Prediction of ciprofloxacin resistance in hospitalized patients using machine learning | Communications Medicine – Nature.com
Smith, R. A., Mikanatha, N. M. & Read, A. F. Antibiotic resistance: A primer and call to action. Health Commun 30, 309314 (2015).
Article PubMed Google Scholar
Palumbi, S. R. Humans as the worlds greatest evolutionary force. Science 293, 17861790 (2001).
Article CAS PubMed Google Scholar
Weber, D. J. Collateral damage and what the future might hold. The need to balance prudent antibiotic utilization and stewardship with effective patient management. Int. J. Infect. Dis. 10, S17S24 (2006).
Article CAS Google Scholar
Carrara, E., Pfeffer, I., Zusman, O., Leibovici, L. & Paul, M. Determinants of inappropriate empirical antibiotic treatment: systematic review and meta-analysis. Int. J. Antimicrob. Agents 51, 548553 (2018).
Article CAS PubMed Google Scholar
World Health Organization. Executive summary: the selection and use of essential medicines 2019: report of the 22nd WHO Expert Committee on the selection and use of essential medicines: WHO Headquarters, Geneva, 1-5 April 2019. https://apps.who.int/iris/handle/10665/325773 (2019).
Chowers, M. et al. Estimating the impact of cefuroxime versus cefazolin and amoxicillin/clavulanate use on future collateral resistance: a retrospective comparison. J. Antimicrob. Chemother 77, 19921995 (2022).
Article CAS PubMed Google Scholar
Nathwani, D. et al. Value of hospital antimicrobial stewardship programs [ASPs]: a systematic review. Antimicrob. Resist. Infect. Control 8, 113 (2019).
Article Google Scholar
Tribble, A. C. et al. Appropriateness of antibiotic prescribing in United States childrens hospitals: a national point prevalence survey. Clin. Infect. Dis 71, e226e234 (2020).
Article PubMed Google Scholar
eEML - Electronic Essential Medicines List. https://list.essentialmeds.org/.
Loscalzo, J. et al. Harrisons Principles of Internal Medicine, (Vol. 1 & Vol. 2). (McGraw Hill Professional, 2022).
Sharma, P. C., Jain, A., Jain, S., Pahwa, R. & Yar, M. S. Ciprofloxacin: review on developments in synthetic, analytical, and medicinal aspects. J. Enzyme Inhib. Med. Chem. 25, 577589 (2010).
Article CAS PubMed Google Scholar
Thomson, C. J. The global epidemiology of resistance to ciprofloxacin and the changing nature of antibiotic resistance: a 10 year perspective. J. Antimicrob. Chemother. 43, 3140 (1999).
Article CAS PubMed Google Scholar
Organization, W. H. Global antimicrobial resistance and use surveillance system (GLASS) report: 2021. (2021).
Dalhoff, A. Global fluoroquinolone resistance epidemiology and implictions for clinical use. Interdiscip. Perspect. Infect. Dis. 2012, 976273 (2012).
Article PubMed PubMed Central Google Scholar
Low, M. et al. Association between urinary community-acquired fluoroquinolone-resistant Escherichia coli and neighbourhood antibiotic consumption: a population-based case-control study. Lancet Infect. Dis. 19, 419428 (2019).
Article CAS PubMed Google Scholar
Eliopoulos, G. M., Cosgrove, S. E. & Carmeli, Y. The impact of antimicrobial resistance on health and economic outcomes. Clin. Infect. Dis 36, 14331437 (2003).
Article Google Scholar
Gottesman, B. S., Carmeli, Y., Shitrit, P. & Chowers, M. Impact of quinolone restriction on resistance patterns of Escherichia coli isolated from urine by culture in a community setting. Clin. Infect. Dis. 49, 869875 (2009).
Article CAS PubMed Google Scholar
Anahtar, M. N., Yang, J. H. & Kanjilal, S. Applications of machine learning to the problem of antimicrobial resistance: an emerging model for translational research. J. Clin. Microbiol. 59, e0126020 (2021).
Article CAS PubMed PubMed Central Google Scholar
Rawson, T. M., Ahmad, R., Toumazou, C., Georgiou, P. & Holmes, A. H. Artificial intelligence can improve decision-making in infection management. Nat. Hum. Behav. 3, 543545 (2019).
