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
- Predicting land suitability for wheat and barley crops using machine learning techniques - Nature - May 10th, 2025 [May 10th, 2025]
- AI and Machine Learning - Ribeiro Preto adopts Optibus to optimise public bus system - Smart Cities World - May 10th, 2025 [May 10th, 2025]
- Childrens Hospital Los Angeles Leads Development of First Machine Learning Tool to Predict Risk of Cisplatin-Induced Hearing Loss - Business Wire - May 10th, 2025 [May 10th, 2025]
- Google is using machine learning to help Android users avoid unwanted and dangerous notifications - BetaNews - May 10th, 2025 [May 10th, 2025]
- London School of Emerging Technology (LSET) Concludes International Workshop on Emerging AI & Machine Learning Innovation - Barchart.com - May 10th, 2025 [May 10th, 2025]
- Thermal performance, entropy generation, and machine learning insights of AlO-TiO hybrid nanofluids in turbulent flow - Nature - May 10th, 2025 [May 10th, 2025]
- Predicting the efficacy of bevacizumab on peritumoral edema based on imaging features and machine learning - Nature - May 10th, 2025 [May 10th, 2025]
- How AI and machine learning are supercharging video conferencing tools - European CEO - May 10th, 2025 [May 10th, 2025]
- The need for a risk-based approach to AI and machine learning in healthcare - Health Tech World - May 10th, 2025 [May 10th, 2025]
- Integrated bioinformatics, machine learning, and molecular docking reveal crosstalk genes and potential drugs between periodontitis and systemic lupus... - May 10th, 2025 [May 10th, 2025]
- Adversarial Machine Learning in Detecting Inauthentic Behavior on Social Platforms - AiThority - May 10th, 2025 [May 10th, 2025]
- Exploring crop health and its associations with fungal soil microbiome composition using machine learning applied to remote sensing data - Nature - May 10th, 2025 [May 10th, 2025]
- Trust-based model and machine learning improve forest fire detection system - International Fire & Safety Journal - May 10th, 2025 [May 10th, 2025]
- A machine learning engineer shares the rsums that landed her jobs at Meta and X and what she'd change if she applied again - Business Insider Africa - May 5th, 2025 [May 5th, 2025]
- Recentive Analytics v. Fox: The Federal Circuit Provides Analysis on the Patent Eligibility of Machine Learning Claims - Mintz - May 5th, 2025 [May 5th, 2025]
- A machine learning engineer shares the rsums that landed her jobs at Meta and X and what she'd change if she applied again - Business Insider - May 5th, 2025 [May 5th, 2025]
- Enhancing urban resilience through machine learning-supported flood risk assessment: integrating flood susceptibility with building function... - May 5th, 2025 [May 5th, 2025]
- MicroAlgo Inc. Develops Classifier Auto-Optimization Technology Based on Variational Quantum Algorithms, Accelerating the Advancement of Quantum... - May 5th, 2025 [May 5th, 2025]
- Enhanced metal ion adsorption using ZnO-MXene nanocomposites with machine learning-based performance prediction - Nature - May 5th, 2025 [May 5th, 2025]
- Integrating SHAP analysis with machine learning to predict postpartum hemorrhage in vaginal births - BMC Pregnancy and Childbirth - May 5th, 2025 [May 5th, 2025]
- Machine learning provide new insights into how the brain responds to heroin use - News-Medical - May 2nd, 2025 [May 2nd, 2025]
- Machine Learning and AI in Basic HIV Research: From Big Data Analysis to Large Language Models - UNC Gillings School of Global Public Health - May 2nd, 2025 [May 2nd, 2025]
- Machine learning brings new insights to cells role in addiction, relapse - University of Cincinnati - May 2nd, 2025 [May 2nd, 2025]
- UH/UC Researchers Use Machine Learning to Map Brain Changes from Heroin Addiction - University of Houston - May 2nd, 2025 [May 2nd, 2025]
- Machine Learning Algorithm Predicts Shiba Inu Price In May You Should See This - The Crypto Update - May 2nd, 2025 [May 2nd, 2025]
- Seerist partners with SOCOM to enhance AI and machine learning for special operations - Defence Industry Europe - May 2nd, 2025 [May 2nd, 2025]
- How machine learning can spark many discoveries in science and medicine - The Indian Express - April 30th, 2025 [April 30th, 2025]
