scholarly journals A machine-learning classifier trained with microRNA ratios to distinguish melanomas from nevi

2018 ◽  
Author(s):  
Rodrigo Torres ◽  
Ursula E Lang ◽  
Miroslav Hejna ◽  
Samuel J Shelton ◽  
Nancy M Joseph ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tumor Cell ◽  
Small Rna Sequencing ◽  
Cell Content ◽  
Patient Age ◽  
Learning Classifier ◽  
Melanocytic Lesions ◽  
Patient Âgé ◽  
Confounding Variables ◽  
Effective Use

The use of microRNAs as biomarkers has been proposed for many diseases including the diagnosis of melanoma. Although hundreds of microRNAs have been identified as differentially expressed in melanomas as compared to benign melanocytic lesions, limited consensus has been achieved across studies, constraining the effective use of these potentially useful markers. In this study we quantified microRNAs by next-generation sequencing from melanomas and their adjacent benign precursor nevi. We applied a machine learning-based pipeline to identify a microRNA signature that separated melanomas from nevi and was unaffected by confounding variables, such as patient age and tumor cell content. By employing the ratios of microRNAs that were either enriched or depleted in melanoma compared to nevi as a normalization strategy, the classifier performed similarly across multiple published microRNA datasets, obtained by microarray, small RNA sequencing, or RT-qPCR. Validation on separate cohorts of melanomas and nevi correctly classified lesions with 83% sensitivity and 71-83% specificity, independent of variation in tumor cell content of the sample or patient age.

scholarly journals Sex Differences in Predictors of Seizure in Contrast-Enhancing Gliomas at Clinical Presentation: A Network Approach

2019 ◽  
Author(s):  
Sandra K Johnston ◽  
Aditya Khurana ◽  
Paula Whitmire ◽  
Sara Ranjbar ◽  
Akanksha Sharma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tumor Size ◽  
Clinical Presentation ◽  
Tumor Location ◽  
Data Repository ◽  
Patient Age ◽  
Entire Cohort ◽  
Prescription Practices ◽  
Patient Âgé ◽  
Co Morbidity

ABSTRACTBackgroundBrain tumor related epilepsy (BTE) is a major co-morbidity related to the management of patients with brain cancer. Despite published practice guidelines recommending against anti-epileptic drug (AED) utilization in patients with gliomas, there is heterogeneity in prescription practices of AEDs in these patients. In an attempt to impact BTE management, we statistically analyzed clinically relevant attributes (sex, age, tumor size, tumor growth kinetics, and tumor location) pertaining to seizure at presentation and used them to build a computational machine learning model to predict the probability of a seizure (at presentation).MethodsFrom our clinical data repository, we identified 223 patients (females, n=86; males, n=137) with pathologically-determined glioma and known seizure status at clinical presentation. Non-parametric and Fisher’s Exact tests were used to identify statistical differences in clinical characteristics. We utilized a random forest machine learning method for generating our predictive models by entire cohort and separated by male and female.FindingsPatients were divided into those that presented with seizure (SP, n=96, 43%; F, n= 28; M, n= 68) and those that presented without seizure (nSP, n=127, 57%, F n=58, M n=69). Females presented with seizures significantly less often than males (x2=6·28, p=0·01). SP patients had significantly smaller T1Gd radius compared to nSP (SP 11·30mm, nSP 18.66mm, p<0·0001). Tumor size and patient age were significant negative predictors for SP; patients with larger tumors, older age and less tumor diffusivity (p/D) were at lower risk for SP.InterpretationDespite heterogeneity across our patient cohort, there is strong evidence of a role for patient sex, tumor size, tumor invasion, and patient age in predicting the incidence of seizures at diagnosis. Future studies, with prospectively detailed data collection, may provide clearer insights into the incidence of seizures through a patient’s treatment course.

Using machine learning to examine drivers of inappropriate outpatient antibiotic prescribing in acute respiratory illnesses

2022 ◽  
pp. 1-5
Author(s):  
Laura M. King ◽  
Michael Kusnetsov ◽  
Avgoustinos Filippoupolitis ◽  
Deniz Arik ◽  
Monina Bartoces ◽  
...  
Keyword(s):  
Machine Learning ◽  
Claims Data ◽  
Learning Model ◽  
Outpatient Setting ◽  
Antibiotic Prescribing ◽  
Patient Age ◽  
Data Set ◽  
Respiratory Illnesses ◽  
Patient Âgé ◽  
Machine Learning Model

Abstract Using a machine-learning model, we examined drivers of antibiotic prescribing for antibiotic-inappropriate acute respiratory illnesses in a large US claims data set. Antibiotics were prescribed in 11% of the 42 million visits in our sample. The model identified outpatient setting type, patient age mix, and state as top drivers of prescribing.

scholarly journals Early risk assessment for COVID-19 patients from emergency department data using machine learning

