Machine learning applied in patient-reported outcome research-exploring symptoms in adjuvant treatment of breast cancer.

BACKGROUND: Patient-reported outcome (PRO) data may help us better understand the life of breast cancer patients. We have previously collected PRO data in a national Danish breast cancer study in patients undergoing adjuvant chemotherapy. The aim of the present post-hoc explorative study is to apply Machine Learning (ML) algorithms using permutation importance to explore how specific PRO symptoms influence nonadherence to six cycles of planned adjuvant chemotherapy in breast cancer patients. METHODS: We here investigate ePRO-data from the 347 patients. The ePRO presented 42 PROCTCAE questions on 25 symptoms. Patients completed the ePRO before each cycle of chemotherapy. Number of patients with completion of the scheduled six cycles of chemotherapy were registered. Two ML models were applied. One aimed at discovering the individual relative importance of the different questions in the dataset while the second aimed at discovering the relationships between the questions. Permutation importance was used. RESULTS: Out of 347 patients 238 patients remained in the final dataset, 15 patients dropped out. Two symptoms: aching joints and numbness/tingling, were the most important for dropout in the final dataset, each with an importance value of about 0.04. Model's average ROC-AUC-score being 0.706. In the second model a low performance score made the results very unreliable. CONCLUSION: In conclusion, this explorative data analysis using ML methodologies in an ePRO dataset from a population of women with breast cancer treated with adjuvant chemotherapy unravels that the symptoms aching joints and numbness/tingling could be important for drop out of planned adjuvant chemotherapy.

Semantic similarity metrics for image registration.

Image registration aims to find geometric transformations that align images. Most algorithmic and deep learning-based methods solve the registration problem by minimizing a loss function, consisting of a similarity metric comparing the aligned images, and a regularization term ensuring smoothness of the transformation. Existing similarity metrics like Euclidean Distance or Normalized Cross-Correlation focus on aligning pixel intensity values or correlations, giving difficulties with low intensity contrast, noise, and ambiguous matching. We propose a semantic similarity metric for image registration, focusing on aligning image areas based on semantic correspondence instead. Our approach learns dataset-specific features that drive the optimization of a learning-based registration model. We train both an unsupervised approach extracting features with an auto-encoder, and a semi-supervised approach using supplemental segmentation data. We validate the semantic similarity metric using both deep-learning-based and algorithmic image registration methods. Compared to existing methods across four different image modalities and applications, the method achieves consistently high registration accuracy and smooth transformation fields.

Convergence Analysis of the Hessian Estimation Evolution Strategy.

The class of algorithms called Hessian Estimation Evolution Strategies (HE-ESs) update the covariance matrix of their sampling distribution by directly estimating the curvature of the objective function. The approach is practically efficient, as attested by respectable performance on the BBOB testbed, even on rather irregular functions. In this article, we formally prove two strong guarantees for the (1 + 4)-HE-ES, a minimal elitist member of the family: stability of the covariance matrix update, and as a consequence, linear convergence on all convex quadratic problems at a rate that is independent of the problem instance.

Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients.

Patients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic markers of disease progression and death. From a cohort of approx. 2.6 million citizens in Denmark, SARS-CoV-2 PCR tests were performed on subjects suspected for COVID-19 disease; 3944 cases had at least one positive test and were subjected to further analysis. SARS-CoV-2 positive cases from the United Kingdom Biobank was used for external validation. The ML models predicted the risk of death (Receiver Operation Characteristics-Area Under the Curve, ROC-AUC) of 0.906 at diagnosis, 0.818, at hospital admission and 0.721 at Intensive Care Unit (ICU) admission. Similar metrics were achieved for predicted risks of hospital and ICU admission and use of mechanical ventilation. Common risk factors, included age, body mass index and hypertension, although the top risk features shifted towards markers of shock and organ dysfunction in ICU patients. The external validation indicated fair predictive performance for mortality prediction, but suboptimal performance for predicting ICU admission. ML may be used to identify drivers of progression to more severe disease and for prognostication patients in patients with COVID-19. We provide access to an online risk calculator based on these findings.

Impact of device programming on the success of the first anti-tachycardia pacing therapy: An anonymized large-scale study.

BACKGROUND: Antitachycardia pacing (ATP) is an effective treatment for ventricular tachycardia (VT). We evaluated the efficacy of different ATP programs based on a large remote monitoring data set from patients with implantable cardioverter-defibrillators (ICDs). METHODS: A dataset from 18,679 ICD patients was used to evaluate the first delivered ATP treatment. We considered all device programs that were used for at least 50 patients, leaving us with 7 different programs and a total of 32,045 episodes. We used the two-proportions z-test (α = 0.01) to compare the probability of success and the probability for acceleration in each group with the corresponding values of the default setting. RESULTS: Overall, the first ATP treatment terminated in 78.4%-97.5% of episodes with slow VT and 81.5%-91.1% of episodes with fast VT. The default setting of the ATP programs with the number of sequences S = 3 was applied to treat 30.1% of the slow and 36.6% of the fast episodes. Reducing the maximum number of sequences to S = 2 decreased the success rate for slow VT (P < 0.0001, h = 0.38), while the setting S = 4 resulted in the highest success rate of 97.5% (P < 0.0001, h = 0.27). CONCLUSION: While the default programs performed well, we found that increasing the number of sequences from 3 to 4 was a promising option to improve the overall ATP performance.

Predicting electrical storms by remote monitoring of implantable cardioverter-defibrillator patients using machine learning.

AIMS: Electrical storm (ES) is a serious arrhythmic syndrome that is characterized by recurrent episodes of ventricular arrhythmias. Electrical storm is associated with increased mortality and morbidity despite the use of implantable cardioverter-defibrillators (ICDs). Predicting ES could be essential; however, models for predicting this event have never been developed. The goal of this study was to construct and validate machine learning models to predict ES based on daily ICD remote monitoring summaries. METHODS AND RESULTS: Daily ICD summaries from 19 935 patients were used to construct and evaluate two models [logistic regression (LR) and random forest (RF)] for predicting the short-term risk of ES. The models were evaluated on the parts of the data not used for model development. Random forest performed significantly better than LR (P < 0.01), achieving a test accuracy of 0.96 and an area under the curve (AUC) of 0.80 (vs. an accuracy of 0.96 and an AUC of 0.75). The percentage of ventricular pacing and the daytime activity were the most relevant variables in the RF model. CONCLUSION: The use of large-scale machine learning showed that daily summaries of ICD measurements in the absence of clinical information can predict the short-term risk of ES.