OBJECTIVE: The study aimed to develop natural language processing (NLP) algorithms to automate extracting patient-centred breast cancer treatment outcomes from clinical notes in electronic health records (EHRs), particularly for women from under-represented populations. METHODS: The study used clinical notes from 2010 to 2021 from a tertiary hospital in the USA. The notes were processed through various NLP techniques, including vectorisation methods (term frequency-inverse document frequency (TF-IDF), Word2Vec, Doc2Vec) and classification models (support vector classification, K-nearest neighbours (KNN), random forest (RF)). Feature selection and optimisation through random search and fivefold cross-validation were also conducted. RESULTS: The study annotated 100 out of 1000 clinical notes, using 970 notes to build the text corpus. TF-IDF and Doc2Vec combined with RF showed the highest performance, while Word2Vec was less effective. RF classifier demonstrated the best performance, although with lower recall rates, suggesting more false negatives. KNN showed lower recall due to its sensitivity to data noise. DISCUSSION: The study highlights the significance of using NLP in analysing clinical notes to understand breast cancer treatment outcomes in under-represented populations. The TF-IDF and Doc2Vec models were more effective in capturing relevant information than Word2Vec. The study observed lower recall rates in RF models, attributed to the datasets imbalanced nature and the complexity of clinical notes. CONCLUSION: The study developed high-performing NLP pipeline to capture treatment outcomes for breast cancer in under-represented populations, demonstrating the importance of document-level vectorisation and ensemble methods in clinical notes analysis. The findings provide insights for more equitable healthcare strategies and show the potential for broader NLP applications in clinical settings.

Scope: Disruptions of circadian rhythm cause metabolic disorders and are closely related to dietary factors. In this study, we investigated the interplays between the dietary conjugated linoleic acid (CLA)-induced hepatic steatosis and the circadian clock regulation, in association with lipid homeostasis. Methods and Results: Exposure of mice to 1.5% dietary CLA for 28 days caused insulin resistance, enlarged livers, caused hepatic steatosis, and increased triglyceride levels. Transcriptional profiling showed that hepatic circadian clock genes were significantly downregulated with increased expression of the negative transcription factor, REV-ERBα. We uncovered that the nuclear receptor (NR) PPARα, as a major target of dietary CLA, drives REV-ERBα expression via its binding to key genes of the circadian clock, including Cry1 and Clock, and the recruitment of histone marks and cofactors. The PPARα or REV-ERBα inhibition blocked the physical connection of this NR pair, reduced the cobinding of PPARα and REV-ERBα to the genomic DNA response element, and abolished histone modifications in the CLA-hepatocytes. In addition, we demonstrated that CLA promotes PPARα driving REV-ERBα transcriptional activity by directly binding to the PPAR response element (PPRE) at the Nr1d1 gene. Conclusions: Our results add a layer to the understanding of the peripheral clock feedback loop, which involves the PPARα-REV-ERBα, and provide guidance for nutrients optimization in circadian physiology.