We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us

Download Mobile App




Researchers Find Machine Learning Can Improve Patient Care

By HospiMedica International staff writers
Posted on 07 Sep 2017
Print article
Image: Researchers developed a machine-learning approach named “ICU Intervene” to ascertain the types of treatments required for various symptoms (Photo courtesy of MIT).
Image: Researchers developed a machine-learning approach named “ICU Intervene” to ascertain the types of treatments required for various symptoms (Photo courtesy of MIT).
A team of researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has developed a machine-learning approach named “ICU Intervene” which uses large amounts of intensive-care-unit (ICU) data, including vitals, labs, notes and demographics, to ascertain the types of treatments required for various symptoms. Using “deep learning,” the system makes real-time predictions by learning from earlier ICU cases to offer suggestions for critical care, along with providing the reasoning for the decisions.

ICU Intervene makes hourly predictions of five different interventions covering various critical care needs, like breathing assistance, improving cardiovascular function, reducing blood pressure, and fluid therapy. The system extracts values from the data representing the vital signs, along with clinical notes and other data points every hour. All this data is represented with values indicating how far away a patient is from the average (for evaluating further treatment).

What is particularly notable is that ICU Intervene can also make future predictions. For instance, the model can predict if a patient will require a ventilator six hours later instead of only 30 minutes or an hour later. The researchers found that the system outperformed the earlier work done in predicting interventions and was particularly good in predicting the requirement for vasopressors. The researchers will focus on improving ICU Intervene in the future to allow for more individualized care and provide more advanced reasoning for decisions, like why the dosage of steroids can be gradually reduced for a patient, or why a procedure such as an endoscopy may be required in the case of another patient.

“The system could potentially be an aid for doctors in the ICU, which is a high-stress, high-demand environment,” according to Harini Suresh, a PhD student and lead author of the paper on ICU Intervene presented in August 2017 at the Machine Learning for Healthcare Conference held in Boston. “The goal is to leverage data from medical records to improve health care and predict actionable interventions.”

Another team of CSAIL researchers has developed “EHR Model Transfer,” an approach to facilitate the application of predictive models on an electronic health record (EHR) system, in spite of being trained on data from a different EHR system. The team used this approach to demonstrate that predictive models for mortality and prolonged length of stay can be trained on one EHR system and used to make predictions in another. The approach can be adopted across different versions of EHR platforms, using natural language processing to identify clinical concepts that are encoded differently across systems and then mapping them to a common set of clinical concepts (such as “blood pressure” and “heart rate”). For instance, in the case of a patient in one EHR platform who could be changing hospitals and would need their data transferred to a different type of platform, the EHR Model Transfer can ensure that the model will still predict aspects of that patient’s ICU visit, such as their chances of a prolonged stay or even of dying in the unit. The researchers plan to evaluate the EHR Model Transfer system on data and EHR systems from other hospitals and care settings in the future.

Related Links:
CSAIL

Gold Member
SARS‑CoV‑2/Flu A/Flu B/RSV Sample-To-Answer Test
SARS‑CoV‑2/Flu A/Flu B/RSV Cartridge (CE-IVD)
Gold Member
STI Test
Vivalytic Sexually Transmitted Infection (STI) Array
Silver Member
Wireless Mobile ECG Recorder
NR-1207-3/NR-1207-E
New
Thyroid Shield
Standard Thyroid Shield

Print article

Channels

Critical Care

view channel
Image: Researchers have developed a novel risk score for cardiovascular complications after bone marrow transplant (Photo courtesy of 123RF)

Novel Tool Predicts Cardiovascular Risks after Bone Marrow Transplantation

Every year, thousands of people undergo bone marrow transplants to potentially cure serious diseases like leukemia, lymphoma, and immune deficiency disorders. While these transplants can be lifesaving,... Read more

Surgical Techniques

view channel
Image: The Early Bird Bleed Monitoring System provides visual and audible indicators of the onset and progression of bleeding events (Photo courtesy of Saranas)

Novel Technology Monitors and Lowers Bleeding Complications in Patients Undergoing Heart Procedures

Bleeding complications at the femoral access site can significantly hamper recovery, affecting the success of procedures, patient satisfaction, and overall healthcare costs. It is crucial for surgeons... Read more

Patient Care

view channel
Image: The newly-launched solution can transform operating room scheduling and boost utilization rates (Photo courtesy of Fujitsu)

Surgical Capacity Optimization Solution Helps Hospitals Boost OR Utilization

An innovative solution has the capability to transform surgical capacity utilization by targeting the root cause of surgical block time inefficiencies. Fujitsu Limited’s (Tokyo, Japan) Surgical Capacity... Read more

Health IT

view channel
Image: First ever institution-specific model provides significant performance advantage over current population-derived models (Photo courtesy of Mount Sinai)

Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients

Machine learning algorithms have been deployed to create predictive models in various medical fields, with some demonstrating improved outcomes compared to their standard-of-care counterparts.... Read more

Point of Care

view channel
Image: The new eye-safe laser technology can diagnose traumatic brain injury (Photo courtesy of 123RF)

Novel Diagnostic Hand-Held Device Detects Known Biomarkers for Traumatic Brain Injury

The growing need for prompt and efficient diagnosis of traumatic brain injury (TBI), a major cause of mortality globally, has spurred the development of innovative diagnostic technologies.... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.