2020 ◽  
Author(s):  
Frank Stefan Heldt ◽  
Marcela P Vizcaychipi ◽  
Sophie Peacock ◽  
Mattia Cinelli ◽  
Lachlan McLachlan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Emergency Department ◽  
Mechanical Ventilation ◽  
Intensive Care ◽  
Patient Age ◽  
Clinical Endpoints ◽  
Early Risk ◽  
Patient Âgé ◽  
Laboratory Results

Background Since its emergence in late 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic, with more than 4.8 million reported cases and 310 000 deaths worldwide. While epidemiological and clinical characteristics of COVID-19 have been reported, risk factors underlying the transition from mild to severe disease among patients remain poorly understood. Methods In this retrospective study, we analysed data of 820 confirmed COVID-19 positive patients admitted to a two-site NHS Trust hospital in London, England, between January 1st and April 23rd, 2020, with a majority of cases occurring in March and April. We extracted anonymised demographic data, physiological clinical variables and laboratory results from electronic healthcare records (EHR) and applied multivariate logistic regression, random forest and extreme gradient boosted trees. To evaluate the potential for early risk assessment, we used data available during patients' initial presentation at the emergency department (ED) to predict deterioration to one of three clinical endpoints in the remainder of the hospital stay: A) admission to intensive care, B) need for mechanical ventilation and C) mortality. Based on the trained models, we extracted the most informative clinical features in determining these patient trajectories. Results Considering our inclusion criteria, we have identified 126 of 820 (15%) patients that required intensive care, 62 of 808 (8%) patients needing mechanical ventilation, and 170 of 630 (27%) cases of in-hospital mortality. Our models learned successfully from early clinical data and predicted clinical endpoints with high accuracy, the best model achieving AUC-ROC scores of 0.75 to 0.83 (F1 scores of 0.41 to 0.56). Younger patient age was associated with an increased risk of receiving intensive care and ventilation, but lower risk of mortality. Clinical indicators of a patient's oxygen supply and selected laboratory results were most predictive of COVID-19 patient trajectories. Conclusion Among COVID-19 patients machine learning can aid in the early identification of those with a poor prognosis, using EHR data collected during a patient's first presentation at ED. Patient age and measures of oxygenation status during ED stay are primary indicators of poor patient outcomes.

P–378 Using a machine learning tool (72% accuracy with 64% PPV) to predict multiple live birth when transferring multiple embryos, based on embryo specific data

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
C A Pena ◽  
J Chambost ◽  
C Hickman ◽  
C Jacques ◽  
K Wiemer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Embryo Transfer ◽  
Live Birth ◽  
Predictive Power ◽  
Cohort Analysis ◽  
Multiple Pregnancy ◽  
Patient Age ◽  
Patient Âgé ◽  
Increased Risk ◽  
Number Of Cells

Abstract Study question Can Machine Learning predict multiple pregnancy based on data specific to the embryos and the patient? Summary answer Embryo data are useful in determining which embryos are likely to lead to multiple pregnancy. Patient age has low predictive value compared to embryo data. What is known already Our previous assessment of the HFEA data demonstrated that single embryo transfer (SET) in the UK occurred in a minority (45%) of fresh cycles, with a marginal increase in live birth rate (LBR) in some patient cohorts in favor of multiple embryo transfer (MET). Current policies on determining number of embryos for transfer tend to be generic and do not account for detailed embryology data. Generic policies may compromise LBR for some patients that would benefit from MET. Artificial Intelligence has the potential to assist in this decision process. Study design, size, duration Retrospective cohort analysis from 2013 to 2020 of 193 cycles with 386 embryos used in double ETs on day 5 at POMA fertility clinic with positive live birth outcome. ML model, xgboost, was trained to predict multiple live birth (N = 54) versus single live birth (N = 139). Detailed embryology data from day 1 to day 5 were used as input. Participants/materials, setting, methods Input of the machine learning model included patient age and 18 morphological parameters collected on days 1, 2, 3 and 5 (symmetry, number of cells, blastocyst status, fragmentation, ICM and troph grades) from the two transferred embryos. An xgboost algorithm was trained on 80% of the data (n = 154) and tested on 20% of blind data (n = 39). Main results and the role of chance Xgboost machine learning algorithm predicted multiple live birth on the blind dataset with an accuracy of 72%, with an AUC of 0.60, showing better results than random. PPV (true prediction of multiple births) was 64% and NPV (true prediction of single birth) was 75%. The following parameters ranked high in the predictive power of the machine learning (in order of predictive power): blastocyst status on day 5 of both embryos, symmetry on day 3, number of cells on day 2, scores on day 2 and 3. Limitations, reasons for caution: The dataset was derived from a single clinic with manual annotations and may not be transferable to other clinics. The risk of bias is important as the model was trained only àon embryos that were transferred and led to at least one birth Wider implications of the findings: A tool to help identify which patients are at increased risk of MP with MET would be clinically useful to help patients and clinical team make the best personalised decision for a specific embryo, finding the balance between maximising success rate whilst minimising multiple pregnancy rate and its associated risks. Trial registration number Not applicable