Article PubMed Google Scholar
Yelin, I. et al. Personal clinical history predicts antibiotic resistance of urinary tract infections. Nat. Med. 25, 11431152 (2019).
Article CAS PubMed PubMed Central Google Scholar
Feretzakis, G. et al. Using machine learning techniques to aid empirical antibiotic therapy decisions in the intensive care unit of a general hospital in Greece. Antibiotics 9, 50 (2020).
Article CAS PubMed PubMed Central Google Scholar
Dan, S. et al. Prediction of fluoroquinolone resistance in gram-negative bacteria causing bloodstream infections. Antimicrob. Agents Chemother. 60, 22652272 (2016).
Article CAS PubMed PubMed Central Google Scholar
Dickstein, Y., Geffen, Y., Andreassen, S., Leibovici, L. & Paul, M. Predicting antibiotic resistance in urinary tract infection patients with prior urine cultures. Antimicrob. Agents Chemother. 60, 47174721 (2016).
Article CAS PubMed PubMed Central Google Scholar
Binuya, M. A. E., Engelhardt, E. G., Schats, W., Schmidt, M. K. & Steyerberg, E. W. Methodological guidance for the evaluation and updating of clinical prediction models: a systematic review. BMC Med. Res. Methodol. 22, 114 (2022).
Article Google Scholar
Staffa, S. J. & Zurakowski, D. Statistical development and validation of clinical prediction models. Anesthesiology 135, 396405 (2021).
Article PubMed Google Scholar
de Hond, A. A. et al. Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review. Npj Digit. Med. 5, 113 (2022).
Google Scholar
Debray, T. P. et al. A new framework to enhance the interpretation of external validation studies of clinical prediction models. J. Clin. Epidemiol. 68, 279289 (2015).
Article PubMed Google Scholar
Eilers, P. H. C., Boer, J. M., van Ommen G. J. & van Houwelingen, H. C. Classification of microarray data with penalized logistic regression. in Microarrays: Optical Technologies and Informatics vol. 4266 187198 (International Society for Optics and Photonics, 2001).
Friedman, J., Hastie, T. & Tibshirani, R. The Elements of Statistical Learning. vol. 1 (Springer series in statistics New York, 2001).
Bergstra, J. & Bengio, Y. Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281305 (2012).
Google Scholar
Sill, J., Takcs, G., Mackey, L. & Lin, D. Feature-weighted linear stacking. ArXiv Prepr. arXiv:0911.0460 (2009).
Van der Laan, M. J., Polley, E. C. & Hubbard, A. E. Super learner. Stat. Appl. Genet. Mol. Biol. 6 (2007).
Lundberg, S. M. & Lee, S.-I. A Unified Approach to Interpreting Model Predictions. in Advances in Neural Information Processing Systems 30 (eds. Guyon, I. et al.) 47654774 (Curran Associates, Inc., 2017).
Vickers, A. J. & Elkin, E. B. Decision curve analysis: a novel method for evaluating prediction models. Med. Decis. Mak. 26, 565574 (2006).
Article Google Scholar
Kerr, K. F., Brown, M. D., Zhu, K. & Janes, H. Assessing the clinical impact of risk prediction models with decision curves: guidance for correct interpretation and appropriate use. J. Clin. Oncol. 34, 2534 (2016).
Article PubMed PubMed Central Google Scholar
Python Software Foundation. Python programming language. https://www.python.org/.
NumPy Developers. NumPy: Scientific computing with Python. https://numpy.org/doc/stable/.
Pandas Developers. Pandas: Powerful data structures for data analysis and manipulation. https://pandas.pydata.org/.
Scikit-learn developers. Scikit-learn: Machine learning in Python. https://scikit-learn.org/stable/.
XGBoost: Scalable, distributed gradient boosting. https://xgboost.readthedocs.io/en/latest/.
TensorFlow Developers. TensorFlow: An end-to-end open source machine learning platform. https://www.tensorflow.org/.
Matplotlib: A comprehensive library for static, animated, and interactive visualizations in Python. https://matplotlib.org/stable/.
SHAP Developers. SHAP: A unified approach to explain the output of any machine learning model. https://shap.readthedocs.io/en/latest/.
Gallini, A. et al. Influence of fluoroquinolone consumption in inpatients and outpatients on ciprofloxacin-resistant Escherichia coli in a university hospital. J. Antimicrob. Chemother. 65, 26502657 (2010).
