Results 21 to 30 of about 662,113 (303)

Prediction of Chronic Periodontitis Severity Using Machine Learning Models Based On Salivary Bacterial Copy Number

Frontiers in Cellular and Infection Microbiology, 2020
Periodontitis is a widespread chronic inflammatory disease caused by interactions between periodontal bacteria and homeostasis in the host. We aimed to investigate the performance and reliability of machine learning models in predicting the severity of ...
Eun-Hye Kim, Eun-Hye Kim, Seunghoon Kim, Seunghoon Kim, Hyun-Joo Kim, Hyun-Joo Kim, Hyoung-oh Jeong, Hyoung-oh Jeong, Jaewoong Lee, Jaewoong Lee, Jinho Jang, Jinho Jang, Ji-Young Joo, Ji-Young Joo, Yerang Shin, Jihoon Kang, Ae Kyung Park, Ju-Youn Lee, Ju-Youn Lee, Semin Lee, Semin Lee   +20 more
doaj   +1 more source

Interleukin 8 and hepatocyte growth factor in predicting development of severe acute pancreatitis

Cogent Medicine, 2017
Objectives: We aimed to study if interleukin (IL) 8 and hepatocyte growth factor (HGF) predict development of severe acute pancreatitis (SAP) among patients without organ dysfunction (OD) at presentation, and if they discriminate transient OD from ...
Anne K. Penttilä, Outi Lindström, Johanna Hästbacka, Krista Kuuliala, Harri Mustonen, Pauli Puolakkainen, Antti Kuuliala, Marko Salmi, Mari Hämäläinen, Eeva Moilanen, Heikki Repo, Leena Kylänpää   +11 more
doaj   +1 more source

Prediction of survival probabilities with Bayesian Decision Trees [PDF]

, 2013
Practitioners use Trauma and Injury Severity Score (TRISS) models for predicting the survival probability of an injured patient. The accuracy of TRISS predictions is acceptable for patients with up to three typical injuries, but unacceptable for patients
Bailey, Becalick, Bishop, Bouamra, Boyd, Breiman, Chawda, Chipman, Chipman, Clermont, Denison, Dietterich, DiRusso, Domingos, Duda, Green, Hadfield, Hall, Hilden, Hunter, Jaimes, Jakaite, Kilgo, Kilgo, Koshy, Kreke, Krzanowski, Kuncheva, Li, Livia Jakaite, Lunn, Millham, Oakland, Osler, Osler, Patil, Quinlan, Robert, Rogers, Rogers, Schetinin, Schetinin, Schetinin, Schetinin, Silva, Steyerberg, Stojadinovic, Sujin, Vapnik, Vitaly Schetinin, Wojtek J. Krzanowski   +50 more
core   +2 more sources

Cognitive-behavioral therapy for obsessive-compulsive disorder: access to treatment, prediction of long-term outcome with neuroimaging. [PDF]

, 2015
This article reviews issues related to a major challenge to the field for obsessive-compulsive disorder (OCD): improving access to cognitive-behavioral therapy (CBT).
Feusner, Jamie D, O'Neill, Joseph
core   +2 more sources

Predicting Severe Pneumonia Outcomes in Children [PDF]

Pediatrics, 2016
BACKGROUND: Substantial morbidity and excessive care variation are seen with pediatric pneumonia. Accurate risk-stratification tools to guide clinical decision-making are needed. METHODS: We developed risk models to predict severe pneumonia outcomes
Derek J, Williams, Yuwei, Zhu, Carlos G, Grijalva, Wesley H, Self, Frank E, Harrell, Carrie, Reed, Chris, Stockmann, Sandra R, Arnold, Krow K, Ampofo, Evan J, Anderson, Anna M, Bramley, Richard G, Wunderink, Jonathan A, McCullers, Andrew T, Pavia, Seema, Jain, Kathryn M, Edwards   +15 more
openaire   +2 more sources

Dolosigranulum pigrum: Predicting Severity of Infection [PDF]

Cureus, 2020
In this report, we describe a case of a 61-year-old male patient who had the bacterium Dolosigranulum pigrum growing in a blood culture. It was susceptible to ampicillin, ceftriaxone, levofloxacin, and vancomycin but was intermediately resistant to erythromycin.
Sherret, John, Gajjar, Bhavesh, Ibrahim, Lamis, Elgazzar, Ahmed Mohamed, Panta, Utsab R.   +4 more
openaire   +3 more sources

Traffic Accident’s Severity Prediction: A Deep-Learning Approach-Based CNN Network

IEEE Access, 2019
In traffic accident, an accurate and timely severity prediction method is necessary for the successful deployment of an intelligent transportation system to provide corresponding levels of medical aid and transportation in a timely manner.
Ming Zheng, Tong Li, Rui Zhu, Jing Chen, Zifei Ma, Mingjing Tang, Zhongqiang Cui, Zhan Wang   +7 more
doaj   +1 more source

Phenotype-based and Self-learning Inter-individual Sleep Apnea Screening with a Level IV Monitoring System [PDF]

, 2018
Purpose: We propose a phenotype-based artificial intelligence system that can self-learn and is accurate for screening purposes, and test it on a Level IV monitoring system. Methods: Based on the physiological knowledge, we hypothesize that the phenotype
Hau-Tieng Wu, Hau-Tieng Wu, Hau-Tieng Wu, Jhao-Cheng Wu, Po-Chiun Huang, Ting-Yu Lin, Tsai-Yu Wang, Yu-Lun Lo, Yuan-Hao Huang   +8 more
core   +2 more sources

Anomaly Detection and Severity Prediction of Air Leakage in Train Braking Pipes

International Journal of Prognostics and Health Management, 2017
Air leakage in braking pipes is a commonly encountered mechanical defect on trains. A severe air leakage will lead to braking issues and therefore decrease the reliability and cause train delays or stranding.
Wan-Jui Lee
doaj   +1 more source

External validation of a simple clinical tool used to predict falls in people with Parkinson disease [PDF]

, 2015
Published in final edited form as: Parkinsonism Relat Disord. 2015 August ; 21(8): 960–963. doi:10.1016/j.parkreldis.2015.05.008.BACKGROUND: Assessment of fall risk in an individual with Parkinson disease (PD) is a critical yet often time consuming ...
Canning, Colleen G., Cavanaugh, James T., Dibble, Leland E., Duncan, Ryan P., Earhart, Gammon M., Ellis, Terry D., Ford, Matthew P., Foreman, K. Bo, Leddy, Abigail L., Paul, Serene S., Thackeray, Anne   +10 more
core   +1 more source