Tootfinder

Sonnet 122 - CXXII
Thy gift, thy tables, are within my brain
Full charactered with lasting memory,
Which shall above that idle rank remain,
Beyond all date, even to eternity:
Or, at the least, so long as brain and heart
Have faculty by nature to subsist;
Till each to razed oblivion yield his part
Of thee, thy record never can be missed.
That poor retention could not so much hold,
Nor need I tallies thy dear love to score;
Ther…

Privacy-Aware, Public-Aligned: Embedding Risk Detection and Public Values into Scalable Clinical Text De-Identification for Trusted Research Environments
Arlene Casey, Stuart Dunbar, Franz Gruber, Samuel McInerney, Mat\'u\v{s} Falis, Pamela Linksted, Katie Wilde, Kathy Harrison, Alison Hamilton, Christian Cole
https://arxiv.…

Privacy-Aware, Public-Aligned: Embedding Risk Detection and Public Values into Scalable Clinical Text De-Identification for Trusted Research Environments
Clinical free-text data offers immense potential to improve population health research such as richer phenotyping, symptom tracking, and contextual understanding of patient care. However, these data present significant privacy risks due to the presence of directly or indirectly identifying information embedded in unstructured narratives. While numerous de-identification tools have been developed, few have been tested on real-world, heterogeneous datasets at scale or assessed for governance read…

Query, Don't Train: Privacy-Preserving Tabular Prediction from EHR Data via SQL Queries
Josefa Lia Stoisser, Marc Boubnovski Martell, Kaspar M\"artens, Lawrence Phillips, Stephen Michael Town, Rory Donovan-Maiye, Julien Fauqueur
https://arxiv.org/abs/2505.21801

Query, Don't Train: Privacy-Preserving Tabular Prediction from EHR Data via SQL Queries
Electronic health records (EHRs) contain richly structured, longitudinal data essential for predictive modeling, yet stringent privacy regulations (e.g., HIPAA, GDPR) often restrict access to individual-level records. We introduce Query, Don't Train (QDT): a structured-data foundation-model interface enabling tabular inference via LLM-generated SQL over EHRs. Instead of training on or accessing individual-level examples, QDT uses a large language model (LLM) as a schema-aware query planner to g…

Got this "Little Lime" #hydrangea planted to replace a 40-something-year-old azalea bush that died off. It's healthy now, let's see if I don't fuck it up. I've had good luck with hydrangeas as long as they like the spot they're in.
The #Comcast drop runs through here,…

This https://arxiv.org/abs/2505.21801 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

Query, Don't Train: Privacy-Preserving Tabular Prediction from EHR Data via SQL Queries
Electronic health records (EHRs) contain richly structured, longitudinal data essential for predictive modeling, yet stringent privacy regulations (e.g., HIPAA, GDPR) often restrict access to individual-level records. We introduce Query, Don't Train (QDT): a structured-data foundation-model interface enabling tabular inference via LLM-generated SQL over EHRs. Instead of training on or accessing individual-level examples, QDT uses a large language model (LLM) as a schema-aware query planner to g…

In silico tool for identification of colorectal cancer from cell-free DNA biomarkers
Kartavya Mathur (Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, School of Biotechnology, Gautam Buddha University, Uttar Pradesh), Shipra Jain (Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi), Nisha Bajiya (Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi), Nishant Ku…

In silico tool for identification of colorectal cancer from cell-free DNA biomarkers
Colorectal cancer remains a major global health concern, with early detection being pivotal for improving patient outcomes. In this study, we leveraged high throughput methylation profiling of cellfree DNA to identify and validate diagnostic biomarkers for CRC. The GSE124600 study data were downloaded from the Gene Expression Omnibus, as the discovery cohort, comprising 142 CRC and 132 normal cfDNA methylation profiles obtained via MCTA seq. After preprocessing and filtering, 97,863 CpG sites w…