Tuning a Cancer Patient Typology Based on Emergency Department Visits There has been considerable research on developing symptom clusters for cancer patients but finding a consistent and replicable typology of patients in terms of the kinds of symptoms they experience remains elusive (e.g., 1). One reason for lack of consensus may be that analyses are unfocused, and use different combinations of clustering variables. This paper identifies subgroups of similar cancer patients within a sample of 18,535 patients, based on the Edmonton Symptom Assessment System (ESAS) and also a set of inpatient and outpatient variables using linked administrative sources of Ontario healthcare data. Kmeans cluster analysis was performed on cancer patients having only one primary cancer type, and who had at least one emergency department (ED) visit. Only variables that had a Pearson correlation of at least 0.1 with the number of days until visiting an ED, after an assessment, were included in the cluster analysis. While information about next emergency department visit was not included as a clustering variable, the number of patient types was chosen so as to minimize the mean absolute error of predictions of next emergency department visits by cancer patients. The next emergency department visit, after the last assessment date of each patient, was predicted for each cluster solution. The cluster solution with maximum accuracyminimum MAE was used to derive the final set of patient types. With the help of physicians, and guided by the results of these analyses, a description of each patient type was created. Based on our results we grouped cancer patients into four types that differ in terms of type of cancer, stage of cancer, and symptomology. Implications for symptom management and reduction of ED visits are discussed.

Unsupervised Machine Learning Elicits Patient Archetypes in a Primary Percutaneous Coronary Intervention Service A primary percutaneous coronary intervention (PPCI) reestablishes blood flow in an obstructed coronary artery. PPCI referrals vary in admission criteria partly on the basis of ECG findings, hence, not all the referrals are accepted. The aim of the paper is to discover archetypes of accepted patients referred to the PPCI center. Cluster analysis was performed on a PPCI referral dataset to identify patient archetypes and identify any key patterns of patients who were accepted for PPCI. A kmeans clustering algorithm was used with the elbow method for determining the optimum number of clusters (groups of patients). A silhouette plot was generated for within cluster validation. Among the accepted PPCI referrals, there were four different groups of patients. The patients within each group have similar characteristics. The largest cluster of patients include male patients being referred out of hours and with excessive door to balloon times (DTBTs) as compared to those referred in hours. Another cluster includes older female patients who are referred out of hour. Also, it was discovered that the false activation rate and DTBTs are higher in females as compared to male clusters. The smallest cluster include the most elderly patients in the whole referral dataset and mainly includes more males than femaleu0027s who are referred out of hour and have the highest false activation rate, DTBT, and 30 days mortality rate. The cluster analysis of PPCI dataset revealed different patient archetypes. Each group of patients have a different mean age, out of hours referral rate, DTBT, false activation and 30 days mortality rate compared to other group. The identified clusters could be helpful for the clinicians to better understand their patients and utilize this information to aid the clinical decision making.