Article CAS PubMed Google Scholar
Wang, T. et al. Predicting Antimicrobial Resistance in the Intensive Care Unit. ArXiv Prepr. ArXiv211103575 (2021).
Wojcik, G. et al. Understanding the complexities of antibiotic prescribing behaviour in acute hospitals: a systematic review and meta-ethnography. Arch. Public Health 79, 119 (2021).
Article Google Scholar
Diamant, M. et al. A game theoretic approach reveals that discretizing clinical information can reduce antibiotic misuse. Nat. Commun. 12, 113 (2021).
Article Google Scholar
Shapley, L. S. A value for n-person games. Contrib. Theory Games 2, 307317 (1953).
Google Scholar
Kumar, I. E., Venkatasubramanian, S., Scheidegger, C. & Friedler, S. Problems with Shapley-value-based explanations as feature importance measures. in International Conference on Machine Learning 54915500 (PMLR, 2020).
Chen, M. et al. Physician and Medical Student Attitudes Toward Clinical Artificial Intelligence: A Systematic Review with Cross-Sectional Survey. Available SSRN 4128867.
Mulder, M. et al. Risk factors for resistance to ciprofloxacin in community-acquired urinary tract infections due to Escherichia coli in an elderly population. J. Antimicrob. Chemother. 72, 281289 (2016).
Article PubMed Google Scholar
Arslan, H., Azap, . K., Ergnl, . & Timurkaynak, F. On behalf of the Urinary Tract Infection Study Group Risk factors for ciprofloxacin resistance among Escherichia coli strains isolated from community-acquired urinary tract infections in Turkey. J. Antimicrob. Chemother. 56, 914918 (2005).
Article CAS PubMed Google Scholar
Beckley, A. M. & Wright, E. S. Identification of antibiotic pairs that evade concurrent resistance via a retrospective analysis of antimicrobial susceptibility test results. Lancet Microbe 2, e545e554 (2021).
Article CAS PubMed PubMed Central Google Scholar
Cherny, S. S., Chowers, M. & Obolski, U. Patterns of antibiotic cross-resistance by bacterial sample source: a retrospective cohort study. medRxiv (2022).
Cherny, S. S. et al. Revealing antibiotic cross-resistance patterns in hospitalized patients through Bayesian network modelling. J. Antimicrob. Chemother 76, 239248 (2021).
Article CAS PubMed Google Scholar
Lewin-Epstein, O., Baruch, S., Hadany, L., Stein, G. & Obolski, U. Predicting antibiotic resistance in hospitalized patients by applying machine learning to electronic medical records. medRxiv 2020.06.03.20120535 https://doi.org/10.1101/2020.06.03.20120535. (2020)
Chatterjee, A. et al. Quantifying drivers of antibiotic resistance in humans: a systematic review. Lancet Infect. Dis. 18, e368e378 (2018).
Article CAS PubMed Google Scholar
Truong, W. R., Hidayat, L., Bolaris, M. A., Nguyen, L. & Yamaki, J. The antibiogram: Key considerations for its development and utilization. JAC-Antimicrob. Resist. 3, dlab060 (2021).
Article PubMed PubMed Central Google Scholar
Oonsivilai, M. et al. Using machine learning to guide targeted and locally-tailored empiric antibiotic prescribing in a childrens hospital in Cambodia. Wellcome Open Res. 3, 131 (2018).
Article PubMed PubMed Central Google Scholar
Bell, B. G., Schellevis, F., Stobberingh, E., Goossens, H. & Pringle, M. A systematic review and meta-analysis of the effects of antibiotic consumption on antibiotic resistance. BMC Infect. Dis. 14, 125 (2014).
Article Google Scholar
Baraz, A., Chowers, M., Nevo, D. & Obolski, U. Stable temporal relationships as a first step towards causal inference: an application to antibiotic resistance. medRxiv (2022).
Fasugba, O., Gardner, A., Mitchell, B. G. & Mnatzaganian, G. Ciprofloxacin resistance in community-and hospital-acquired Escherichia coli urinary tract infections: a systematic review and meta-analysis of observational studies. BMC Infect. Dis. 15, 116 (2015).
