Backdoor Adjustment of Confounding by Provenance for Robust Text Classification of Multi-institutional Clinical Notes.

Natural Language Processing (NLP) methods have been broadly applied to clinical tasks. Machine learning and deep learning approaches have been used to improve the performance of clinical NLP. However, these approaches require sufficiently large datasets for training, and trained models have been shown to transfer poorly across sites.

A flexible symbolic regression method for constructing interpretable clinical prediction models.

Machine learning (ML) models trained for triggering clinical decision support (CDS) are typically either accurate or interpretable but not both. Scaling CDS to the panoply of clinical use cases while mitigating risks to patients will require many ML models be intuitively interpretable for clinicians. To this end, we adapted a symbolic regression method, coined the feature engineering automation tool (FEAT), to train concise and accurate models from high-dimensional electronic health record (EHR) data.

Predicting Drug Blood-Brain Barrier Penetration with Adverse Event Report Embeddings.

Adverse event reports (AER) are widely used for post-market drug safety surveillance and drug repurposing, with the assumption that drugs with similar side-effects may have similar therapeutic effects also. In this study, we used distributed representations of drugs derived from the Food and Drug Administration (FDA) AER system using aer2vec, a method of representing AER, with drug embeddings emerging from a neural network trained to predict the probability of adverse drug effects given observed drugs. We combined these representations with molecular features to predict permeability of the blood-brain barrier to drugs, a prerequisite to their application to treat conditions of the central nervous system.

Augmenting aer2vec: Enriching distributed representations of adverse event report data with orthographic and lexical information.

Adverse Drug Events (ADEs) are prevalent, costly, and sometimes preventable. Post-marketing drug surveillance aims to monitor ADEs that occur after a drug is released to market. Reports of such ADEs are aggregated by reporting systems, such as the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS).

Retrofitting Vector Representations of Adverse Event Reporting Data to Structured Knowledge to Improve Pharmacovigilance Signal Detection.

Adverse drug events (ADE) are prevalent and costly. Clinical trials are constrained in their ability to identify potential ADEs, motivating the development of spontaneous reporting systems for post-market surveillance. Statistical methods provide a convenient way to detect signals from these reports but have limitations in leveraging relationships between drugs and ADEs given their discrete count-based nature.

A maximum likelihood approach to electronic health record phenotyping using positive and unlabeled patients.

Objective: Phenotyping patients using electronic health record (EHR) data conventionally requires labeled cases and controls. Assigning labels requires manual medical chart review and therefore is labor intensive. For some phenotypes, identifying gold-standard controls is prohibitive.

Tracing Tumor Evolution in Sarcoma Reveals Clonal Origin of Advanced Metastasis.

Cellular heterogeneity is frequently observed in cancer, but the biological significance of heterogeneous tumor clones is not well defined. Using multicolor reporters and CRISPR-Cas9 barcoding, we trace clonal dynamics in a mouse model of sarcoma. We show that primary tumor growth is associated with a reduction in clonal heterogeneity.

Designing risk prediction models for ambulatory no-shows across different specialties and clinics.

Objective: As available data increases, so does the opportunity to develop risk scores on more refined patient populations. In this paper we assessed the ability to derive a risk score for a patient no-showing to a clinic visit.

Methods: Using data from 2 264 235 outpatient appointments we assessed the performance of models built across 14 different specialties and 55 clinics.

An analogous wood barrel theory to explain the occurrence of hormesis: A case study of sulfonamides and erythromycin on Escherichia coli growth.

Hormesis has aroused much attention during the past two decades and may have great implications on many fields, including toxicology and risk assessment. However, the observation of hormesis remains challenged under laboratory conditions. To determine favorable conditions under which to observe hormesis, we investigated the hormetic responses of Escherichia coli (E.

The Comparison of the Combined Toxicity between Gram-negative and Gram-positive Bacteria: a Case Study of Antibiotics and Quorum-sensing Inhibitors.

Quorum-sensing inhibitors (QSIs) are being used increasingly in diverse fields, and are likely to end up in the environment, where they may encounter the antibiotics and consequently cause joint effects on biological systems. However, the potential joint effects of QSIs and antibiotics have received little attention. In this study, the joint effects of antibiotics, represented by sulfonamides (SAs) and penicillin, as well as three potential QSIs, were investigated using both Gram-negative (Escherichia coli, E.