How an alliance of tech startups, MAHA operatives, and actual medical experts made millennial women the new face of hormone therapy. Lisa Schrenk didn't know it yet, as she trudged down a dirt trail last August, but her life was about to change. She'd set out late the night before with her hiking group, scrambling up Virginia's Old Rag Mountain in darkness. They reached the peak in time to watch the sun rise over the Blue Ridge range. Afterward, the women snapped photos. In one, Schrenk gazes out over the horizon, her long dark hair pulled into a ponytail. The image is deceptively triumphant. In reality, she had been contemplating suicide. Schrenk, a longtime IT specialist for the federal government, had started setting aside belongings for friends and family, organizing her financial affairs, and clearing out her office.

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Breast cancer (BC) remains a significant global health challenge, with personalized treatment selection complicated by the disease's molecular and clinical heterogeneity. BC treatment decisions rely on various patient-specific clinical factors, and machine learning (ML) offers a powerful approach to predicting treatment outcomes. This study utilizes The Cancer Genome Atlas (TCGA) breast cancer clinical dataset to develop ML models for predicting the likelihood of undergoing chemotherapy or hormonal therapy. The models are trained using five-fold cross-validation and evaluated through performance metrics, including accuracy, precision, recall, specificity, sensitivity, F1-score, and area under the receiver operating characteristic curve (AUROC). Model uncertainty is assessed using bootstrap techniques, while SHAP values enhance interpretability by identifying key predictors. Among the tested models, the Gradient Boosting Machine (GBM) achieves the highest stable performance (accuracy = 0.7718, AUROC = 0.8252), followed by Extreme Gradient Boosting (XGBoost) (accuracy = 0.7557, AUROC = 0.8044) and Adaptive Boosting (AdaBoost) (accuracy = 0.7552, AUROC = 0.8016). These findings underscore the potential of ML in supporting personalized breast cancer treatment decisions through data-driven insights.

Breast cancer is a significant public health concern and is the leading cause of cancer-related deaths among women. Despite advances in breast cancer treatments, medication non-adherence remains a major problem. As electronic health records do not typically capture patient-reported outcomes that may reveal information about medication-related experiences, social media presents an attractive resource for enhancing our understanding of the patients' treatment experiences. In this paper, we developed natural language processing (NLP) based methodologies to study information posted by an automatically curated breast cancer cohort from social media. We employed a transformer-based classifier to identify breast cancer patients/survivors on X (Twitter) based on their self-reported information, and we collected longitudinal data from their profiles. We then designed a multi-layer rule-based model to develop a breast cancer therapy-associated side effect lexicon and detect patterns of medication usage and associated side effects among breast cancer patients. 1,454,637 posts were available from 583,962 unique users, of which 62,042 were detected as breast cancer members using our transformer-based model. 198 cohort members mentioned breast cancer medications with tamoxifen as the most common. Our side effect lexicon identified well-known side effects of hormone and chemotherapy. Furthermore, it discovered a subject feeling towards cancer and medications, which may suggest a pre-clinical phase of side effects or emotional distress. This analysis highlighted not only the utility of NLP techniques in unstructured social media data to identify self-reported breast cancer posts, medication usage patterns, and treatment side effects but also the richness of social data on such clinical questions.

Advances in AI could spare thousands of men unnecessary treatment and side effects. Men with prostate cancer which has an intermediate risk of spreading when they are given radiotherapy are currently offered six months to three years of hormone therapy at the same time. But AI, which looks for biological patterns in the cancer cells taken from individual men's biopsies at the point of diagnosis, can identify those who would not benefit from hormone therapy. This could save men from the side effects of hormone therapy including fatigue, hot flushes, reduced muscle strength, weight gain around the waist and changes to their sex life such as a low libido or problems getting an erection. Doctors now know hormone therapy, which starves tumours of the male sex hormones which fuel them, only helps some men.

Estimating causal effects from large experimental and observational data has become increasingly prevalent in both industry and research. The bootstrap is an intuitive and powerful technique used to construct standard errors and confidence intervals of estimators. Its application however can be prohibitively demanding in settings involving large data. In addition, modern causal inference estimators based on machine learning and optimization techniques exacerbate the computational burden of the bootstrap. The bag of little bootstraps has been proposed in non-causal settings for large data but has not yet been applied to evaluate the properties of estimators of causal effects. In this paper, we introduce a new bootstrap algorithm called causal bag of little bootstraps for causal inference with large data. The new algorithm significantly improves the computational efficiency of the traditional bootstrap while providing consistent estimates and desirable confidence interval coverage. We describe its properties, provide practical considerations, and evaluate the performance of the proposed algorithm in terms of bias, coverage of the true 95% confidence intervals, and computational time in a simulation study. We apply it in the evaluation of the effect of hormone therapy on the average time to coronary heart disease using a large observational data set from the Women's Health Initiative.

Software that can determine which breast cancer patients are in the most urgent need of surgery or chemotherapy has been developed by British scientists. The artificial intelligence algorithm uses data from different studies to identify patients able to go on hormone therapy rather than require urgent surgery. Scientists created the technology as a way to ensure women get the best available treatment during the COVID-19 pandemic that has seen surgeries cancelled. It uses data from multiple international trials to pick out those whose tumours are less likely to respond to hormonal drugs and found 5 per cent were at the most risk. Software that can determine which breast cancer patients are in the most urgent need of surgery or chemotherapy has been developed by British scientists.

Hormonal therapies have been found to help communication skills and social interactions in children and men with autism. When the US Food and Drug Administration recently asked people with autism and their caregivers what drugs they would find most useful, the top result was overwhelmingly something that would help with communication and socialisation behaviours, says Paulo Fontoura of the pharmaceutical firm Roche. Some people with autism take ADHD medication for help with attention, or anti-psychotics for help with aggression, but there are no drugs available to help with things like social difficulties. Now two separate studies have tested approaches that target the body's system for regulating vasopressin, a hormone known to affect social interactions. In the first study, 30 autistic children aged 6 to 12 were given a nasal spray to use daily for 4 weeks.