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Delirium occurrence is common and preventive strategies are resource intensive. Screening tools can prioritize patients at risk. Using machine learning, we can capture time and treatment effects that pose a challenge to delirium prediction. We aim to develop a delirium prediction model that can be used as a screening tool.

To summarize applications of natural language processing (NLP) in model informed drug development (MIDD) and identify potential areas of improvement.

Scanned documents in electronic health records (EHR) have been a challenge for decades, and are expected to stay in the foreseeable future. Current approaches for processing include image preprocessing, optical character recognition (OCR), and natural language processing (NLP). However, there is limited work evaluating the interaction of image preprocessing methods, NLP models, and document layout.

To determine whether a machine learning model can detect SARS-CoV-2 infection from physiological metrics collected from wearable devices.

This paper provides a description of the MyCap data collection platform, utilization metrics, and vignettes associated with use from diverse research institutions. MyCap is a participant-facing mobile application for survey data collection and the automated administration of active tasks (activities performed by participants using mobile device sensors under semi-controlled conditions). Launched in 2018, MyCap is a no-code solution for research teams conducting longitudinal studies, integrates tightly with REDCap and is [...]

Simplifying healthcare text to improve understanding is difficult but critical to improve health literacy. Unfortunately, few tools exist that have been shown objectively to improve text and understanding. We developed an online editor that integrates simplification algorithms that suggest concrete simplifications, all of which have been shown individually to affect text difficulty.

Predicting Coronavirus disease 2019 (COVID-19) mortality for patients is critical for early-stage care and intervention. Existing studies mainly built models on datasets with limited geographical range or size. In this study, we developed COVID-19 mortality prediction models on worldwide, large-scale "sparse" data and on a "dense" subset of the data.

To assess the overlap of information between electronic health record (EHR) and patient-nurse verbal communication in home healthcare (HHC).

The identification of delirium in electronic health records (EHRs) remains difficult due to inadequate assessment or under-documentation. The purpose of this research is to present a classification model that identifies delirium using retrospective EHR data. Delirium was confirmed with the Confusion Assessment Method for the Intensive Care Unit. Age, sex, Elixhauser comorbidity index, drug exposures, and diagnoses were used as features. The model was developed based on the Columbia University [...]

To support development of a robust postmarket device evaluation system using real-world data (RWD) from electronic health records (EHRs) and other sources, employing unique device identifiers (UDIs) to link to device information.