- Machine learning frameworks to accurately estimate the adsorption of organic materials onto resin and biochar - Nature - April 30th, 2025 [April 30th, 2025]
- Gene Therapy Research Roundup: Gene Circuits and Controlling Capsids With Machine Learning - themedicinemaker.com - April 30th, 2025 [April 30th, 2025]
- Seerist and SOCOM Enter Five-Year CRADA to Advance AI and Machine Learning for Operations - PRWeb - April 30th, 2025 [April 30th, 2025]
- Machine learning models for estimating the overall oil recovery of waterflooding operations in heterogenous reservoirs - Nature - April 30th, 2025 [April 30th, 2025]
- Machine learning-based quantification and separation of emissions and meteorological effects on PM - Nature - April 30th, 2025 [April 30th, 2025]
- Protein interactions, network pharmacology, and machine learning work together to predict genes linked to mitochondrial dysfunction in hypertrophic... - April 30th, 2025 [April 30th, 2025]
- AQR Bets on Machine Learning as Asness Becomes AI Believer - Bloomberg.com - April 30th, 2025 [April 30th, 2025]
- Darktrace enhances Cyber AI Analyst with advanced machine learning for improved threat investigations - Industrial Cyber - April 21st, 2025 [April 21st, 2025]
- Infrared spectroscopy with machine learning detects early wood coating deterioration - Phys.org - April 21st, 2025 [April 21st, 2025]
- A simulation-driven computational framework for adaptive energy-efficient optimization in machine learning-based intrusion detection systems - Nature - April 21st, 2025 [April 21st, 2025]
- Machine learning model to predict the fitness of AAV capsids for gene therapy - EurekAlert! - April 21st, 2025 [April 21st, 2025]
- An integrated approach of feature selection and machine learning for early detection of breast cancer - Nature - April 21st, 2025 [April 21st, 2025]
- Predicting cerebral infarction and transient ischemic attack in healthy individuals and those with dysmetabolism: a machine learning approach combined... - April 21st, 2025 [April 21st, 2025]
- Autolomous CEO Discusses AI and Machine Learning Applications in Pharmaceutical Development and Manufacturing with Pharmaceutical Technology -... - April 21st, 2025 [April 21st, 2025]
- Machine Learning Interpretation of Optical Spectroscopy Using Peak-Sensitive Logistic Regression - ACS Publications - April 21st, 2025 [April 21st, 2025]
- Estimated glucose disposal rate outperforms other insulin resistance surrogates in predicting incident cardiovascular diseases in... - April 21st, 2025 [April 21st, 2025]
- Machine learning-based differentiation of schizophrenia and bipolar disorder using multiscale fuzzy entropy and relative power from resting-state EEG... - April 12th, 2025 [April 12th, 2025]
- Increasing load factor in logistics and evaluating shipment performance with machine learning methods: A case from the automotive industry - Nature - April 12th, 2025 [April 12th, 2025]
- Machine learning-based prediction of the thermal conductivity of filling material incorporating steelmaking slag in a ground heat exchanger system -... - April 12th, 2025 [April 12th, 2025]
- Do LLMs Know Internally When They Follow Instructions? - Apple Machine Learning Research - April 12th, 2025 [April 12th, 2025]
- Leveraging machine learning in precision medicine to unveil organochlorine pesticides as predictive biomarkers for thyroid dysfunction - Nature - April 12th, 2025 [April 12th, 2025]
- Analysis and validation of hub genes for atherosclerosis and AIDS and immune infiltration characteristics based on bioinformatics and machine learning... - April 12th, 2025 [April 12th, 2025]
- AI and Machine Learning - Bentley and Google partner to improve asset analytics - Smart Cities World - April 12th, 2025 [April 12th, 2025]
- Where to find the next Earth: Machine learning accelerates the search for habitable planets - Phys.org - April 10th, 2025 [April 10th, 2025]
- Concurrent spin squeezing and field tracking with machine learning - Nature - April 10th, 2025 [April 10th, 2025]
- This AI Paper Introduces a Machine Learning Framework to Estimate the Inference Budget for Self-Consistency and GenRMs (Generative Reward Models) -... - April 10th, 2025 [April 10th, 2025]
