Abstract
BackgroundClinical decision support systems assist physicians in interpreting complex patient data. However, they typically operate on a per-patient basis and do not exploit the extensive latent medical knowledge in electronic health records (EHRs). The emergence of large EHR systems offers the opportunity to integrate population information actively into these tools.MethodsHere, we assess the ability of a large corpus of electronic records to predict individual discharge diagnoses. We present a method that exploits similarities between patients along multiple dimensions to predict the eventual discharge diagnoses.ResultsUsing demographic, initial blood and electrocardiography measurements, as well as medical history of hospitalized patients from two independent hospitals, we obtained high performance in cross-validation (area under the curve >0.88) and correctly predicted at least one diagnosis among the top ten predictions for more than 84% of the patients tested. Importantly, our method provides accurate predictions (>0.86 precision in cross validation) for major disease categories, including infectious and parasitic diseases, endocrine and metabolic diseases and diseases of the circulatory systems. Our performance applies to both chronic and acute diagnoses.ConclusionsOur results suggest that one can harness the wealth of population-based information embedded in electronic health records for patient-specific predictive tasks.
Highlights
Clinical decision support systems assist physicians in interpreting complex patient data
The inference framework Our objective was to test whether a minimal amount of patient information, available upon admission in Electronic health records (EHR), can be integrated with a background corpus of previous patients to infer the patient’s primary discharge International Classification of Diseases (ICD) codes
To ensure that our method is not limited to detecting only chronic patients, which we defined as ones for whom the discharge diagnosis appears in their medical history, we verified that we achieve a similar performance when applying our method to a set of 9,990 USA or 5,838 ISR hospitalizations which include only non-chronic cases (Table 1)
Summary
Clinical decision support systems assist physicians in interpreting complex patient data They typically operate on a per-patient basis and do not exploit the extensive latent medical knowledge in electronic health records (EHRs). The vision of automatic systems assisting and supporting clinical decisions produced a plethora of clinical decision support systems [1-4], including diagnostic decision support systems for inferring patient diagnosis These methods typically focus on a single patient and apply manually or automatically constructed decision rules to produce a diagnosis [2,5,6]. Several methods have been released for predicting certain patient outcomes using large cohorts of patients Two such examples are the detection of heart failure more than six months before the actual date of clinical diagnosis [12] and inference of patient prognosis based on patient similarities [13]. These methods, use the patient diagnosis for the learning task
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