AUTOMATIC DISEASE DIAGNOSES USING LONGITUDINAL MEDICAL RECORD DATA

• US 20170228507A1
• Filed: 07/31/2015
• Published: 08/10/2017
• Est. Priority Date: 08/08/2014
• Status: Active Application

• Current Assignee: Icahn School of Medicine at Mount Sinai
• Original Assignee: Icahn School of Medicine at Mount Sinai
• Inventors: BOTTINGER, Erwin, NADKARNI, Girish, GOTTESMAN, Omri, ELLIS, Stephen Bartlett, HUOPANIEMI, Ilka
• Application Number:
  US15/502,266
• Publication Number:
  US 20170228507A1
• Time in Patent Office: Days
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/30 Unsupervised data analysis
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

An example method of automated medical diagnosis includes obtaining an electronic longitudinal data set for each of a plurality of patients, where each data set includes a plurality of measurement values corresponding to a metric, where each measurement value is associated with a respective time point. The method also includes arranging the data sets into two or more clusters. Arranging the data sets includes aligning the data sets according to their respective time points, selecting a cluster center for each cluster, determining a similarity between each data set and each cluster center, assigning each data set to a particular cluster based on the similarities, and iteratively re-aligning one or more of the data sets and/or reselecting one or more cluster centers, determining an updated similarity between each data set and each cluster center, and re-assigning data sets to particular clusters based on the updated similarities until a stop criterion is met. The method also includes automatically determining a medical diagnosis for a patient based on a relationship between the patient'"'"'s data set and a cluster center.

Claims

1. A method of automated medical diagnosis, the method comprising:

obtaining an electronic longitudinal data set for each of a plurality of patients, wherein each data set comprises;

a plurality of measurement values corresponding to a metric, wherein each measurement value is associated with a respective time point,

arranging the data sets into two or more clusters, wherein arranging the data sets comprises;

aligning the data sets according to their respective time points;

selecting a cluster center for each cluster;

determining a similarity between each data set and each cluster center;

assigning each data set to a particular cluster based on the similarities; and

iteratively re-aligning one or more of the data sets and/or reselecting one or more cluster centers, determining an updated similarity between each data set and each cluster center, and re-assigning data sets to particular clusters based on the updated similarities until a stop criterion is met; and

automatically determining a medical diagnosis for a patient based on a relationship between the patient'"'"'s data set and a cluster center.

2. The method of claim 1, wherein at least one of the data sets has a different number of measurement values than other data sets.

3. The method of claim 1, wherein each cluster center comprises a plurality of reference values, each measurement value associated with a respective reference time point.

4. The method of claim 3, wherein determining a similarity between each data set and each cluster center comprises determining similarities between measurement values of each data set to corresponding reference values of each cluster center.

5. The method of claim 1, wherein the stop criterion comprises a threshold value associated with the similarity determination.

6. The method of claim 1, wherein aligning the data sets according to their respective time points comprises aligning the data sets such that a first measurement value of each data set is aligned according to a common time point.

7. The method of claim 1, wherein re-aligning one or more of the data sets comprises shifting the time points of the one or more data sets relative to the time points of one or more other data sets.

8. The method of claim 1, wherein the measurement values correspond to a biological metric of a particular patient.

9. The method of claim 1, wherein each measurement value corresponds to an estimated glomerular filtration rate of a particular patient at a particular point in time.

10. The method of claim 1, wherein the medical diagnosis comprises a predicted disease state.

11. The method of claim 10, wherein the disease state is chronic kidney disease.

12. A system for diagnosing chronic kidney disease (CKD), the system comprising:

a computing apparatus configured to;

obtain an electronic longitudinal data set for each of a plurality of patients, wherein each data set comprises;

a plurality of measurement values corresponding to a metric, wherein each measurement value is associated with a respective time point,

arrange the data sets into two or more clusters, wherein arranging the data sets comprises;

aligning the data sets according to their respective time points;

selecting a cluster center for each cluster;

determining a similarity between each data set and each cluster center;

assigning each data set to a particular cluster based on the similarities; and

iteratively re-aligning one or more of the data sets and/or reselecting one or more cluster centers, determining an updated similarity between each data set and each cluster center, and re-assigning data sets to particular clusters based on the updated similarities until a stop criterion is met; and

automatically determine a medical diagnosis for a patient based on a relationship between the patient'"'"'s data set and a cluster center.

