scholarly journals Automated labour detection framework to monitor pregnant women with a high risk of premature labour using machine learning and deep learning

2021 ◽  
pp. 100771
Author(s):  
Hisham Allahem ◽  
Srinivas Sampalli
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Risk ◽  
Pregnant Women ◽  
Premature Labour

scholarly journals The Use of Synthetic Electronic Health Record Data and Deep Learning to Improve Timing of High-Risk Heart Failure Surgical Intervention by Predicting Proximity to Catastrophic Decompensation

2020 ◽  
Vol 2 ◽  
Author(s):  
Aixia Guo ◽  
Randi E. Foraker ◽  
Robert M. MacGregor ◽  
Faraz M. Masood ◽  
Brian P. Cupps ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Deep Learning ◽  
High Risk ◽  
Electronic Health Record ◽  
Surgical Intervention ◽  
Health Record ◽  
Predictive Capability ◽  
Clinical Tool ◽  
Electronic Health

Objective: Although many clinical metrics are associated with proximity to decompensation in heart failure (HF), none are individually accurate enough to risk-stratify HF patients on a patient-by-patient basis. The dire consequences of this inaccuracy in risk stratification have profoundly lowered the clinical threshold for application of high-risk surgical intervention, such as ventricular assist device placement. Machine learning can detect non-intuitive classifier patterns that allow for innovative combination of patient feature predictive capability. A machine learning-based clinical tool to identify proximity to catastrophic HF deterioration on a patient-specific basis would enable more efficient direction of high-risk surgical intervention to those patients who have the most to gain from it, while sparing others. Synthetic electronic health record (EHR) data are statistically indistinguishable from the original protected health information, and can be analyzed as if they were original data but without any privacy concerns. We demonstrate that synthetic EHR data can be easily accessed and analyzed and are amenable to machine learning analyses.Methods: We developed synthetic data from EHR data of 26,575 HF patients admitted to a single institution during the decade ending on 12/31/2018. Twenty-seven clinically-relevant features were synthesized and utilized in supervised deep learning and machine learning algorithms (i.e., deep neural networks [DNN], random forest [RF], and logistic regression [LR]) to explore their ability to predict 1-year mortality by five-fold cross validation methods. We conducted analyses leveraging features from prior to/at and after/at the time of HF diagnosis.Results: The area under the receiver operating curve (AUC) was used to evaluate the performance of the three models: the mean AUC was 0.80 for DNN, 0.72 for RF, and 0.74 for LR. Age, creatinine, body mass index, and blood pressure levels were especially important features in predicting death within 1-year among HF patients.Conclusions: Machine learning models have considerable potential to improve accuracy in mortality prediction, such that high-risk surgical intervention can be applied only in those patients who stand to benefit from it. Access to EHR-based synthetic data derivatives eliminates risk of exposure of EHR data, speeds time-to-insight, and facilitates data sharing. As more clinical, imaging, and contractile features with proven predictive capability are added to these models, the development of a clinical tool to assist in timing of intervention in surgical candidates may be possible.

scholarly journals Using Deep Learning to Identify High-Risk Patients with Heart Failure with Reduced Ejection Fraction

2021 ◽  
Vol 8 (2) ◽  
pp. 6-13
Author(s):  
Zhibo Wang ◽  
Xi Chen ◽  
Xi Tan ◽  
Lingfeng Yang ◽  
Kartik Kannapur ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Deep Learning ◽  
High Risk ◽  
Model Performance ◽  
Learning Models ◽  
Reduced Ejection Fraction ◽  
Patients With Heart Failure ◽  
Risk Patients ◽  
Machine Learning Models

