The CONCERN project aims to develop models and tools to quantify clinician concern about patient deterioration in the inpatient setting that can be used in early warning scores. We have discovered and validated several measurable ways within the Electronic Health Record (EHR) to measure clinician concern and have demonstrated that our approach identified patients at risk of deterioration earlier than other methods, which focus only on physiological data. One of our approaches is leveraging documentation of certain concepts within narrative text in nursing notes that are consistent with concern about a patient. However, this narrative free text is not easily accessible - it is often mixed together with structured or templated text and varies over note types. The steps to be performed are

Continue reading

This project works with a novel corpus of text-based school data to develop a multi-dimensional measure of the degree to which American colleges and universities offer a liberal arts education. We seek a data scientist for various tasks on a project that uses analysis of multiple text corpora to better understand the liberal arts. This is an ongoing three-year project with opportunities for future collaborations, academic publications, and developing and improving existing data science and machine learning skills. Tasks likely include: (1) Using Amazon Web Services to create and maintain cloud-based storage (SQL, S3 buckets) of the project’s expanding library of data. (2) Extracting information (named entities, times, places, books, and so on) from millions of plain-text syllabus records. (3) Merging multiple forms of data into a single dataset. (4) Scraping websites for relevant information (e.g., college course offerings, school rankings). Some pages may include dynamically created content that requires the use of a program such as Selenium.

Continue reading

Our lab is using clinical notes to phenotype COVID patient outcomes. The aim is to better understand the sequela of COVID-19 from clinical notes.

Continue reading

The objective of this project is to construct linkages across disparate public health data systems using machine learning tools and assess them for bias and equitable representation of subpopulations defined by demographic and socioeconomic factors.

Continue reading

This project works with a novel corpus of text-based school data to develop a multi-dimensional measure of the degree to which American colleges and universities offer a liberal arts education. We seek a data scientist for various tasks on a project that uses analysis of multiple text corpora to better understand the liberal arts. This is an ongoing three-year project with opportunities for future collaborations, academic publications, and developing and improving existing data science and machine learning skills. Tasks likely include: (1) Using Amazon Web Services to create and maintain cloud-based storage (SQL, S3 buckets) of the project’s expanding library of data. (2) Extracting information (named entities, times, places, books, and so on) from millions of plain-text syllabus records. (3) Merging multiple forms of data into a single dataset. (4) Scraping websites for relevant information (e.g., college course offerings, school rankings). Some pages may include dynamically created content that requires the use of a program such as Selenium.

Continue reading

Our lives are heavily reliant on Internet-connected devices and services. However, to deliver the desired user experience over the Internet, network operators need to detect and diagnose various network events (e.g., disruption, outage, misconfiguration, etc.) as well as resolve them in real-time. We have developed an Internet-wide measurement infrastructure that collects performance metrics (e.g., latency, jitter, throughput, packet loss rate, signal strength, etc.) from vantage points deployed by real users (mobile phones, WiFi access points, etc.) at regular intervals.

Continue reading

Data is central to the NYC Department of Health’s mission to protect and promote the health of all New Yorkers. The agency’s many programs often require large scale record linkages that integrate data from individuals across multiple public health data systems and disease registries. We are implementing a Master Person Index (MPI) system in order to centralize, optimize and standardize matching methodology for administrative data across the Department of Health.

Continue reading