scholarly journals Simulator-generated training datasets as an alternative to using patient data for machine learning: An example in myocardial segmentation with MRI

2021 ◽  
Vol 198 ◽  
pp. 105817
Author(s):  
Christos G. Xanthis ◽  
Dimitrios Filos ◽  
Kostas Haris ◽  
Anthony H. Aletras
Keyword(s):  
Machine Learning ◽  
Patient Data ◽  
Myocardial Segmentation

Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer.

2020 ◽  
Vol 21 ◽  
Author(s):  
Sukanya Panja ◽  
Sarra Rahem ◽  
Cassandra J. Chu ◽  
Antonina Mitrofanova
Keyword(s):  
Machine Learning ◽  
Missing Values ◽  
Therapeutic Response ◽  
Patient Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Complex Data ◽  
Human Machine Interaction ◽  
Data Repositories ◽  
Response Modeling

Background: In recent years, the availability of high throughput technologies, establishment of large molecular patient data repositories, and advancement in computing power and storage have allowed elucidation of complex mechanisms implicated in therapeutic response in cancer patients. The breadth and depth of such data, alongside experimental noise and missing values, requires a sophisticated human-machine interaction that would allow effective learning from complex data and accurate forecasting of future outcomes, ideally embedded in the core of machine learning design. Objective: In this review, we will discuss machine learning techniques utilized for modeling of treatment response in cancer, including Random Forests, support vector machines, neural networks, and linear and logistic regression. We will overview their mathematical foundations and discuss their limitations and alternative approaches all in light of their application to therapeutic response modeling in cancer. Conclusion: We hypothesize that the increase in the number of patient profiles and potential temporal monitoring of patient data will define even more complex techniques, such as deep learning and causal analysis, as central players in therapeutic response modeling.

scholarly journals ARPNet: Antidepressant Response Prediction Network for Major Depressive Disorder

Genes ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 907 ◽  
Author(s):  
Buru Chang ◽  
Yonghwa Choi ◽  
Minji Jeon ◽  
Junhyun Lee ◽  
Kyu-Man Han ◽  
...  
Keyword(s):  
Machine Learning ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Prediction Model ◽  
Response Prediction ◽  
Clinical Decision ◽  
Patient Data ◽  
Antidepressant Response ◽  
Major Depressive ◽  
Antidepressant Prescription

Treating patients with major depressive disorder is challenging because it takes several months for antidepressants prescribed for the patients to take effect. This limitation may result in increased risks and treatment costs. To address this limitation, an accurate antidepressant response prediction model is needed. Recently, several studies have proposed models that extract useful features such as neuroimaging biomarkers and genetic variants from patient data, and use them as predictors for predicting the antidepressant responses of patients. However, it is impossible to utilize all the different types of predictors when making a clinical decision on what drugs to prescribe for a patient. Although a machine learning-based antidepressant response prediction model has been proposed to overcome this problem, the model cannot find the most effective antidepressant for a patient. Based on a neural network, we propose an Antidepressant Response Prediction Network (ARPNet) model capturing high-dimensional patterns from useful features. Based on a literature survey and data-driven feature selection, we extract useful features from patient data, and use the features as predictors. In ARPNet, the patient representation layer captures patient features and the antidepressant prescription representation layer captures antidepressant features. Utilizing the patient and antidepressant prescription representation vectors, ARPNet predicts the degree of antidepressant response. The experimental evaluation results demonstrate that our proposed ARPNet model outperforms machine learning-based models in predicting antidepressant response. Moreover, we demonstrate the applicability of ARPNet in downstream applications in use case scenarios.

scholarly journals Classification of influenza H1N1 and COVID-19 patient data using machine learning

2021 ◽  
Author(s):  
Ersin Elbasi ◽  
Aymen Zreikat ◽  
Shinu Mathew ◽  
Ahmet E. Topcu
Keyword(s):  
Machine Learning ◽  
Patient Data ◽  
Influenza H1n1

scholarly journals Machine learning-based PRediction of ICU DElirium (PRIDE) Model for Delirium Prediction in Intensive Care Units: A Retrospective Study (Preprint)

2020 ◽  
Author(s):  
Sujeong Hur ◽  
Ryoung-Eun Ko ◽  
Junsang Yoo ◽  
Juhyung Ha ◽  
Won Chul Cha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Electronic Health Record ◽  
Patient Data ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Icu Admission ◽  
Record Data ◽  
Electronic Health ◽  
Icu Delirium

BACKGROUND Delirium occurs frequently among patients admitted to intensive care unit (ICU). There is only limited evidence to support interventions to treat or resolve delirium in patients who have already developed delirium. Therefore, the early recognition and prevention of delirium is important in the management of critically ill patients. OBJECTIVE This study aimed to develop and validate the PRIDE (PRediction of ICU DElirium) model with machine learning using electronic health record data for delirium prediction within 24 hours from ICU admission. METHODS This is a retrospective cohort study performed at a tertiary referral hospital with 120 ICU beds. Machine learning-based PRIDE (PRediction of ICU DElirium) models were developed using patient data from the first 2 years of the study period and validated using patient data from the last 6 months. eXtreme Gradient Boosting (XGBoost), random forest (RF), deep neural network (DNN), and logistic regression (LR) were used. The PRIDE model was externally validated using MIMIC-III data. RESULTS We only included patients who were 18 years or older at the time of admission and who stayed in the medical or surgical ICU. A total of 37,543 cases were collected. After patient exclusion, 12,409 remained as our study population, of which 3,816 (30.8%) patients experienced delirium incidents during the study period. The MIMIC-3 dataset, based on the exclusion criteria, out of the 96,016 ICU admission cases, 2,061 cases were included, and 272 (13.2%) delirium incidents occurred. In the internal validation, the area under the receiver operating characteristics (AUROC) for XGBoost, RF, DNN, and LR was 0.919 (95% CI 0.919–0.919), 0.916 (95% CI 0.916–0.916), 0.881 (95% CI 0.878–0.884), and 0.875 (95% CI 0.875–0.875), respectively. Regarding the external validation, the best AUROC was 0.721 (95% CI 0.72–0.721), 0.697 (95% CI 0.695–0.699), 0.655 (95% CI 0.654–0.657), and 0.631 (95% CI 0.631–0.631) for RF, XGBoost, DNN, and LR, respectively. The Brier score of the XGBoost model is 0.094, indicating that it is well calibrated. CONCLUSIONS A machine learning approach based on electronic health record data can be used to predict delirium within 24 hours of ICU admission. CLINICALTRIAL N/A