Here is the original post:
Prediction of ciprofloxacin resistance in hospitalized patients using machine learning | Communications Medicine - Nature.com
- Snowflake Supercharges Machine Learning for Enterprises with Native Integration of NVIDIA CUDA-X Libraries - Yahoo Finance - November 18th, 2025 [November 18th, 2025]
- An interpretable machine learning model for predicting 5year survival in breast cancer based on integration of proteomics and clinical data -... - November 18th, 2025 [November 18th, 2025]
- scMFF: a machine learning framework with multiple feature fusion strategies for cell type identification - BMC Bioinformatics - November 18th, 2025 [November 18th, 2025]
- URI professor examines how machine learning can help with depression diagnosis Rhody Today - The University of Rhode Island - November 18th, 2025 [November 18th, 2025]
- Predicting drug solubility in supercritical carbon dioxide green solvent using machine learning models based on thermodynamic properties - Nature - November 18th, 2025 [November 18th, 2025]
- Relationship between C-reactive protein triglyceride glucose index and cardiovascular disease risk: a cross-sectional analysis with machine learning -... - November 18th, 2025 [November 18th, 2025]
- Using machine learning to predict student outcomes for early intervention and formative assessment - Nature - November 18th, 2025 [November 18th, 2025]
- Prevalence, associated factors, and machine learning-based prediction of probable depression among individuals with chronic diseases in Bangladesh -... - November 18th, 2025 [November 18th, 2025]
- Snowflake supercharges machine learning for enterprises with native integration of Nvidia CUDA-X libraries - MarketScreener - November 18th, 2025 [November 18th, 2025]
- Unlocking Cardiovascular Disease Insights Through Machine Learning - BIOENGINEER.ORG - November 18th, 2025 [November 18th, 2025]
- Machine learning boosts solar forecasts in diverse climates of India - researchmatters.in - November 18th, 2025 [November 18th, 2025]
- Big Data Machine Learning In Telecom Market by Type and Application Set for 14.8% CAGR Growth Through 2033 - openPR.com - November 18th, 2025 [November 18th, 2025]
- How Humans Could Soon Understand and Talk to Animals, Thanks to Machine Learning - SYFY - November 10th, 2025 [November 10th, 2025]
- Machine learning based analysis of diesel engine performance using FeO nanoadditive in sterculia foetida biodiesel blend - Nature - November 10th, 2025 [November 10th, 2025]
- Machine Learning in Maternal Care - Johns Hopkins Bloomberg School of Public Health - November 10th, 2025 [November 10th, 2025]
- Machine learning-based differentiation of benign and malignant adrenal lesions using 18F-FDG PET/CT: a two-stage classification and SHAP... - November 10th, 2025 [November 10th, 2025]
- How to Better Use AI and Machine Learning in Dermatology, With Renata Block, MMS, PA-C - HCPLive - November 10th, 2025 [November 10th, 2025]
- Avoiding Catastrophe: The Importance of Privacy when Leveraging AI and Machine Learning for Disaster Management - CSIS | Center for Strategic and... - November 10th, 2025 [November 10th, 2025]
- Efferocytosis-related signatures identified via Single-cell analysis and machine learning predict TNBC outcomes and immunotherapy response - Nature - November 10th, 2025 [November 10th, 2025]
- Arc Raiders' use of AI highlights the tension and confusion over where machine learning ends and generative AI begins - PC Gamer - November 3rd, 2025 [November 3rd, 2025]
- From performance to prediction: extracting aging data from the effects of base load aging on washing machines for a machine learning model - Nature - November 3rd, 2025 [November 3rd, 2025]
- Meet 'kvcached': A Machine Learning Library to Enable Virtualized, Elastic KV Cache for LLM Serving on Shared GPUs - MarkTechPost - October 28th, 2025 [October 28th, 2025]
- Bayesian-optimized machine learning boosts actual evapotranspiration prediction in water-stressed agricultural regions of China - Nature - October 28th, 2025 [October 28th, 2025]
- Using machine learning to shed light on how well the triage systems work - News-Medical - October 28th, 2025 [October 28th, 2025]
- Our Last Hope Before The AI Bubble Detonates: Taming LLMs - Machine Learning Week US - October 28th, 2025 [October 28th, 2025]
- Using multiple machine learning algorithms to predict spinal cord injury in patients with cervical spondylosis: a multicenter study - Nature - October 28th, 2025 [October 28th, 2025]