13. The system of claim 12, wherein at least one of the data sets has a different number of measurement values than other data sets.

14. The system of claim 12, wherein each cluster center comprises a plurality of reference values, each measurement value associated with a respective reference time point.

15. The system of claim 14, wherein determining a similarity between each data set and each cluster center comprises determining similarities between measurement values of each data set to corresponding reference values of each cluster center.

16. The system of claim 12, wherein the stop criterion comprises a threshold value associated with the similarity determination.

17. The system of claim 12, wherein aligning the data sets according to their respective time points comprises aligning the data sets such that a first measurement value of each data set is aligned according to a common time point.

18. The system of claim 12, wherein re-aligning one or more of the data sets comprises shifting the time points of the one or more data sets relative to the time points of one or more other data sets.

19. The system of claim 12, wherein the measurement values correspond to a biological metric of a particular patient.

20. The system of claim 12, wherein each measurement value corresponds to an estimated glomerular filtration rate of a particular patient at a particular point in time.

21. The system of claim 12, wherein the medical diagnosis comprises a predicted disease state.

22. The system of claim 21, wherein the disease state is chronic kidney disease.

23. A non-transitory computer readable medium storing instructions that are operable when executed by a data processing apparatus to perform operations for determining a permeability of a subterranean formation, the operations comprising:

obtaining an electronic longitudinal data set for each of a plurality of patients, wherein each data set comprises;

a plurality of measurement values corresponding to a metric, wherein each measurement value is associated with a respective time point,

arranging the data sets into two or more clusters, wherein arranging the data sets comprises;

aligning the data sets according to their respective time points;

selecting a cluster center for each cluster;

determining a similarity between each data set and each cluster center;

assigning each data set to a particular cluster based on the similarities; and

iteratively re-aligning one or more of the data sets and/or reselecting one or more cluster centers, determining an updated similarity between each data set and each cluster center, and re-assigning data sets to particular clusters based on the updated similarities until a stop criterion is met; and

automatically determining a medical diagnosis for a patient based on a relationship between the patient'"'"'s data set and a cluster center.

24. The non-transitory computer readable medium of claim 23, wherein at least one of the data sets has a different number of measurement values than other data sets.

25. The non-transitory computer readable medium of claim 23, wherein each cluster center comprises a plurality of reference values, each measurement value associated with a respective reference time point.

26. The non-transitory computer readable medium of claim 25, wherein determining a similarity between each data set and each cluster center comprises determining similarities between measurement values of each data set to corresponding reference values of each cluster center.

27. The non-transitory computer readable medium of claim 23, wherein the stop criterion comprises a threshold value associated with the similarity determination.

28. The non-transitory computer readable medium of claim 23, wherein aligning the data sets according to their respective time points comprises aligning the data sets such that a first measurement value of each data set is aligned according to a common time point.

29. The non-transitory computer readable medium of claim 23, wherein re-aligning one or more of the data sets comprises shifting the time points of the one or more data sets relative to the time points of one or more other data sets.

30. The non-transitory computer readable medium of claim 23, wherein the measurement values correspond to a biological metric of a particular patient.

31. The non-transitory computer readable medium of claim 23, wherein each measurement value corresponds to an estimated glomerular filtration rate of a particular patient at a particular point in time.

32. The non-transitory computer readable medium of claim 23, wherein the medical diagnosis comprises a predicted disease state.

33. The non-transitory computer readable medium of claim 32, wherein the disease state is chronic kidney disease.