scholarly journals Using machine learning to guide targeted and locally-tailored empiric antibiotic prescribing in a children’s hospital in Cambodia

2018 ◽  
Author(s):  
Mathupanee Oonsivilai ◽  
Mo Yin ◽  
Nantasit Luangasanatip ◽  
Yoel Lubell ◽  
Thyl Miliya ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Patient Outcomes ◽  
Empiric Therapy ◽  
Patient Data ◽  
Machine Learning Algorithms ◽  
List Type ◽  
Children's Hospital ◽  
Empiric Antibiotic ◽  
Children’S Hospital

AbstractBackgroundEarly and appropriate empiric antibiotic treatment of patients suspected of having sepsis is associated with reduced mortality. The increasing prevalence of antimicrobial resistance risks eroding the benefits of such empiric therapy. This problem is particularly severe for children in developing country settings. We hypothesized that by applying machine learning approaches to readily collected patient data, it would be possible to obtain actionable and patient-specific predictions for antibiotic-susceptibility. If sufficient discriminatory power can be achieved, such predictions could lead to substantial improvements in the chances of choosing an appropriate antibiotic for empiric therapy, while minimizing the risk of increased selection for resistance due to use of antibiotics usually held in reserve.Methods and FindingsWe analyzed blood culture data collected from a 100-bed children’s hospital in North-West Cambodia between February 2013 and January 2016. Clinical, demographic and living condition information for each child was captured with 35 independent variables. Using these variables, we used a suite of machine learning algorithms to predict Gram stains and whether bacterial pathogens could be treated with standard empiric antibiotic therapies: i) ampicillin and gentamicin; ii) ceftriaxone; iii) at least one of the above.243 cases of bloodstream infection were available for analysis. We used 195 (80%) to train the algorithms, and 48 (20%) for evaluation. We found that the random forest method had the best predictive performance overall as assessed by the area under the receiver operating characteristic curve (AUC), though support vector machine with radial kernel had similar performance for predicting Gram stain and ceftriaxone susceptibility. Predictive performance of logistic regression, simple and boosted decision trees and k-nearest neighbors were poor in comparison. The random forest method gave an AUC of 0.91 (95%CI 0.81-1.00) for predicting susceptibility to ceftriaxone, 0.75 (0.60-0.90) for susceptibility to ampicillin and gentamicin, 0.76 (0.59-0.93) for susceptibility to neither, and 0.69 (0.53-0.85) for Gram stain result. The most important variables for predicting susceptibility were time from admission to blood culture, patient age, hospital versus community-acquired infection, and age-adjusted weight score.ConclusionsApplying machine learning algorithms to patient data that are readily available even in resource-limited hospital settings can provide highly informative predictions on susceptibilities of pathogens to guide appropriate empiric antibiotic therapy. Used as a decision support tool, such approaches have the potential to lead to better targeting of empiric therapy, improve patient outcomes and reduce the burden of antimicrobial resistance.Author summaryWhy was this study done?Early and appropriate antibiotic treatment of patients with life-threatening bacterial infections is thought to reduce the risk of mortality.In hospitals that have a microbiology laboratory, it takes 3-4 days to get results which indicate which antibiotics are likely to be effective; before this information is available antibiotics have to be prescribed empirically i.e. without knowledge of the causative organism.Increasing resistance to antibiotics amongst bacteria makes finding an appropriate antibiotic to use empirically difficult; this problem is particularly severe for children in developing country settings.If we could predict which antibiotics were likely to be effective at the time of starting antibiotic therapy, we might be able to improve patient outcomes and reduce resistance.What Did the Researchers Do and Find?We evaluated the ability of a number of different algorithms (i.e. sets of step-by-step instructions) to predict susceptibility to commonly-used antibiotics using routinely available patient data from a children’s hospital in Cambodia.We found that an algorithm called random forests enabled surprisingly accurate predictions, particularly for predicting whether the infection was likely to be treatable with ceftriaxone, the most commonly used empiric antibiotic at the study hospital.Using this approach it would be possible to correctly predict when a different antibiotic would be needed for empiric treatment over 80% of the time, while recommending a different antibiotic when ceftriaxone would suffice less than 20% of the time.What Do These Findings Mean?Using readily available patient information, sophisticated algorithms can enable good predictions of whether antibiotics are likely to be effective several days before laboratory tests are available.Algorithms would need to be trained with local hospital data, but our study shows that even with relatively limited data from a small hospital, good predictions can be obtained.Used as part of a decision support system such algorithms could help choose appropriate antibiotics for empiric therapy; this would be expected to translate into better patient outcomes and may help to reduce resistance.Such as a decision support system would have very low costs and be easy to implement in low- and middle-income countries.

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