Abstract: The present disclosure is directed to systems and methods for developing an individual-specific patient baseline for a target patient. An exemplary method involves: determining one or more acuity scores for the target patient; identifying patient health data corresponding to one or more low acuity time periods; storing retrospective clinical data from a group of patients in a second database; comparing the patient health data corresponding to the one or more low acuity time periods with retrospective clinical data from a group of patients by identifying one or more patient subgroups; determining the individual-specific patient baseline using an adaptive baseline selection algorithm, wherein the adaptive baseline selection algorithm is used to determine whether to determine the individual-specific patient baseline using patient health data or using retrospective clinical data from one or more patient subgroups; and displaying, using a user interface, the individual-specific patient baseline.

Abstract: A non-transitory computer readable medium (26) stores instructions executable by at least one electronic processor (20) to perform a method (100) of acuity monitoring of a patient. The method includes: generating a set of diagnosis-specific acuity scores (32) for a plurality of diagnoses using diagnosis-specific acuity scoring modules (28) for the respective diagnoses of the plurality of diagnoses applied to clinical metrics of the patient; determining at least one primary diagnosis (34) of the patient using a computer-aided diagnosis (CAD) module (30) applied to the clinical metrics of the patient; selecting an acuity score for the at least one primary diagnosis from the set of diagnosis-specific acuity scores; and displaying an indication of the at least one primary diagnosis and the acuity score for the at least one primary diagnosis.

Abstract: A method (100) for generating and presenting a patient risk score, comprising: (i) receiving (104) a plurality of features about the patient comprising at least a plurality of vital signs obtained for the patient; (ii) characterizing (106), using a trained risk model, an importance of each of the received plurality of features to a risk score analysis; (iii) calculating (108) an initial risk score; (iv) identifying (110) one or more missing features; (v) calculating (110) a risk score confidence interval comprising an effect of the identified one or more missing features on a confidence range of the initial risk score; (vi) calculating (112), from the initial risk score and the calculated risk score confidence interval, a risk score range; and (vii) presenting (118) the risk score range comprising the initial the score plus and minus the calculated risk score confidence interval.

Abstract: A system and for interpreting a risk score and comparing a decision trajectory are described. The system includes: a memory adapted to store: input features comprising clinical measurements of a patient; engineered features; a trained risk score computational model comprising instructions; a defined subgroup of patients; and a processor.

Abstract: A method for processing medical information includes identifying a first patient in a first state, identifying a second patient in a second state, calculating a first risk score for the first patient, calculating a first risk score for the second patient, and determining a risk prone area in a medical facility based on the first risk score for the first patient and the first risk score for the second patient. The first state is an infected state and the second state is different from the first state. The first risk score of the first patient provides an indication of a severity of the infected state of the first patient, and the first risk score of the second patient provides an indication of the second patient being infected by the first patient.

Abstract: The present disclosure is directed to systems and methods for developing an individual-specific patient baseline for a target patient. An exemplary method involves: determining one or more acuity scores for the target patient; identifying patient health data corresponding to one or more low acuity time periods; storing retrospective clinical data from a group of patients in a second database; comparing the patient health data corresponding to the one or more low acuity time periods with retrospective clinical data from a group of patients by identifying one or more patient subgroups; determining the individual-specific patient baseline using an adaptive baseline selection algorithm, wherein the adaptive baseline selection algorithm is used to determine whether to determine the individual-specific patient baseline using patient health data or using retrospective clinical data from one or more patient subgroups; and displaying, using a user interface, the individual-specific patient baseline.