University of Wisconsin
School of Medicine and Public Health

ICU Data Science Lab

ICU Data Science Lab

Majid Afshar, MD, MSCRMatthew Churpek, MD, MPH, PhD

Matthew Churpek, MD, MPH, PhD, Majid Afshar, MD, MSCR, and Anoop Mayampurath, PhD, are pulmonary and critical care physicians and clinical informaticians. The Churpek-Afshar Lab is an integrated data science laboratory, with a research focus on using electronic health record data combined with epidemiology, biostatistics and machine learning (ML) methods to improve the care of hospitalized patients.

Dr. Matthew Churpek's Faculty Biography

Dr. Majid Afshar's Faculty Biography

Dr. Anoop Mayampurath's Faculty Biography

ICU Lab members

Machine Learning at the Bedside

Up to 5% of hospitalized patients on the medical-surgical wards develop clinical deterioration requiring intensive care. To prevent clinical deterioration and keep patients out of intensive care, the Churpek-Afshar Lab uses machine learning techniques, such as natural language processing (NLP) and deep learning, to identify patients at risk for sepsis, acute kidney injury (AKI), acute respiratory distress syndrome (ARDS) and other syndromes of critical illness, as well as substance misuse.

The long-term goal of each of our initiatives is to develop and implement clinically useful algorithms and decision support tools to assist in the delivery of early, personalized care to decrease preventable death.

ICU Lab Topics

The ICU Data Science Lab uses multiple data science methods to study a variety of clinical topics.

Research Team

Data Scientist

Data Scientist

Clark Xu, HCISPP

Data Scientist

Administrative Program Specialist

Post-Doctoral Research Associate

Post-Doctoral Research Associate


ICU Lab members

Active Projects

Sepsis Early Predication and Subphenotype Illumination Study (SEPSIS)

This project uses data from the electronic health record and statistical modeling techniques to identify high-risk infected patients and important new subphenotypes of sepsis and risk-stratify patients. This work will help deliver early, life-saving care to septic patients' bedsides and lead to future interventional trials aimed at decreasing preventable death. SEPSIS is funded by the National Institutes of Health. Dr. Churpek is the PI.

  • Aim 1: To develop a novel algorithm for identifying hospitalized patients with infection.
  • Aim 2: To derive a severity of illness and organ failure prediction scores in hospitalized patients.
  • Aim 3: To determine which subgroups of infected patients benefit most from early intervention.

Funding: NIH/NIGMS, R01GM123193 (PI: Matthew Churpek) (04/15/2017 - 03/31/2022)

Related Articles

Using Machine Learning to Identify, Risk Stratify, and Guide Personalized Treatment of COVID-19 Patients

To provide life-saving interventions for patients with COVID-19, this project aims to improve the early identification and risk stratification by developing novel machine learning models to identify, risk stratify and provide personalized treatment recommendations. 

  • Aim 1: Develop novel machine learning models to identify, risk stratify, and provide personalized treatment recommendations for patients with COVID-19.
  • Aim 2: Develop a CDS tool graphical user interface (GUI) and test it in a simulation study.

Funding: DOD USAMRAA W81XWH-21-1-0009, PRMRP-Technology/Therapeutic Development Award-COVID:, (PI: Matthew Churpek) (01/31/2021 – 12/31/2024)

Related Articles

Developing a clinical decision support tool for the identification, diagnosis, and treatment of critical illness in hospitalized patients

Clinical deterioration is characterized as the physiological decompensation that occurs when a patient experiences worsening conditions or acute onset of a serious physiological disturbance. Our goal is to develop and implement a clinical decision support tool for the identification, diagnosis, and treatment of patients at high risk of deterioration to decrease preventable death. 

  • Aim 1: Develop machine learning models to identify patients at high risk of deterioration using both structured data and unstructured clinical notes.
  • Aim 2: Develop models to predict the diagnosis that is causing the deterioration event and the potentially life-saving treatments that should be provided to high-risk patients.
  • Aim 3: Develop a clinical decision support tool with a graphical user interface incorporating the models from Aims 1 and 2 via user-centered design principles and then test its effectiveness, efficiency, and user satisfaction in a case-based simulation study.

