A system (200, 200', 210, 210', 220, 220') for determining a patient's risk of developing an infection, the system (200, 200', 210, 210', 220, 220') comprising: at least one computing device comprising: a memory (320, 430, 1220, 1506, 1510, 1512, 1550) storing instructions; and a processor (310, 420, 1210, 1302, 1504, 1542) coupled to the memory (320, 430, 1220, 1506, 1510, 1512, 1550) operative to, responsive to executing the instructions: extract patient historical data from one or more databases corresponding to a pool of patients (240, 1401) receiving at-home peritoneal dialysis treatment, the pool of patients (240, 1401) comprising at least one first training set of patients (240, 1401) previously diagnosed with an infection within a selected time period and at least one second training set of patients (240, 1401) that did not develop an infection within the selected time period; train one or more predictive models using the at least one first training set to determine factors associated with an infection diagnosis, the one or more predictive models comprising a machine learning model; train the one or more predictive models model using the at least one second training set to determine factors associated with no infection diagnosis; analyze patient data of a patient (240, 1401) via the one or more predictive models to determine: a patient risk score for the patient (240, 1401) to develop an infection within the selected time period, and at least one reason associated with the patient risk score identifying leading factors for developing the infection; and determine, responsive to patient risk score being over a predetermined threshold value, at least one individualized interventional treatment for the patient (240, 1401) based on the patient risk score and the at least one reason to reduce the risk of the patient (240, 1401) developing the infection. a memory (320, 430, 1220, 1506, 1510, 1512, 1550) storing instructions; and a processor (310, 420, 1210, 1302, 1504, 1542) coupled to the memory (320, 430, 1220, 1506, 1510, 1512, 1550) operative to, responsive to executing the instructions: extract patient historical data from one or more databases corresponding to a pool of patients (240, 1401) receiving at-home peritoneal dialysis treatment, the pool of patients (240, 1401) comprising at least one first training set of patients (240, 1401) previously diagnosed with an infection within a selected time period and at least one second training set of patients (240, 1401) that did not develop an infection within the selected time period; train one or more predictive models using the at least one first training set to determine factors associated with an infection diagnosis, the one or more predictive models comprising a machine learning model; train the one or more predictive models model using the at least one second training set to determine factors associated with no infection diagnosis; analyze patient data of a patient (240, 1401) via the one or more predictive models to determine: a patient risk score for the patient (240, 1401) to develop an infection within the selected time period, and at least one reason associated with the patient risk score identifying leading factors for developing the infection; and determine, responsive to patient risk score being over a predetermined threshold value, at least one individualized interventional treatment for the patient (240, 1401) based on the patient risk score and the at least one reason to reduce the risk of the patient (240, 1401) developing the infection. extract patient historical data from one or more databases corresponding to a pool of patients (240, 1401) receiving at-home peritoneal dialysis treatment, the pool of patients (240, 1401) comprising at least one first training set of patients (240, 1401) previously diagnosed with an infection within a selected time period and at least one second training set of patients (240, 1401) that did not develop an infection within the selected time period; train one or more predictive models using the at least one first training set to determine factors associated with an infection diagnosis, the one or more predictive models comprising a machine learning model; train the one or more predictive models model using the at least one second training set to determine factors associated with no infection diagnosis; analyze patient data of a patient (240, 1401) via the one or more predictive models to determine: a patient risk score for the patient (240, 1401) to develop an infection within the selected time period, and at least one reason associated with the patient risk score identifying leading factors for developing the infection; and determine, responsive to patient risk score being over a predetermined threshold value, at least one individualized interventional treatment for the patient (240, 1401) based on the patient risk score and the at least one reason to reduce the risk of the patient (240, 1401) developing the infection. a patient risk score for the patient (240, 1401) to develop an infection within the selected time period, and at least one reason associated with the patient risk score identifying leading factors for developing the infection; and determine, responsive to patient risk score being over a predetermined threshold value, at least one individualized interventional treatment for the patient (240, 1401) based on the patient risk score and the at least one reason to reduce the risk of the patient (240, 1401) developing the infection.