Acute kidney injury (AKI) — a condition in which the kidneys suddenly fail to filter waste from the blood — can devastate the renal system of critically ill patients. The mortality rate can approach 89 percent if it progresses beyond stage 2 (AKI is categorized into three stages). And if AKI develops after major abdominal surgery, the risk of death is increased 12-fold.
Fortunately, rogress has been made toward techniques that aid in early detection. A paper published by researchers at Northwestern University and the University of Texas Health Science Center (“Early Prediction of Acute Kidney Injury in Critical Care Setting Using Clinical Notes“) describes an artificially intelligent (AI) system that can collect and extract risk factors from electronic health records (EHRs), and predict the liklihood of AKI within the first 24 hours following intensive care unit (ICU).
“We developed data-driven prediction models to estimate the risk of new AKI onset,” the researchers wrote. “From a practical point of view, our prediction model could be used to alert clinicians of critically ill patients at high risk for developing AKI soon after ICU admission.”
To train the AI system, the team sourced records from Medical Information Mart for Intensive Care III (MIMIC-III), a freely available critical care database containing the anonymized health information of over 40,000 patients who stayed in ICUs of the Beth Israel Deaconess Medical Center. They developed a script that scraped age, gender, race and ethnicity, and clinical notes during the first day of ICU admission and 72-hour serum creatinine levels (a common measure of toxicity in urine) after admission, and that excluded patients without physician notes and signs of kidney dysfunction
Altogether, they compiled 77,160 clinical notes from 14,1470 patients’ 16,560 ICU stays, which they split into two sets: one for training and another for testing. Then, they set about building a machine learning model.
Some preprocessing of the data was required to arrive at structured features, some of which involved tapping the National Library of Medicine’s freely available MetaMap toolset to identify medical concepts from free text clinical notes. Extracted features came in the form of Concept Unique Identifiers (CUIs) — concepts associated with words and terms — from Unified Medical Language System (UMLS), a comprehensive compendium of biomedical terms and classifications.
Five algorithms were used to classify the ICU stays and estimate AKI risk from scikit-learn, an open-source machine learning library for the Python programming language. In testing, the researchers’ supervised learning classifier achieved 0.779 area under the receiver operating characteristic (AUC), meaning it was able to identify patients at risk of developing AKI more than 50 percent of the time and with precision “competitive” with previous methods.
Still, it wasn’t perfect. It incorrectly flagged AKI onset in a patient whose chart contained highly associative words such as “chest tube” and “labile.” And in another case, it failed to predict AKI in a patient who later developed it. (In the latter case, they noted that there weren’t enough patients in the dataset with similar conditions.)
The researchers leave to future work investigation of alternative phenotyping systems, clinical notes databases, and validation on additional patient datasets.
Antother party of note applying AI to AKI detection is Google subsidiary DeepMind, which announced in February a partnership with the U.S. Department of Veterans Affairs that saw it gain access to more than 700,000 medical records.
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