The Robust EM-type Algorithms for Log-concave Mixtures of Regression Models

Different Machine Learning Approaches for Implementing Telehealth-Based Cancer Pain Management Strategies

Background: The most effective strategy for managing cancer pain remotely should be better defined. There is a need to identify those patients who require increased attention and calibrated follow-up programs. Methods: Machine learning (ML) models were developed using the data prospectively obtained from a single-center program of telemedicine-based cancer pain management. These models included random forest (RF), gradient boosting machine (GBM), artificial neural network (ANN), and the LASSO−RIDGE algorithm. Thirteen demographic, social, clinical, and therapeutic variables were adopted to define the conditions that can affect the number of teleconsultations. After ML validation, the risk analysis for more than one remote consultation was assessed in target individuals. Results: The data from 158 patients were collected. In the training set, the accuracy was about 95% and 98% for ANN and RF, respectively. Nevertheless, the best accuracy on the test set was obtained with RF (70%). The ML-based simulations showed that young age (<55 years), lung cancer, and occurrence of breakthrough cancer pain help to predict the number of remote consultations. Elderly patients (>75 years) with bone metastases may require more telemedicine-based clinical evaluations. Conclusion: ML-based analyses may enable clinicians to identify the best model for predicting the need for more remote consultations. It could be useful for calibrating care interventions and resource allocation.

Robust structured heterogeneity analysis approach for high-dimensional data

Revealing relationships between genes and disease phenotypes is a critical problem in biomedical studies. This problem has been challenged by the heterogeneity of diseases. Patients of a perceived same disease may form multiple subgroups, and different subgroups have distinct sets of important genes. It is hence imperative to discover the latent subgroups and reveal the subgroup-specific important genes. Some heterogeneity analysis methods have been proposed in the recent literature. Despite considerable successes, most of the existing studies are still limited as they cannot accommodate data contamination and ignore the interconnections among genes. Aiming at these shortages, we develop a robust structured heterogeneity analysis approach to identify subgroups, select important genes as well as estimate their effects on the phenotype of interest. Possible data contamination is accommodated by employing the Huber loss function. A sparse overlapping group lasso penalty is imposed to conduct regularization estimation and gene identification, while taking into account the possibly overlapping cluster structure of genes. This approach takes an iterative strategy in the similar spirit of K-means clustering. Simulations demonstrate that the proposed approach outperforms alternatives in revealing the heterogeneity and selecting important genes for each subgroup. The analysis of Cancer Cell Line Encyclopedia data leads to biologically meaningful findings with improved prediction and grouping stability.

An Observational Retrospective Cohort Trial on 4,828 IVF Cycles Evaluating Different Low Prognosis Patients Following the POSEIDON Criteria

Objective: To study the actual controlled ovarian stimulation (COS) management in women with suboptimal response, comparing clinical outcomes to the gonadotropins consume, considering potential role of luteinizing hormone (LH) addition to follicle-stimulating hormone (FSH). Design: Monocentric, observational, retrospective, real-world, clinical trial on fresh intra-cytoplasmic sperm injection (ICSI) cycles retrieving from 1 to 9 oocytes, performed at Humanitas Fertility Center from January 1st, 2012 to December 31st, 2015. Methods: COS protocols provided gonadotropin releasing-hormone (GnRH) agonist long, flare-up, short and antagonist. Both recombinant and urinary FSH were used for COS and LH was added according to the clinical practice. ICSI outcomes considered were: gonadotropins dosages; total, mature, injected and frozen oocytes; cumulative, transferred and frozen embryos; implantation rate; pregnancy, delivery and miscarriage rates. Outcomes were compared according to the gonadotropin regimen used during COS. Results: Our cohort showed 20.8% of low responders, defined as 1-3 oocytes retrieved and 79.2% of "suboptimal" responders, defined as 4-9 oocytes retrieved. According to recent POSEIDON stratification, cycles were divided in group 1 (6.9%), 2 (19.8%), 3 (11.7%), and 4 (61.5%). The cohort was divided in 3 groups, according to the gonadotropin's regimen. Women treated with FSH plus LH showed worst prognostic factors, in terms of age, basal FSH, AMH, and AFC. This difference was evident in suboptimal responders, whereas only AMH and AFC were different among treatment groups in low responders. Although a different result, in terms of oocytes and embryos detected, major ICSI outcomes (i.e., pregnancy and delivery rates) were similar among groups of COS treatment. Outcomes were significantly different among Poseidon groups. Implantation, pregnancy and delivery rates were significantly higher in Poseidon group 1 and progressively declined in other POSEIDON groups, reaching the worst percentage in group 4. Conclusions: In clinical practice, women with worst prognosis factors are generally treated with a combination of LH and FSH. Despite low prognosis women showed a reduced number of oocytes retrieved, the final ICSI outcome, in terms of pregnancy, is similarly among treatment group. This result suggests that the LH addition to FSH during COS could improve the quality of oocytes retrieved, balancing those differences that are evident at baseline. Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT03290911.