- The diagnostic potential of proteomics and machine learning in Lyme neuroborreliosis - Nature - October 28th, 2025 [October 28th, 2025]
- Using unsupervised machine learning methods to cluster cardio-metabolic profile of the middle-aged and elderly Chinese with general and central... - October 28th, 2025 [October 28th, 2025]
- The prognostic value of POD24 for multiple myeloma: a comprehensive analysis based on traditional statistics and machine learning - BMC Cancer - October 28th, 2025 [October 28th, 2025]
- Reducing inequalities using an unbiased machine learning approach to identify births with the highest risk of preventable neonatal deaths - Population... - October 28th, 2025 [October 28th, 2025]
- Association between SHR and mortality in critically ill patients with CVD: a retrospective analysis and machine learning approach - Diabetology &... - October 28th, 2025 [October 28th, 2025]
- AI-Powered Visual Storytelling: How Machine Learning Transforms Creative Content Production - About Chromebooks - October 28th, 2025 [October 28th, 2025]
- How beauty brand Shiseido nearly tripled revenue per user with machine learning - Performance Marketing World - October 28th, 2025 [October 28th, 2025]
- Magnite introduces machine learning-powered ad podding for streaming platforms - PPC Land - October 26th, 2025 [October 26th, 2025]
- Krafton is an AI first company and will invest 70M USD on machine learning - Female First - October 26th, 2025 [October 26th, 2025]
- Machine learning prediction of bacterial optimal growth temperature from protein domain signatures reveals thermoadaptation mechanisms - BMC Genomics - October 24th, 2025 [October 24th, 2025]
- Data Proportionality and Its Impact on Machine Learning Predictions of Ground Granulated Blast Furnace Slag Concrete Strength | Newswise - Newswise - October 24th, 2025 [October 24th, 2025]
- The Evolution of Machine Learning and Its Applications in Orthopaedics: A Bibliometric Analysis - Cureus - October 24th, 2025 [October 24th, 2025]
- Sentiment Analysis with Machine Learning Achieves 83.48% Accuracy in Predicting Consumer Behavior Trends - Quantum Zeitgeist - October 24th, 2025 [October 24th, 2025]
- Use of machine learning for risk stratification of chest pain patients in the emergency department - BMC Medical Informatics and Decision Making - October 24th, 2025 [October 24th, 2025]
- Mass spectrometry combined with machine learning identifies novel protein signatures as demonstrated with multisystem inflammatory syndrome in... - October 24th, 2025 [October 24th, 2025]
- How Machine Learning Is Shrinking to Fit the Sensor Node - All About Circuits - October 24th, 2025 [October 24th, 2025]
- Machine learning models for mechanical properties prediction of basalt fiber-reinforced concrete incorporating graphical user interface - Nature - October 24th, 2025 [October 24th, 2025]
- Ohio wins national cybersecurity award for fraud solutions using machine learning - Spectrum News NY1 - October 24th, 2025 [October 24th, 2025]
- Itron Partners with Gordian Technologies to Enhance Grid Edge Intelligence with AI and Machine Learning Solutions - Quiver Quantitative - October 24th, 2025 [October 24th, 2025]
- Wearable sensors and machine learning give leg up on better running data - Medical Xpress - October 23rd, 2025 [October 23rd, 2025]
- Geophysical-machine learning tool developed for continuous subsurface geomaterials characterization - Phys.org - October 23rd, 2025 [October 23rd, 2025]
- Ohio wins national cybersecurity award for fraud solutions using machine learning - Spectrum News 1 - October 23rd, 2025 [October 23rd, 2025]
- Machine learning predictions of climate change effects on nearly threatened bird species ( Crithagra xantholaema) habitat in Ethiopia for conservation... - October 23rd, 2025 [October 23rd, 2025]
- A machine learning tool for predicting newly diagnosed osteoporosis in primary healthcare in the Stockholm Region - Nature - October 23rd, 2025 [October 23rd, 2025]
- ECBs New Perspective on Machine Learning in Banking - KPMG - October 23rd, 2025 [October 23rd, 2025]
- Ensemble Machine Learning for Digital Mapping of Soil pH and Electrical Conductivity in the Andean Agroecosystem of Peru - Frontiers - October 21st, 2025 [October 21st, 2025]
- New UA research develops machine learning to address needs of children with autism - AZPM News - October 21st, 2025 [October 21st, 2025]
