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Found 9 papers, 1 papers with code
Identifying Symptoms of Delirium from Clinical Narratives Using Natural Language Processing
To assist in the diagnosis and phenotyping of delirium, we formed an expert panel to categorize diverse delirium symptoms, composed annotation guidelines, created a delirium corpus with diverse delirium symptoms, and developed NLP methods to extract delirium symptoms from clinical notes.
DR-VIDAL -- Doubly Robust Variational Information-theoretic Deep Adversarial Learning for Counterfactual Prediction and Treatment Effect Estimation on Real World Data
DR-VIDAL integrates: (i) a variational autoencoder (VAE) to factorize confounders into latent variables according to causal assumptions; (ii) an information-theoretic generative adversarial network (Info-GAN) to generate counterfactuals; (iii) a doubly robust block incorporating treatment propensities for outcome predictions.
Variational Temporal Deconfounder for Individualized Treatment Effect Estimation from Longitudinal Observational Data
Estimating treatment effects, especially individualized treatment effects (ITE), using observational data is challenging due to the complex situations of confounding bias.
Joint Application of the Target Trial Causal Framework and Machine Learning Modeling to Optimize Antibiotic Therapy: Use Case on Acute Bacterial Skin and Skin Structure Infections due to Methicillin-resistant Staphylococcus aureus
ABSSSI-MRSA is a challenging condition with reduced treatment options - vancomycin is the preferred choice, but it has non-negligible side effects.
Assessing putative bias in prediction of anti-microbial resistance from real-world genotyping data under explicit causal assumptions
Whole genome sequencing (WGS) is quickly becoming the customary means for identification of antimicrobial resistance (AMR) due to its ability to obtain high resolution information about the genes and mechanisms that are causing resistance and driving pathogen mobility.
Integrating Crowdsourcing and Active Learning for Classification of Work-Life Events from Tweets
Results show that crowdsourcing is useful to create high-quality annotations and active learning helps in reducing the number of required tweets, although there was no substantial difference among the strategies tested.
Mining Twitter to Assess the Determinants of Health Behavior towards Human Papillomavirus Vaccination in the United States
We applied topic modeling to discover major themes, and subsequently explored the associations between the topics learned from consumers' discussions and the responses of HPV-related questions in the Health Information National Trends Survey (HINTS).
Understanding Perceptions and Attitudes in Breast Cancer Discussions on Twitter
Among American women, the rate of breast cancer is only second to lung cancer.
Inference of a Multi-Domain Machine Learning Model to Predict Mortality in Hospital Stays for Patients with Cancer upon Febrile Neutropenia Onset
Febrile neutropenia (FN) has been associated with high mortality, especially among adults with cancer.
