Abstract: A method for mitigating the spread of infectious diseases among dialysis patients is provided. The method comprises: receiving, by a prediction system and from a medical facility, individual treatment data indicating dialysis treatment information associated with a patient undergoing dialysis treatment; receiving, by the prediction system and from a blood testing laboratory, individual lab data indicating blood analysis information associated with the patient; determining, by the prediction system, disease analysis results for the patient based on inputting the individual treatment data and the individual lab data into a disease prediction machine learning (ML) model, wherein the disease analysis results indicate a likelihood of the patient being infected with a contagious disease; and providing, by the prediction system and to the medical facility, instructions indicating one or more responsive actions based on the disease analysis results.

Abstract: Exemplary systems and methods for estimating progression of chronic kidney disease in a patient and applying clinical interventions may include determining historic values of at least one patient parameter that varies as a function of the progression of the chronic kidney disease over time, and computationally estimating a trend corresponding to the historic values. Based on the trend, at least one marker may be automatically provided that identifies a clinical intervention and a time in the future when the clinical intervention is expected to be needed. Based on the at least one marker, clinical preparations may be executed at a time prior to the administration of the intervention in order to improve at least one of a) timeliness of the execution of the intervention and b) effectiveness of the intervention.

Abstract: Exemplary systems and methods for estimating progression of chronic kidney disease in a patient and applying clinical interventions may include determining historic values of at least one patient parameter that varies as a function of the progression of the chronic kidney disease over time, and computationally estimating a trend corresponding to the historic values. Based on the trend, at least one marker may be automatically provided that identifies a clinical intervention and a time in the future when the clinical intervention is expected to be needed. Based on the at least one marker, clinical preparations may be executed at a time prior to the administration of the intervention in order to improve at least one of a) timeliness of the execution of the intervention and b) effectiveness of the intervention.

Abstract: Systems and methods for generating anonymized acoustic fingerprints. For a predetermined time interval, raw audio data associated with an environment can be captured and processed. Based on the captured raw audio data, a plurality of discrete data elements can be generated.

Abstract: A system and method for determining patient hospitalization risk and treating patients is disclosed. The system and method may include extracting patient data from one or databases corresponding to a pool of patients having end stage renal disease; using a predictive model with the extracted patient data to generate, for each of the patients, a respective expected probability for hospitalization within a predetermined time period; identifying a subset of patients having respective expected probabilities that are higher than other patients in the pool of patients; identifying, for each patient, at least one factor from the patient data that increased the expected probability of hospitalization; and based on the identified factors, determining and executing clinical interventions to lower the probability of hospitalization within the subset of the pool of patients.

Abstract: A method and system for determining a functionality level of dialysis patients for assessing parameters and timing of care. In one embodiment, the method includes extracting patient data from one or databases corresponding to a pool of patients having end stage renal disease (ESRD); using a predictive model with the extracted patient data to generate, for each of the patients in the pool of patients, a respective patient functional status score; identifying a subset of the pool of patients having a respective patient functional status score that is lower than a predetermined threshold value and/or a patient functional status score that is trending downwards; and based on the identified subset of the pool of patients, providing one or more treatment plans, wherein the treatment plans include at least one of (a) commencing one of an interventional treatment plan, or (b) commencing palliative and/or hospice care, or (c) combinations thereof.

Abstract: Exemplary systems and methods for estimating progression of chronic kidney disease in a patient and applying clinical interventions may include determining historic values of at least one patient parameter that varies as a function of the progression of the chronic kidney disease over time, and computationally estimating a trend corresponding to the historic values. Based on the trend, at least one marker may be automatically provided that identifies a clinical intervention and a time in the future when the clinical intervention is expected to be needed. Based on the at least one marker, clinical preparations may be executed at a time prior to the administration of the intervention in order to improve at least one of a) timeliness of the execution of the intervention and b) effectiveness of the intervention.