Chinese researchers from The Trauma Center
of Peking University People's Hospital and National Institute of Health Data
Science at Peking University are using big data to help identify trauma
patients who could experience potential adverse health events in the emergency
department through the aid of a clinical decision support system. It was
developed using a novel real-world evidence mining and evidence-based inference
method, driven by improved information storage and electronic medical records.
The researchers published their results
online on February 7 in IEEE Transactions on Systems, Man, and Cybernetics:
Systems, a journal of the Institute of Electrical and Electronics Engineers.
This is the first clinical decision support systems developed using evidential
reasoning in an emergency department setting.
Appropriate use of information
technologies, particularly clinical decision support systems, may aid clinicians
to make better clinical decisions and reduce the rate of medical errors. By
inputting clinical data of a patient, combined with available historical data,
our proposed clinical decision support system outputs a predicted belief degree
of severe trauma, including ICU admission and in-hospital death." Prof.
Baoguo Jiang, corresponding author, Director of The Trauma Center of Peking
University People's Hospital and China's National Center for Trauma Medicine
"The clinical variable signs and
symptoms may be interrelated and lead to a clinical outcome. For example, a
patient may have low level of consciousness because of the location of the
injury, or it might be related to the high body temperature". In
developing their clinical decision support system, the researchers used a
trauma dataset from the emergency department at Kailuan Hospital in China, a
hospital that has a close research collaboration with The Trauma Center of
Peking University People's Hospital. Through the dataset, the researchers
obtained the data of 1,299 trauma patients. The degree of interdependence
between clinical signs and symptoms can be calculated from historical patient
data. In the proposed clinical decision support system, the emergency room
physician supplies information about the patient, including blood pressure,
pulse rate, respiration rate, consciousness level, body temperature, age,
comorbidities, mechanism and location of injury. These clinical signs and
symptoms are then processed using an evidential reasoning rule, which compares
each piece against the evidence mined from real-world data to predict the
probability of adverse events and to optimally manage trauma patients and help
them achieve ideal outcomes, trauma patients with a high probability of being
admitted to the intensive care unit or dying in hospital need to be identified
quickly and accurately upon their arrival at a hospital.
The team found that not only did their
model prove especially useful in cases without prior expert knowledge or
clinical experiences, but that the clinical decision support system also
allowed for more accurate identification of trauma patients with adverse events
compared to other systems with traditional machine learning models.
Furthermore, the clinical decision support system works in a real-time fashion.
From a physician's input of a patient's data to generating appropriate advices,
the system works almost without any delay, which in turn helps buy trauma
patients valuable time.
Next, the researchers plan to finetune
their system and to generalize it for use in other clinical areas and
non-emergent department settings.
