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Johns Hopkins HealthCare machine learning
Machine Learning, supplied by Johns Hopkins HealthCare, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/machine+learning+algorithms/machine+learning+algorithm/pmc11932220-159-1-12
Average 90 stars, based on 1 article reviews
machine learning - by Bioz Stars, 2026-09
90/100 stars

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Allele-specific Oligonucleotide:

Article Title: ASO Visual Abstract: Predicting Postoperative Infection After Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy with Splenectomy.
Article Snippet: Vol.. :(0123456789) Ann Surg Oncol https://doi.org/10.1245/s10434-025-16894-w ASO VISUAL ABSTRACT ASO Visual Abstract: Predicting Postoperative Infection After Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy with Splenectomy Nolan M. Winicki, MD, MS, Shannon N. Radomski, MD, Yusuf Ciftci, BS, Fabian M. Johnston, MD, MHS, and Jonathan B. Greer, MD Peritoneal Surface Malignancy Program, Division of Gastrointestinal Surgical Oncology, Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD A novel machine learning algorithm was developed to predict postoperative infection after cytoreductive surgery/hyperthermic intraperitoneal chemotherapy with splenectomy that could aid in the early diagnosis and initiation of treatment (https:// doi. org/ 10.. 1245/ s10434- 024- 16728-1).

Infection:

Article Title: ASO Visual Abstract: Predicting Postoperative Infection After Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy with Splenectomy.
Article Snippet: Vol.. :(0123456789) Ann Surg Oncol https://doi.org/10.1245/s10434-025-16894-w ASO VISUAL ABSTRACT ASO Visual Abstract: Predicting Postoperative Infection After Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy with Splenectomy Nolan M. Winicki, MD, MS, Shannon N. Radomski, MD, Yusuf Ciftci, BS, Fabian M. Johnston, MD, MHS, and Jonathan B. Greer, MD Peritoneal Surface Malignancy Program, Division of Gastrointestinal Surgical Oncology, Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD A novel machine learning algorithm was developed to predict postoperative infection after cytoreductive surgery/hyperthermic intraperitoneal chemotherapy with splenectomy that could aid in the early diagnosis and initiation of treatment (https:// doi. org/ 10.. 1245/ s10434- 024- 16728-1).

Biomarker Discovery:

Article Title: ASO Visual Abstract: Predicting Postoperative Infection After Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy with Splenectomy.
Article Snippet: Vol.. :(0123456789) Ann Surg Oncol https://doi.org/10.1245/s10434-025-16894-w ASO VISUAL ABSTRACT ASO Visual Abstract: Predicting Postoperative Infection After Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy with Splenectomy Nolan M. Winicki, MD, MS, Shannon N. Radomski, MD, Yusuf Ciftci, BS, Fabian M. Johnston, MD, MHS, and Jonathan B. Greer, MD Peritoneal Surface Malignancy Program, Division of Gastrointestinal Surgical Oncology, Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD A novel machine learning algorithm was developed to predict postoperative infection after cytoreductive surgery/hyperthermic intraperitoneal chemotherapy with splenectomy that could aid in the early diagnosis and initiation of treatment (https:// doi. org/ 10.. 1245/ s10434- 024- 16728-1).

other:

Article Title: Machine learning methods for classifying novel fentanyl analogs from Raman spectra of pure compounds
Article Snippet: In previous research, we demonstrated the promise of detecting novel fentanyl analogs from mass spectra using machine learning models.. This approach complements existing library matching methods and provides a key capability amid the recent sharp increase in abuse of fentanyl and its analogs.. However, many applications rely upon portable devices such as Raman spectrometers, rather than mass spectrometers that are generally located in laboratories.

Article Title: Obesity prediction: Novel machine learning insights into waist circumference accuracy.
Article Snippet: Aims: This study aims to enhance the precision of obesity risk assessments by improving the accuracy of waist circumference predictions using machine learning techniques.. Methods: We utilized data from the NHANES and Look AHEAD studies, applying machine learning algorithms augmented with uncertainty quantification.. Our approach centered on conformal prediction techniques, which provide a methodological basis for generating prediction intervals that reflect uncertainty levels.

