A symptom-based community-weighted similarity approach for inpatient health condition monitoring

Given a patient’s series of exams conducted over time, how can we identify cases with similar abnormalities or symptoms? Hospitals and medical facilities continuously monitor patients through periodic exams, a crucial practice for assessing their current condition and potential progression, thereby supporting decision-making. However, similarity-based searches often consider several exams of a patient, most times overlooking the temporal aspect, which is crucial for patient monitoring. In this paper, we present: (1) a novel similarity search framework that identifies similar cases based on symptoms while considering the temporal evolution of the patients’ conditions; and (2) a novel similarity function, called GCWei function, which is built upon the traditional Levenshtein similarity and improves the quality of the search by penalizing the similarity between non-related sets of symptoms. To identify relations, GCWei relies on well-established graph community detection procedures using all patients’ historical data. By combining (1) and (2), we obtain a search approach called GCWei-based search, which efficiently retrieves similar cases with similar developments and thus gives the specialist a broader view of the patient’s condition based on past cases of other patients. To demonstrate the value of our approach, we evaluate it both quantitatively and qualitatively using the recent and publicly available MIMIC-IV database.