Release date: 2017-08-30 The Massachusetts Institute of Technology's computer science and artificial intelligence laboratories use a variety of medical data, including electronic health data records, to predict medical conditions. The two teams created “ICU Intervention†and “EHR Model Migration†machine learning methods to improve patient care conditions. Source: billion euros
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Doctors are often plagued by the need to look at various charts, test results, and other indicators. It is very difficult to make real-time treatment decisions while integrating and monitoring multiple patient data, especially when the data records between hospitals are inconsistent, the challenges are even greater.
Researchers at the Massachusetts Institute of Technology's Computer Science and Artificial Intelligence Laboratory (CSAIL) explored how a computer could help doctors make better medical decisions in a new article.
Among them, a team created a machine learning method called ICU Intervene, which requires a large amount of intensive care unit (ICU) data, including human vital signs, laboratory data. , notes, and demographic data to determine what treatments are needed for different symptoms. The system uses “deep learning†techniques for real-time predictions, learning from past ICU cases to advise on intensive care and explaining the reasons for these decisions.
Dr. Harini Suresh, lead author of the ICU intervention article, said: "The system may help doctors who are on standby in the ICU because it is a highly stressful, high-demand environment. The goal is to use data from medical records. Improve medical conditions and make predictions about possible interventions."
Another team developed the “EHR Model Migration†approach, which provides systematic training for processing data from different EHR systems and can be used to help apply predictive models to Electronic Health Record (EHR) systems. Specifically, using this research team's approach, a predictive model of mortality data and extended dwell time can be trained on an EHR system, and the resulting model can be migrated for prediction in another EHR system.
The ICU intervention was jointly developed by Dr. Suresh, Nathan Hunt, Dr. Alistair Johnson, Leo Anthony Celi, MIT professor Peter Szolovits, and doctoral student Marzyeh Ghassemi, and presented this month at the Boston Medical Machine Learning Conference.
The EHR model transfer was developed by CSAIL's PhD students Jen Gong and Tristan Naumann, as well as Szolovits and electrical engineering professor John Guttag. It was first proposed at the ACM Knowledge Discovery and Data Mining Special Interest Group in Halifax, Canada.
Both models were trained using data from the key care database MIMIC, which included de-identification data from approximately 40,000 intensive care patients and was developed by the Massachusetts Institute of Technology's Computational Physiology Laboratory.
Intensive care unit (ICU) intervention
Integrating ICU data is critical to automating the process of predicting patient health outcomes.
Suresh said: “Previously, much of the work in clinical decision-making focused on outcomes such as mortality, and the emergence of this work was to predict possible treatments. In addition, the system was able to predict multiple outcomes using a single model.â€
The ICU intervention focuses on predicting five key measures in hours, covering a variety of key care needs, such as respiratory assistance, improved cardiovascular function, lowering blood pressure, and infusion therapy.
At hourly, the system extracts values ​​from data representing vital signs and clinical notes and other data points. All data are expressed as values, indicating how far the patient is from the mean (and then evaluating further treatment).
Importantly, ICU intervention can make predictions about the future. For example, the model predicts whether a patient needs a ventilator after 6 hours, not just a patient who needs to use the ventilator after 30 minutes or an hour. The team also focused on providing reasoning for model predictions and providing doctors with more insights.
Nigam Shah, an associate professor of medicine at Stanford University, said, "Deep neural prediction models based on neural networks are often criticized for their machine identity. However, these authors highly accurately predict the beginning and end of medical interventions. And can actually confirm the interpretability of the predictions it makes."
The team found that the system is superior to previous interventions in predicting interventions and is particularly good at predicting the need for vasopressin, a drug used to tighten blood vessels and increase blood pressure.
In the future, researchers will work to improve ICU interventions in order to provide patients with more personalized care and to provide more advanced predictions for decision making, such as why one patient is likely to gradually reduce steroids, or why another patient may need to do so. Endoscopy.
EHR model migration
Another important consideration when using ICU data is how it is stored and what might happen when the storage method changes. Existing machine learning models need to encode data in a consistent manner, so hospitals often changing their EHR systems can pose significant problems for data analysis and forecasting.
This is where the EHR model migrates. This method is applicable to different versions of the EHR platform, using natural language processing to identify clinical information encoded across systems and then mapping it to common clinical information such as "blood pressure" and "heart rate".
For example, a patient in an EHR platform may be switching hospitals and need to transfer their data to different types of platforms. The EHR model migration is designed to ensure that the model maintains predictability of the patient's condition, such as the patient's long-term stay, or the likelihood of death.
Shah said: "The machine learning models used in medical treatment often have the disadvantages of low external efficiencies and poor portability between sites. These authors have devised a sophisticated strategy for using acquired knowledge in medical ontology. , thereby resulting in a mutually-recognized expression between the two websites that can help the model perform well on another website after being trained on one website. It can be seen that such creative use of coded medical knowledge to enhance the predictive model I am very excited about portability."
Using the EHR model migration, the team tested its model's ability to predict both outcomes: mortality and long-term hospitalization needs. They trained the model on an EHR platform and tested its predictions on different platforms. The EHR model migration was found to be superior to the common method, and the EHR prediction model was better able to migrate data than when using EHR-specific events alone.
In the future, the EHR Model Migration Team plans to evaluate data and EHR systems from other hospitals and care facilities.
This article is from MIT News, compiled by billion euros, compiled by Zeng Duo.