Funding: NIH/NHLBI, 1R01HL157262 (PI: Matthew Churpek) (08/01/2021 – 07/31/2025)


Related Articles


Using Machine Learning for Early Recognition and Personalized Treatment of Acute Kidney Injury

Up to 20% of hospitalized patients develop acute kidney injury, which is associated with an increased risk of readmission, morbidity, and mortality. This project will use advanced machine learning methods and biomarkers to improve the identification and treatment of patients at risk of acute kidney injury, and will result in novel tools and personalized treatment algorithms that can be implemented to improve patient outcomes. 

  • Aim 1: To develop and validate a novel AKI risk stratification tool using NLP and deep learning.
  • Aim 2: To identify the gaps in care for patients at high risk of severe AKI and their association with patient outcomes
  • Aim 3: To determine the additive value of renal biomarkers to EHR-based machine learning algorithms for detecting patients at high risk for severe AKI.

Funding: NIH/ NIDDK, 1R01DK126933-01A1 (MPIs: Jay Koyner and Matthew Churpek) (08/01/2021 –07/31/2026)


Related Articles


Data-Driven Strategies for Substance Misuse Identification in Hospitalized Patients (SMART-AI)

Substance misuse screening in hospitalized patients is inconsistent and not standardized. The goal of this project is to provide novel and critically important tools in artificial intelligence for the detection of substance misuse from the electronic health record (EHR), which would enable daily substance misuse screenings. 

  • Aim 1: Using clinical notes from the EHR, we will train and test a natural language processing (NLP) substance misuse classifier in a cohort of adult hospitalized patients.
  • Aim 2: Externally validate our NLP substance misuse classifier from Aim 1 in an independent cohort of hospitalized patients at a separate health system (UW) without current screening for substance misuse.
  • Aim 3: Evaluate the effectiveness of the substance misuse classifier in increasing the proportion of patients who receive an intervention compared to usual care (Rush), which involves interviewer administered screening.

Funding: NIH/NIDA, 1R01DA051464 (PI: Majid Afshar) (09/30/2020 – 07/31/2025)


Related Articles


Using Machine Learning to Predict Clinical Deterioration in Hospitalized Children

Children who are admitted to the hospital and experience deterioration have a high risk of mortality and poor long-term health. Current warning early scores indicating risk of deterioration are subjectively derived and have not reduced in-hospital mortality. The long-term goal is to implement a validated risk-prediction algorithm in hospitals for better detection of clinical deterioration in admitted children. This holds the promise of improving their survival and preventing long-term complications.

  • Aim 1: Develop and validate models to improve the prediction of clinical deterioration in hospitalized children using structured EHR data.
  • Aim 2: Develop and validate models predicting clinical deterioration in hospitalized children using unstructured pediatric clinical notes.
  • Aim 3: Determine the impact of hospital-level environmental factors for predicting clinical deterioration in hospitalized children.

Funding: NIH/ NHLBI  K01HL148390 (PI: Anoop Mayampurath) (07/15/2019 - 5/31/2022)


Related Articles


Building a Substance Use Data Commons for Public Health Informatics

We aim to foster an academic-public-private collaboration to build a data ecosystem that will harmonize data across a Wisconsin regional hospital, pre-hospital agencies like fire, and public health agencies for the first time. We will build a cohort with substance misuse with linked data that are engineered as an AI/ML-ready data commons. During our one-year timeline, we will train and test an AI/ML model that can prioritize those at the highest risk for poor outcomes and uncover important biases in our data sources with input by health equity experts.

  • Aim 1:  Build a Substance Misuse Data Commons across a major hospital system and Wisconsin agencies;
  • Aim 2: Develop and validate a machine learning tool for substance use-related health outcomes;
  • Aim 3: Examine model performance across health disparate groups (race/ethnic groups as well as neighborhoods).

Funding: NIH/NIDA 3R01DA051464-02S1 (PI: Majid Afshar) (8/1/2021 - 7/31/2022)


View Dr. Matthew Churpek's publications on NCBI My Bibliography

View Dr. Majid Afshar's publications on NCBI My Bibliography

View Dr. Anoop Mayampurath’s publications on NCBI My Bibliography

Software/GitHub Repositories

Yanjun Gao

Positions Available 

Full-time positions 

Medical fellow/resident positions

For questions, contact Madeline Oguss at or 608-265-2878.

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