8 research outputs found
Effects of high hydrostatic pressure on O(2) consumption of skeletal muscle
No full textExperiments have been performed to examin differences of O(2) consumption of frog's muscle (M. sartorius) on which applied 300 atm. pressure for 30 min. and stimulating electrically. At that time muscles had constracted. The results were as follows: 1) There were no differences on O(2) consumption of muscle on which applied pressure and stimulating electrically. 2) It has been supposed that there were no differences of metabolism of contracted muscle and tetanus. 3) On the lower temperature of 18°C. O(2) consumption of stimulated muscle electrically have been increased a little more than that of applied pressure. (increased avarage 23%) 4) It has been supposed that the pressure stimulus of lower than 300 atm. pressure did not become a sufficient stimulus of contraction, and higher pressure than 300 atm. pressure had been able to be pressure to increase the O(2) consumption of muscle
Table_1_Machine learning early prediction of respiratory syncytial virus in pediatric hospitalized patients.docx
No full textRespiratory syncytial virus (RSV) causes millions of infections among children in the US each year and can cause severe disease or death. Infections that are not promptly detected can cause outbreaks that put other hospitalized patients at risk. No tools besides diagnostic testing are available to rapidly and reliably predict RSV infections among hospitalized patients. We conducted a retrospective study from pediatric electronic health record (EHR) data and built a machine learning model to predict whether a patient will test positive to RSV by nucleic acid amplification test during their stay. Our model demonstrated excellent discrimination with an area under the receiver-operating curve of 0.919, a sensitivity of 0.802, and specificity of 0.876. Our model can help clinicians identify patients who may have RSV infections rapidly and cost-effectively. Successfully integrating this model into routine pediatric inpatient care may assist efforts in patient care and infection control.</p
Data_Sheet_1_Pediatric Severe Sepsis Prediction Using Machine Learning.docx
No full textBackground: Early detection of pediatric severe sepsis is necessary in order to optimize effective treatment, and new methods are needed to facilitate this early detection.Objective: Can a machine-learning based prediction algorithm using electronic healthcare record (EHR) data predict severe sepsis onset in pediatric populations?Methods: EHR data were collected from a retrospective set of de-identified pediatric inpatient and emergency encounters for patients between 2–17 years of age, drawn from the University of California San Francisco (UCSF) Medical Center, with encounter dates between June 2011 and March 2016.Results: Pediatric patients (n = 9,486) were identified and 101 (1.06%) were labeled with severe sepsis following the pediatric severe sepsis definition of Goldstein et al. (1). In 4-fold cross-validation evaluations, the machine learning algorithm achieved an AUROC of 0.916 for discrimination between severe sepsis and control pediatric patients at the time of onset and AUROC of 0.718 at 4 h before onset. The prediction algorithm significantly outperformed the Pediatric Logistic Organ Dysfunction score (PELOD-2) (p Conclusion: This machine learning algorithm has the potential to deliver high-performance severe sepsis detection and prediction through automated monitoring of EHR data for pediatric inpatients, which may enable earlier sepsis recognition and treatment initiation.</p
Supplementary Material, cjkhd_supplemental_tables – Prediction of Acute Kidney Injury With a Machine Learning Algorithm Using Electronic Health Record Data
No full textSupplementary Material, cjkhd_supplemental_tables for Prediction of Acute Kidney Injury With a Machine Learning Algorithm Using Electronic Health Record Data by Hamid Mohamadlou, Anna Lynn-Palevsky, Christopher Barton, Uli Chettipally, Lisa Shieh, Jacob Calvert, Nicholas R. Saber, and Ritankar Das in Canadian Journal of Kidney Health and Disease</p
Abb. 9a-c in Über bemerkenswerte Faltenwespen aus der äthiopischen Region Teil 10 (Hymnoptera, Vespidae: Eumeninae)
No full textAbb. 9a-c: (9a) Stroudua idae nov.sp. ♀, Habitus; (9b) Etikett; (9c) Habitus lateral. Abb. 10-11: Stroudua idae nov.sp. ♀ (10) Clypeus; (11) 1. Tergit.Published as part of Gusenleitner, Josef, 2017, Über bemerkenswerte Faltenwespen aus der äthiopischen Region Teil 10 (Hymnoptera, Vespidae: Eumeninae), pp. 119-129 in Linzer biologische Beiträge 49 (1) on page 123, DOI: 10.5281/zenodo.535641