Gestion orthopédique d’une ankylose de l’ATM chez l’enfant

2020 ◽  
Vol 54 (4) ◽  
pp. 407-418
Author(s):  
Pamela Villalon-Pooley ◽  
Camila Hernandez-Veliz ◽  
Maria Fernanda Pinto-Chavez ◽  
Pierre Bourdiol
Keyword(s):  
Patient Age ◽  
Patient Âgé

Parmi les fractures cranio-faciales, celles affectant le condyle mandibulaire font partie des fractures les plus souvent rencontrées chez le patient en âge pédiatrique. L’évolution sans traitement peut produire une ankylose temporo-mandibulaire entraînant troubles fonctionnels et asymétrie de la croissance cranio-faciale. Le traitement traditionnellement chirurgical est d’un pronostic généralement réservé. Dans cet article est présenté le cas d’un patient, âgé de quatre ans, atteint d’ankylose fibreuse de l’articulation temporo-mandibulaire gauche, suite probable d’une fracture du col du condyle non-diagnostiquée. La libération fonctionnelle de la fibro-ankylose articulaire a été l’objectif de la première étape thérapeutique. Celle-ci a été suivie, à l’âge de sept ans, d’une distraction articulaire obtenue au moyen de butées occlusales controlatérales disposées côté droit. Ceci a produit un ajustement de la croissance dento-alvéolaire assurant à la fois un rattrapage du déficit de croissance unilatéral de départ et une néoformation condylienne par remodelage de l’articulation temporo-mandibulaire gauche. Quatre années après la mise en route de la phase orthopédique initiale, la fonction articulaire restaurée et l’équilibre facial obtenu restent stables chez ce jeune patient

Hemmkörperentwicklung bei Hämophilie-Patienten nach Präparate wechsel

2012 ◽  
Vol 32 (S 01) ◽  
pp. S39-S42 ◽  
Author(s):  
S. Kocher ◽  
G. Asmelash ◽  
V. Makki ◽  
S. Müller ◽  
S. Krekeler ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Observational Study ◽  
Retrospective Observational Study ◽  
Frequent Change ◽  
Patient Age ◽  
Treatment Regimens ◽  
Patient Âgé ◽  
The Relationship ◽  
Observation Period

SummaryThe retrospective observational study surveys the relationship between development of inhibitors in the treatment of haemophilia patients and risk factors such as changing FVIII products. A total of 119 patients were included in this study, 198 changes of FVIII products were evaluated. Results: During the observation period of 12 months none of the patients developed an inhibitor, which was temporally associated with a change of FVIII products. A frequent change of FVIII products didn’t lead to an increase in inhibitor risk. The change between plasmatic and recombinant preparations could not be confirmed as a risk factor. Furthermore, no correlation between treatment regimens, severity, patient age and comorbidities of the patients could be found.

Hämophilie-Zentrum Bonn 1980 – 2009

2011 ◽  
Vol 31 (S 01) ◽  
pp. S4-S10 ◽  
Author(s):  
I. Besmens ◽  
H.-H. Brackmann ◽  
J. Oldenburg
Keyword(s):  
Young Patients ◽  
Severe Form ◽  
Coagulation Disorder ◽  
Care Providers ◽  
Long Distance ◽  
Patient Age ◽  
Patient Âgé ◽  
Number Of Patients ◽  
Clotting Disorder ◽  
Regional Character

SummaryThe Bonn Haemophilia Care Center provides patient care on a superregional level. The centre’s large service area is, in part, due to the introduction of haemophilia home treatment and related to this the individualized prophylaxis in children and adults by Egli and Brack-mann in Bonn in the early 1970s, that represented a milestone in German haemophilia therapy. Epidemiologic patient data from the two selected time points, 1980 and 2009, are evaluated to illustrate the change in the composition of the patient clientele. In 1980 a total of 639 patients were treated at the Bonn Haemophilia Center. 529 patients exhibited a severe form and 110 a non-severe form of the respective clotting disorder. In 2009 the Bonn Haemophilia Center took care for a total of 837 patients. There were 445 patients who suffered from a severe form of the considered clotting disorder while 392 showed a non-severe course. The number of less severely affected patients has increased significantly in 2009. Patients in 1980 were predominantly suffering from a severe form and most had to travel more than 150 km from their homes to the treatment center. In 2009 the number of patients living a medium-long distance from the care provider has significantly increased while the number of patients living more than 150km from the center has decreased. Comparing 2009 to 1980 a growth of the center’s regional character becomes apparent, especially when patient age and severity of the coagulation disorder are taken into consideration. The regional character was more strongly pronounced with milder disease severity and lower patient age. Due to the existence of well established primary haemophilia care in CCCs in Germany, the trend for the recent years is that the proportion of young patients that choose haemophilia care providers closer to their homes is increasing.

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