- UCI researchers study use of machine learning to improve stroke diagnosis, access to timely treatment - UCI Health - April 10th, 2025 [April 10th, 2025]
- Assessing dengue forecasting methods: a comparative study of statistical models and machine learning techniques in Rio de Janeiro, Brazil - Tropical... - April 10th, 2025 [April 10th, 2025]
- Machine learning integration of multimodal data identifies key features of circulating NT-proBNP in people without cardiovascular diseases - Nature - April 10th, 2025 [April 10th, 2025]
- How AI, Data Science, And Machine Learning Are Shaping The Future - Forbes - April 10th, 2025 [April 10th, 2025]
- Development and validation of interpretable machine learning models to predict distant metastasis and prognosis of muscle-invasive bladder cancer... - April 10th, 2025 [April 10th, 2025]
- From fax machines to machine learning: The fight for efficiency - HME News - April 10th, 2025 [April 10th, 2025]
- Carbon market and emission reduction: evidence from evolutionary game and machine learning - Nature - April 10th, 2025 [April 10th, 2025]
- Infleqtion Unveils Contextual Machine Learning (CML) at GTC 2025, Powering AI Breakthroughs with NVIDIA CUDA-Q and Quantum-Inspired Algorithms - Yahoo... - March 22nd, 2025 [March 22nd, 2025]
- Karlie Kloss' coding nonprofit offering free AI and machine learning workshop this weekend - KSDK.com - March 22nd, 2025 [March 22nd, 2025]
- Machine learning reveals distinct neuroanatomical signatures of cardiovascular and metabolic diseases in cognitively unimpaired individuals -... - March 22nd, 2025 [March 22nd, 2025]
- Machine learning analysis of cardiovascular risk factors and their associations with hearing loss - Nature.com - March 22nd, 2025 [March 22nd, 2025]
- Weekly Recap: Dual-Cure Inks, AI And Machine Learning Top This Weeks Stories - Ink World Magazine - March 22nd, 2025 [March 22nd, 2025]
- Network-based predictive models for artificial intelligence: an interpretable application of machine learning techniques in the assessment of... - March 22nd, 2025 [March 22nd, 2025]
- Machine learning aids in detection of 'brain tsunamis' - University of Cincinnati - March 22nd, 2025 [March 22nd, 2025]
- AI & Machine Learning in Database Management: Studying Trends and Applications with Nithin Gadicharla - Tech Times - March 22nd, 2025 [March 22nd, 2025]
- MicroRNA Biomarkers and Machine Learning for Hypertension Subtyping - Physician's Weekly - March 22nd, 2025 [March 22nd, 2025]
- Machine Learning Pioneer Ramin Hasani Joins Info-Tech's "Digital Disruption" Podcast to Explore the Future of AI and Liquid Neural Networks... - March 22nd, 2025 [March 22nd, 2025]
- Predicting HIV treatment nonadherence in adolescents with machine learning - News-Medical.Net - March 22nd, 2025 [March 22nd, 2025]
- AI And Machine Learning In Ink And Coatings Formulation - Ink World Magazine - March 22nd, 2025 [March 22nd, 2025]
- Counting whales by eavesdropping on their chatter, with help from machine learning - Mongabay.com - March 22nd, 2025 [March 22nd, 2025]
- Associate Professor - Artificial Intelligence and Machine Learning job with GALGOTIAS UNIVERSITY | 390348 - Times Higher Education - March 22nd, 2025 [March 22nd, 2025]
- Innovative Machine Learning Tool Reveals Secrets Of Marine Microbial Proteins - Evrim Aac - March 22nd, 2025 [March 22nd, 2025]
- Exploring the role of breastfeeding, antibiotics, and indoor environments in preschool children atopic dermatitis through machine learning and hygiene... - March 22nd, 2025 [March 22nd, 2025]
- Applying machine learning algorithms to explore the impact of combined noise and dust on hearing loss in occupationally exposed populations -... - March 22nd, 2025 [March 22nd, 2025]
- 'We want them to be the creators': Karlie Kloss' coding nonprofit offering free AI and machine learning workshop this weekend - KSDK.com - March 22nd, 2025 [March 22nd, 2025]
- New headset reads minds and uses AR, AI and machine learning to help people with locked-in-syndrome communicate with loved ones again - PC Gamer - March 22nd, 2025 [March 22nd, 2025]
- Enhancing cybersecurity through script development using machine and deep learning for advanced threat mitigation - Nature.com - March 11th, 2025 [March 11th, 2025]