Image_1_Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm.pdf
No full textBackgroundStrokes represent a leading cause of mortality globally. The evolution of developing new therapies is subject to safety and efficacy testing in clinical trials, which operate in a limited timeframe. To maximize the impact of these trials, patient cohorts for whom ischemic stroke is likely during that designated timeframe should be identified. Machine learning may improve upon existing candidate identification methods in order to maximize the impact of clinical trials for stroke prevention and treatment and improve patient safety.MethodsA retrospective study was performed using 41,970 qualifying patient encounters with ischemic stroke from inpatient visits recorded from over 700 inpatient and ambulatory care sites. Patient data were extracted from electronic health records and used to train and test a gradient boosted machine learning algorithm (MLA) to predict the patients' risk of experiencing ischemic stroke from the period of 1 day up to 1 year following the patient encounter. The primary outcome of interest was the occurrence of ischemic stroke.ResultsAfter training for optimization, XGBoost obtained a specificity of 0.793, a positive predictive value (PPV) of 0.194, and a negative predictive value (NPV) of 0.985. The MLA further obtained an area under the receiver operating characteristic (AUROC) of 0.88. The Logistic Regression and multilayer perceptron models both achieved AUROCs of 0.862. Among features that significantly impacted the prediction of ischemic stroke were previous stroke history, age, and mean systolic blood pressure.ConclusionMLAs have the potential to more accurately predict the near risk of ischemic stroke within a 1-year prediction window for individuals who have been hospitalized. This risk stratification tool can be used to design clinical trials to test stroke prevention treatments in high-risk populations by identifying subjects who would be more likely to benefit from treatment.</p
Table_1_Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm.pdf
No full textBackgroundStrokes represent a leading cause of mortality globally. The evolution of developing new therapies is subject to safety and efficacy testing in clinical trials, which operate in a limited timeframe. To maximize the impact of these trials, patient cohorts for whom ischemic stroke is likely during that designated timeframe should be identified. Machine learning may improve upon existing candidate identification methods in order to maximize the impact of clinical trials for stroke prevention and treatment and improve patient safety.MethodsA retrospective study was performed using 41,970 qualifying patient encounters with ischemic stroke from inpatient visits recorded from over 700 inpatient and ambulatory care sites. Patient data were extracted from electronic health records and used to train and test a gradient boosted machine learning algorithm (MLA) to predict the patients' risk of experiencing ischemic stroke from the period of 1 day up to 1 year following the patient encounter. The primary outcome of interest was the occurrence of ischemic stroke.ResultsAfter training for optimization, XGBoost obtained a specificity of 0.793, a positive predictive value (PPV) of 0.194, and a negative predictive value (NPV) of 0.985. The MLA further obtained an area under the receiver operating characteristic (AUROC) of 0.88. The Logistic Regression and multilayer perceptron models both achieved AUROCs of 0.862. Among features that significantly impacted the prediction of ischemic stroke were previous stroke history, age, and mean systolic blood pressure.ConclusionMLAs have the potential to more accurately predict the near risk of ischemic stroke within a 1-year prediction window for individuals who have been hospitalized. This risk stratification tool can be used to design clinical trials to test stroke prevention treatments in high-risk populations by identifying subjects who would be more likely to benefit from treatment.</p
Supplementary_Materials – Supplemental material for Multicenter validation of a machine-learning algorithm for 48-h all-cause mortality prediction
No full textSupplemental material, Supplementary_Materials for Multicenter validation of a machine-learning algorithm for 48-h all-cause mortality prediction by Hamid Mohamadlou, Saarang Panchavati, Jacob Calvert, Anna Lynn-Palevsky, Sidney Le, Angier Allen, Emily Pellegrini, Abigail Green-Saxena, Christopher Barton, Grant Fletcher, Lisa Shieh, Philip B Stark, Uli Chettipally, David Shimabukuro, Mitchell Feldman and Ritankar Das in Health Informatics Journal</p