Article Title: Can we design the next generation of digital health communication programs by leveraging the power of artificial intelligence to segment target audiences, bolster impact and deliver differentiated services? A machine learning analysis of survey data from rural India
Article Snippet: A machine learning analysis of survey data from rural India Jean Juste Harrisson Bashingwa, PhD (corresponding author) MRC/Wits-Agincourt Unit, School of Public Health, University of the Witwatersrand, 27 St. Andrews Road, Parktown, 2193, South Africa Email: jeanjuste@aims.ac.za Diwakar Mohan, DrPH Department of International Health, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St, Baltimore, Maryland, USA Email: dmohan3@jhu.edu Sara Chamberlain, MA Innov8 Old Fort Saket District Mall, Saket District Centre, Sector 6, Pushp Vihar, New Delhi, Delhi 110017, India Email: sara.chamberlain@in.bbcmediaaction.org Kerry Scott, PhD Department of International Health, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St, Baltimore, Maryland, USA Email: kscott26@jhu.edu Osama Ummer, MHA (1) BBC Media Action-India, Innov8 Old Fort Saket District Mall, Saket District Centre, Sector 6, Pushp Vihar, New Delhi, Delhi 110017, India (2) Oxford Policy Management-Delhi, 4/6 First Floor, Siri Fort Institutional Area, New Delhi, Delhi 110049, India Email: kposamaummer@gmail.com Anna Godfrey, PhD BBC Media Action, Ibex House, 42-47 Minories, London, EC3N 1DY, England Email: anna.godfrey@bbc.co.uk Nicola Mulder, PhD Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town Anzio Road, Observatory, 7925, Cape Town, South Africa Email: nicola.mulder@uct.ac.za Deshen Moodley, PhD Department of Computer Science, 18 University Avenue, University of Cape Town Rondebosch, Cape Town, South Africa Email: deshen@cs.uct.ac.za Page 2 of 37 For peer review only - http://bmjopen.bmj.com/site/about/guidelines.xhtml BMJ Open 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For peer review only Amnesty E. LeFevre PhD 1.

Article Title: Learning from COVID-19: How drug hunters can prepare for the next pandemic.
Article Snippet: Fe at ur e P ER SP EC TI V E Over 3 years, the SARS-CoV-2 pandemic killed nearly 7 million people and infected more than 767 million globally.. During this time, our very small company was able to contribute to antiviral drug discovery efforts through global collaborations with other researchers, which enabled the identification and repurposing of multiple molecules with activity against SARS-CoV-2 including pyronaridine tetraphosphate, tilorone, quinacrine, vandetanib, lumefantrine, cetylpyridinium chloride, raloxifene, carvedilol, olmutinib, dacomitinib, crizotinib, and bosutinib.. We highlight some of the key findings from this experience of using different computational and experimental strategies, and detail some of the challenges and strategies for how we might better prepare for the next pandemic so that potential antiviral treatments are available for future outbreaks.

Article Title: Can we design the next generation of digital health communication programs by leveraging the power of artificial intelligence to segment target audiences, bolster impact and deliver differentiated services? A machine learning analysis of survey data from rural India
Article Snippet: A machine learning analysis of survey data from rural India Jean Juste Harrisson Bashingwa, PhD (corresponding author) MRC/Wits-Agincourt Unit, School of Public Health, University of the Witwatersrand, 27 St. Andrews Road, Parktown, 2193, South Africa Email: jeanjuste@aims.ac.za Diwakar Mohan, DrPH Department of International Health, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St, Baltimore, Maryland, USA Email: dmohan3@jhu.edu Sara Chamberlain, MA Innov8 Old Fort Saket District Mall, Saket District Centre, Sector 6, Pushp Vihar, New Delhi, Delhi 110017, India Email: sara.chamberlain@in.bbcmediaaction.org Kerry Scott, PhD Department of International Health, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St, Baltimore, Maryland, USA Email: kscott26@jhu.edu Osama Ummer, MHA (1) BBC Media Action-India, Innov8 Old Fort Saket District Mall, Saket District Centre, Sector 6, Pushp Vihar, New Delhi, Delhi 110017, India (2) Oxford Policy Management-Delhi, 4/6 First Floor, Siri Fort Institutional Area, New Delhi, Delhi 110049, India Email: kposamaummer@gmail.com Anna Godfrey, PhD BBC Media Action, Ibex House, 42-47 Minories, London, EC3N 1DY, England Email: anna.godfrey@bbc.co.uk Nicola Mulder, PhD Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town Anzio Road, Observatory, 7925, Cape Town, South Africa Email: nicola.mulder@uct.ac.za Deshen Moodley, PhD Department of Computer Science, 18 University Avenue, University of Cape Town Rondebosch, Cape Town, South Africa Email: deshen@cs.uct.ac.za Page 2 of 38 For peer review only - http://bmjopen.bmj.com/site/about/guidelines.xhtml BMJ Open 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For peer review only Amnesty E. LeFevre PhD 1.

Article Title: Clinical and Operational Applications of Artificial Intelligence and Machine Learning in Pharmacy: A Narrative Review of Real-World Applications
Article Snippet: Using machine learning to anticipate adverse drug reactions (ADRs) in high-risk patients, Johns Hopkins Hospital has made significant advances in patient safety.

In Silico:

Article Title: Researchers and regulators plan for a future without lab animals.
Article Snippet: T he removal of one word from an 85-year-old American regulation sends a signal of change to the whole field of drug development and could mean a major shift away from animal testing.. Approved by Congress and signed by President Joe Biden in late December 2022, the Food and Drug Administration (FDA) Modernization Act 2.0 replaced the word “animal” with “nonclinical tests” in the law governing the agency’s drug assessments.. The change removed the requirement that pharmaceutical companies test therapies on animals before starting clinical trials and opened the way for expanding the use of alternative methods (Fig. 1).



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