Personalized health risk assessment for critical care
First Claim
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1. A tangible, non-transitory, computer-readable storage medium comprising computer-readable instructions to be executed on a processor of a system for assessing whether a patient is at risk of an adverse outcome relating to one or more clinical conditions, the instructions comprising instructions for:
- receiving training data and target data, whereinreceiving training data includes receiving data x1 through xn, each xi of x1 through xn having one dimension for each of a plurality of patient characteristics and indicating values of the plurality of patient characteristics for an ith patient of n patients, andreceiving target data includes receiving data xtp, xtp having one dimension for each of the plurality of patient characteristics and indicating values of the plurality of patient characteristics for a target patient;
determining whether, or an extent to which, xtp is anomalous, wherein the instructions for determining whether, or the extent to which, xtp is anomalous include instructions for(a) for each xi of x1 through xn, calculating a distance between xi and k nearest neighbors of xi,(b) calculating a distance between xtp and k nearest neighbors of xtp,(c) determining a threshold based at least on the distances calculated in (a), and(d) determining whether, or the extent to which, xtp is anomalous at least in part by comparing the distance calculated in (b) to the threshold,wherein calculating the distances in (a) and (b) includes, for at least some of the distances calculated in (a) and (b), finding the respective k nearest neighbors by implementing a plurality of locality sensitive hashing (LSH) searches with a progressively increasing radius, the radius being increased at least until the respective k nearest neighbors are found; and
indicating whether, or an extent to which, the target patient is at risk of the adverse outcome, wherein the indication is based at least in part on the determination of whether, or the extent to which, xtp is anomalous.
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Abstract
A method for providing a personalized health risk of a patient includes receiving training data corresponding to a plurality of patients and target data corresponding to a target patient; generating model data based on the training data according to an anomaly detection method; either determining whether the target data is anomalous with respect to the training data, or determining the extent to which the target data is anomalous with respect to the training data; and either indicating whether the target patient is at risk of the adverse outcome, or indicating the extent to which the target patient is at risk of the adverse outcome.
20 Citations
14 Claims
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1. A tangible, non-transitory, computer-readable storage medium comprising computer-readable instructions to be executed on a processor of a system for assessing whether a patient is at risk of an adverse outcome relating to one or more clinical conditions, the instructions comprising instructions for:
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receiving training data and target data, wherein receiving training data includes receiving data x1 through xn, each xi of x1 through xn having one dimension for each of a plurality of patient characteristics and indicating values of the plurality of patient characteristics for an ith patient of n patients, and receiving target data includes receiving data xtp, xtp having one dimension for each of the plurality of patient characteristics and indicating values of the plurality of patient characteristics for a target patient; determining whether, or an extent to which, xtp is anomalous, wherein the instructions for determining whether, or the extent to which, xtp is anomalous include instructions for (a) for each xi of x1 through xn, calculating a distance between xi and k nearest neighbors of xi, (b) calculating a distance between xtp and k nearest neighbors of xtp, (c) determining a threshold based at least on the distances calculated in (a), and (d) determining whether, or the extent to which, xtp is anomalous at least in part by comparing the distance calculated in (b) to the threshold, wherein calculating the distances in (a) and (b) includes, for at least some of the distances calculated in (a) and (b), finding the respective k nearest neighbors by implementing a plurality of locality sensitive hashing (LSH) searches with a progressively increasing radius, the radius being increased at least until the respective k nearest neighbors are found; and indicating whether, or an extent to which, the target patient is at risk of the adverse outcome, wherein the indication is based at least in part on the determination of whether, or the extent to which, xtp is anomalous. - View Dependent Claims (2, 3, 4)
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5. A computer-implemented method of assessing whether a patient is at risk of an adverse outcome relating to one or more clinical conditions, the method comprising:
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receiving training data and target data, wherein receiving training data includes receiving data x1 through xn, each xi of x1 through xn having one dimension for each of a plurality of patient characteristics and indicating values of the plurality of patient characteristics for an ith patient of n patients, and receiving target data includes receiving data xtp having one dimension for each of the plurality of patient characteristics and indicating values of the plurality of patient characteristics for a target patient; determining via the computer whether, or an extent to which, xtp is anomalous, wherein determining whether, or the extent to which, xtp is anomalous includes (a) for each xi of x1 through xn, calculating a distance between xi and k nearest neighbors of xi , (b) calculating a distance between xtp and k nearest neighbors of xtp, (c) determining a threshold based at least on the distances calculated in (a), and (d) determining whether, or the extent to which, xtp is anomalous at least in part by comparing the distance calculated in (b) to the threshold, wherein calculating the distances in (a) and (b) includes, for at least some of the distances calculated in (a) and (b), finding the respective k nearest neighbors by implementing a plurality of locality sensitive hashing (LSH) searches with a progressively increasing radius, the radius being increased at least until the respective k nearest neighbors are found; and indicating whether, or an extent to which, the target patient is at risk of the adverse outcome, wherein the indication is based at least in part on the determination of whether, or the extent to which, xtp is anomalous. - View Dependent Claims (6, 7, 8, 9, 10)
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11. A computer-implemented method of assessing whether a patient is at risk of developing a clinical condition, the method comprising:
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receiving training data and target data, wherein receiving training data includes receiving data sets (x1, y1) through (xn, yn), each xiof x1 through xn having one dimension for each of a plurality of patient characteristics and indicating values of the plurality of patient characteristics for an ith patient of n patients, and each yi of y1 through yn indicating whether the ith patient of the n patients is positively associated with the clinical condition, and receiving target data includes receiving data xtp having one dimension for each of the plurality of patient characteristics and indicating values of the plurality of patient characteristics for a target patient; generating model data via a computer using the training data, wherein generating the model data includes minimizing an objective function by selecting a multidimensional vector w and variables ψ
i, ξ
i, b and p subject to constraints that includewTΦ
(xi)−
p ≧
(−
ψ
i) for all i from 1 to n,yi(wTΦ
(xi)−
b)≧
1−
ξ
i for all i from 1 to n,ψ
i≧
0, andξ
i≧
0,wherein the objective function is - View Dependent Claims (12, 13, 14)
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Specification
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Current AssigneeHenry Ford Health System, Board of Regents of the University of Michigan (University of Michigan)
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Original AssigneeBoard of Regents of the University of Michigan (University of Michigan)
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InventorsSyed, Zeeshan H., Rubinfeld, Ilan S.
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Primary Examiner(s)Chaki, Kakali
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Assistant Examiner(s)PELLETT, DANIEL T
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Application NumberUS13/161,088Publication NumberTime in Patent Office1,280 DaysField of SearchNoneUS Class Current706/46CPC Class CodesG16H 50/20 for computer-aided diagnosi...G16H 50/30 for calculating health indi...G16H 50/50 for simulation or modelling...G16Z 99/00 Subject matter not provided...