- NMDSI Speaker Series on Weather Forecasting: What Machine Learning Can and Can't Do, Oct. 23 - Marquette Today - October 21st, 2025 [October 21st, 2025]
- Polyskill Achieves 1.7x Improved Skill Reuse and 9.4% Higher Success Rates through Polymorphic Abstraction in Machine Learning - Quantum Zeitgeist - October 21st, 2025 [October 21st, 2025]
- University of Strathclyde opens admission for MSc in Machine & Deep Learning for Jan 2026 intake - The Indian Express - October 21st, 2025 [October 21st, 2025]
- Reducing Model Biases with Machine Learning Corrections Derived from Ocean Data Assimilation Increments - ESS Open Archive - October 19th, 2025 [October 19th, 2025]
- Unlocking Obesity: Multi-Omics and Machine Learning Insights - Bioengineer.org - October 19th, 2025 [October 19th, 2025]
- Lockheed Martin advances PAC-3 MSE interceptor using artificial intelligence and machine learning - Defence Industry Europe - October 19th, 2025 [October 19th, 2025]
- Semi-automated surveillance of surgical site infections using machine learning and rule-based classification models - Nature - October 19th, 2025 [October 19th, 2025]
- AI and Machine Learning - City of San Jos to release RFP for generative AI platform - Smart Cities World - October 19th, 2025 [October 19th, 2025]
- Machine learning helps identify 'thermal switch' for next-generation nanomaterials - Phys.org - October 17th, 2025 [October 17th, 2025]
- Machine Learning Makes Wildlife Data Analysis Less of a Trek - Maryland.gov - October 17th, 2025 [October 17th, 2025]
- An interpretable multimodal machine learning model for predicting malignancy of thyroid nodules in low-resource scenarios - BMC Endocrine Disorders - October 17th, 2025 [October 17th, 2025]
- In First-Episode Psychosis Patients, Machine Learning Predicted Illness Trajectories to Potentially Improve Outcomes - Brain and Behavior Research - October 17th, 2025 [October 17th, 2025]
- Novel Machine Learning Model Improves MASLD Detection in Type 2 Diabetes - The American Journal of Managed Care (AJMC) - October 17th, 2025 [October 17th, 2025]
- Hybrid machine learning models for predicting the tensile strength of reinforced concrete incorporating nano-engineered and sustainable supplementary... - October 17th, 2025 [October 17th, 2025]
- Modelling of immune infiltration in prostate cancer treated with HDR-brachytherapy using Raman spectroscopy and machine learning - Nature - October 17th, 2025 [October 17th, 2025]
- Association between atherogenic index of plasma and sepsis in critically ill patients with ischemic stroke: a retrospective cohort study using... - October 17th, 2025 [October 17th, 2025]
- AI enters the nuclear age: Pentagon modernizes warheads with machine learning - Washington Times - October 17th, 2025 [October 17th, 2025]
- AI and Machine Learning - Bentley Systems shares its vision for trustworthy AI - Smart Cities World - October 17th, 2025 [October 17th, 2025]
- Looking back to move forward: can historical clinical trial data and machine learning drive change in participant recruitment in anticipation of... - October 15th, 2025 [October 15th, 2025]
- Physics-Based Machine Learning Paves the Way for Advanced 3D-Printed Materials - Bioengineer.org - October 15th, 2025 [October 15th, 2025]
- Predicting one-year overall survival in patients with AITL using machine learning algorithms: a multicenter study - Nature - October 15th, 2025 [October 15th, 2025]
- Explainable machine learning models for predicting of protein-energy wasting in patients on maintenance haemodialysis - BMC Nephrology - October 15th, 2025 [October 15th, 2025]
- Feasibility of machine learning analysis for the identification of patients with possible primary ciliary dyskinesia - Orphanet Journal of Rare... - October 15th, 2025 [October 15th, 2025]
- Machine learning-based prediction of preeclampsia using first-trimester inflammatory markers and red blood cell indices - BMC Pregnancy and Childbirth - October 15th, 2025 [October 15th, 2025]
- Utilizing AI and machine learning to improve railroad safety: Detecting trespasser hotspots - masstransitmag.com - October 15th, 2025 [October 15th, 2025]
- Precision medicine meets machine learning: AI and oncology biomarkers - pharmaphorum - October 15th, 2025 [October 15th, 2025]
- Aether Pro Exchange Transforms Execution Dynamics with Machine-Learning Optimization - GlobeNewswire - October 15th, 2025 [October 15th, 2025]