displacement, near-infrared spectroscopy, optic nerve sheath dia meter, positron emission tomography, and systemic hemodynamic monitoring includi ng heart rate variability analysis. The neurophysical basis and clinical relevan ce of each method within the ICU setting were examined. Machine learning algorit hms have shown promise by helping to analyze and interpret data in real time fro m continuous MMM tools, helping clinicians make more accurate and timely decisio ns. These algorithms can integrate diverse data streams to generate predictive m odels for patient outcomes and optimize treatment strategies. MMM, grounded in n europhysics, offers a more nuanced understanding of cerebral physiology and dise ase in the ICU."
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    Investigators at Hunan University Discuss Findings in Machine Learning (Identify ing Systemic Risk Drivers of Fintech and Traditional Financial Institutions: Mac hine Learning-based Prediction and Interpretation)

    74-74页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing have been published. According to news reporting out of Changsha, People's R epublic of China, by NewsRx editors, research stated, "We study systemic risk dr ivers of FinTech and traditional financial institutions under normal and extreme market conditions." Financial supporters for this research include Huxiang Youth Talent Support Prog ram, National Natural Science Foundation of China (NSFC), National Social Scienc e Fund of China, Natural Science Foundation of Hunan Province. Our news journalists obtained a quote from the research from Hunan University, " We use machine learning (ML) techniques (i.e. random forest and gradient boosted regression trees) to evaluate the role of macroeconomic variables, firm charact eristics, and network topologies as systemic risk drivers and perform the ML-bas ed interpretation by Shapley individual and interaction values. We find that (i) the feature importance in driving systemic risk depends on market conditions; n amely, market volatility (MVOL), individual stock volatility (IVOL), and market capitalization (MC) are positive drivers of systemic risk under extreme (downsid e and upside) market conditions, while under normal market conditions, instituti ons with high price-earnings ratio, large MC, and low IVOL play an essential rol e in stabilizing markets; (ii) macroeconomic variables are the most important ex treme systemic risk drivers, while firm characteristics are more important under normal market conditions; and (iii) the interaction between IVOL and MC or MVOL is the significant source of extreme systemic risk, and MC is the most crucial interaction attribute under normal market conditions."
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    China Pharmaceutical University Reports Findings in Tissue Engineering (Amino ac id metabolomics and machine learning for assessment of post-hepatectomy liver re generation)

    75-76页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Biomedical Engineering-Tissue Engineering is the subject of a report. According to news reporting ou t of Nanjing, People's Republic of China, by NewsRx editors, research stated, "A mino acid (AA) metabolism plays a vital role in liver regeneration. However, its measuring utility for post-hepatectomy liver regeneration under different condi tions remains unclear." Our news journalists obtained a quote from the research from China Pharmaceutica l University, "We aimed to combine machine learning (ML) models with AA metabolo mics to assess liver regeneration in health and non-alcoholic steatohepatitis (N ASH). The liver index (liver weight/body weight) was calculated following 70% hepatectomy in healthy and NASH mice. The serum levels of 39 amino acids were me asured using ultra-high performance liquid chromatography-tandem mass spectromet ry analysis. We used orthogonal partial least squares discriminant analysis to d etermine differential AAs and disturbed metabolic pathways during liver regenera tion. The SHapley Additive exPlanations algorithm was performed to identify pote ntial AA signatures, and five ML models including least absolute shrinkage and s election operator, random forest, K-nearest neighbor (KNN), support vector regre ssion, and extreme gradient boosting were utilized to assess the liver index. El even and twenty-two differential AAs were identified in the healthy and NASH gro ups, respectively. Among these metabolites, arginine and proline metabolism were commonly disturbed metabolic pathways related to liver regeneration in both gro ups. Five AA signatures were identified, including hydroxylysine, L-serine, 3-me thylhistidine, L-tyrosine, and homocitrulline in healthy group, and L-arginine, 2-aminobutyric acid, sarcosine, beta-alanine, and L-cysteine in NASH group. The KNN model demonstrated the best evaluation performance with mean absolute error, root mean square error, and coefficient of determination values of 0.0037, 0.00 47, 0.79 and 0.0028, 0.0034, 0.71 for the healthy and NASH groups, respectively. "
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    Studies Conducted at Institute of Mathematics and Computer Science on Artificial Intelligence Recently Published [EXplainable Artificial Inte lligence (XAI)-From Theory to Methods and Applications]

    76-76页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intell igence are discussed in a new report. According to news originating from the Ins titute of Mathematics and Computer Science by NewsRx correspondents, research st ated, "Intelligent applications supported by Machine Learning have achieved rema rkable performance rates for a wide range of tasks in many domains. However, und erstanding why a trained algorithm makes a particular decision remains problemat ic." Financial supporters for this research include Coordenacao De Aperfeicoamento De Pessoal De Nivel Superior-brasil (Capes)-finance Code 001; Sao Paulo Research F oundation; National Council For Scientific And Technological Development; Fapesp ; Cnpq. The news journalists obtained a quote from the research from Institute of Mathem atics and Computer Science: "Given the growing interest in the application of le arning-based models, some concerns arise in the dealing with sensible environmen ts, which may impact users' lives. The complex nature of those models' decision mechanisms makes them the so-called 'black boxes,' in which the understanding of the logic behind automated decision-making processes by humans is not trivial. Furthermore, the reasoning that leads a model to provide a specific prediction c an be more important than performance metrics, which introduces a trade-off betw een interpretability and model accuracy. Explaining intelligent computer decisio ns can be regarded as a way to justify their reliability and establish trust. In this sense, explanations are critical tools that verify predictions to discover errors and biases previously hidden within the models' complex structures, open ing up vast possibilities for more responsible applications. In this review, we provide theoretical foundations of Explainable Artificial Intelligence (XAI), cl arifying diffuse definitions and identifying research objectives, challenges, an d future research lines related to turning opaque machine learning outputs into more transparent decisions."
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