Background: Deep Learning (DL) has not been well-established as a method to identify high-risk patients among patients with heart failure (HF). Objectives: This study aimed to use DL models to predict hospitalizations, worsening HF events, and 30-day and 90-day readmissions in patients with heart failure with reduced ejection fraction (HFrEF). Methods: We analyzed the data of adult HFrEF patients from the IBM® MarketScan® Commercial and Medicare Supplement databases between January 1, 2015 and December 31, 2017. A sequential model architecture based on bi-directional long short-term memory (Bi-LSTM) layers was utilized. For DL models to predict HF hospitalizations and worsening HF events, we utilized two study designs: with and without a buffer window. For comparison, we also tested multiple traditional machine learning models including logistic regression, random forest, and eXtreme Gradient Boosting (XGBoost). Model performance was assessed by area under the curve (AUC) values, precision, and recall on an independent testing dataset. Results: A total of 47 498 HFrEF patients were included; 9427 with at least one HF hospitalization. The best AUCs of DL models without a buffer window in predicting HF hospitalizations and worsening HF events in the total patient cohort were 0.977 and 0.972; with a 7-day buffer window the best AUCs were 0.573 and 0.608, respectively. The best AUCs in predicting 30- and 90-day readmissions in all adult patients were 0.597 and 0.614, respectively. An AUC of 0.861 was attained for prediction of 90-day readmission in patients aged 18-64. For all outcomes assessed, the DL approach outperformed traditional machine learning models. Discussion: The DL approach can automate feature engineering during the model learning, which can increase the clinical applicability and lead to comparable or better model performance. However, the lack of granular clinical data, and sample size and imbalance issues may have limited the model’s performance. Conclusions: A DL approach using Bi-LSTM was shown to be a feasible and useful tool to predict HF-related outcomes. This study can help inform the future development and deployment of predictive tools to identify high-risk HFrEF patients and ultimately facilitate targeted interventions in clinical practice.

Dual and multiple trisomies: analysis of 38 cases from 13 thousand karyotyped embryos and fetuses

2017 ◽  
pp. 109-115
Author(s):  
N.P. Veropotvelyan ◽  
Keyword(s):  
Prenatal Diagnosis ◽  
High Risk ◽  
Pregnant Women ◽  
Maternal Age ◽  
Chromosomal Abnormalities ◽  
Chorionic Villus ◽  
Primary Group ◽  
Chorionic Villus Sampling ◽  
Missed Abortion ◽  
Reproductive Losses

The study presents data of different authors, as well as its own data on the frequency of multiple trisomies among the early reproductive losses in the I trimester of pregnancy and live fetuses in pregnant women at high risk of chromosomal abnormalities (CA) in I and II trimesters of gestation. The objective: determining the frequency of occurrence of double (DT) and multiple trisomies (MT) among the early reproductive losses in the I trimester of pregnancy and live fetuses in pregnant women at high risk of occurrence of HA in I and II trimesters of gestation; establishment of the most common combinations of diesel fuel and the timing of their deaths compared with single regular trisomy; comparative assessment materinskogo age with single, double and multiple trisomies. Patients and methods. During the period from 1997 to 2016, the first (primary) group of products in 1808 the concept of missed abortion (ST) of I trimester was formed from women who live in Dnepropetrovsk, Zaporozhye, Kirovograd, Cherkasy, Kherson, Mykolaiv regions. The average term of the ST was 8±3 weeks. The average age of women was 29±2 years. The second group (control) consisted of 1572 sample product concepts received during medical abortion in women (mostly residents of Krivoy Rog) in the period of 5-11 weeks of pregnancy, the average age was 32 years. The third group was made prenatally karyotyped fruits (n = 9689) pregnant women with high risk of HA of the above regions of Ukraine, directed the Centre to invasive prenatal diagnosis for individual indications: maternal age, changes in the fetus by ultrasound (characteristic malformations and echo markers HA) and high risk of HA on the results of the combined prenatal screening I and II trimesters. From 11 th to 14 th week of pregnancy, chorionic villus sampling was performed (n=1329), with the 16th week – platsentotsentez (n=2240), 18 th and 24 th week – amniocentesis (n=6120). Results. A comparative evaluation of maternal age and the prevalence anembriony among multiple trisomies. Analyzed 13,069 karyotyped embryonic and fetal I-II trimester of which have found 40 cases of multiple trisomies – 31 cases in the group in 1808 missed abortion (2.84% of total HA), 3 cases including 1 572 induced medabortov and 7 cases during 9689 prenatal research (0.51% of HA). Determined to share the double trisomies preembrionalny, fetal, early, middle and late periods of fetal development. Conclusion. There were no significant differences either in terms of destruction of single and multiple trisomies or in maternal age or in fractions anembrionalnyh pregnancies in these groups. Key words: multiple trisomies, double trisomy, missed abortion, prenatal diagnosis.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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