IPC분류정보
국가/구분 |
United States(US) Patent
등록
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국제특허분류(IPC7판) |
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출원번호 |
US-0690354
(2000-10-17)
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발명자
/ 주소 |
- Miikkulainen,Risto
- Dahlin,Michael D.
- Lipscher,Randolph P.
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출원인 / 주소 |
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대리인 / 주소 |
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인용정보 |
피인용 횟수 :
65 인용 특허 :
55 |
초록
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The current invention is directed to a system for adaptive medical decision support. The invented system provides a system that allows users to efficiently enter, access, and analyze medical information, without disrupting patient-doctor interactions or medical facility course of business; which ass
The current invention is directed to a system for adaptive medical decision support. The invented system provides a system that allows users to efficiently enter, access, and analyze medical information, without disrupting patient-doctor interactions or medical facility course of business; which assists in all stages of medical assessment and treatment; and which is tailored to the particular medical practice or specialty and taking into account the developing habits, preferences, performance, and individual patient histories, of an individual user. The invention provides a learning capacity configured to learn previously presented data and decisions and predict data or decisions based on data that it receives from the user, thereby adapting its operations to the developing habits, preferences, performance, and individual patient histories of an individual user. The system may also provide a "virtual specialist" feature, whereby the system can be instructed to produce the probable actions or recommendations of particular medical specialists.
대표청구항
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What is claimed is: 1. A computer-implemented method for adaptively supporting medical decisions of at least one user, comprising: a. receiving data at a host computer from a graphical medical record interface associated with a medical workflow, the graphical medical record interface implemented on
What is claimed is: 1. A computer-implemented method for adaptively supporting medical decisions of at least one user, comprising: a. receiving data at a host computer from a graphical medical record interface associated with a medical workflow, the graphical medical record interface implemented on a wireless portable interface device; b. predicting at least one medical decision at the host computer based on the received data; c. displaying the at least one predicted medical decision in the graphical medical record interface implemented on the wireless portable interface device; d. receiving at least one user-decision from the at least one user via the graphical medical record interface; and e. learning to predict the at least one user-decision using the host computer based on the received data and the at least one user-decision. 2. The method of claim 1, wherein the step of receiving data further comprises receiving data via a wireless communication means. 3. The method of claim 2, wherein the wireless communication means is chosen from a group consisting of infrared signals, radio signals, and pulse codes. 4. The method of claim 1, wherein the step of learning further comprises updating at least one learning module chosen from a group consisting of behavioral models, rule-based algorithms, learning-based algorithms, and neural networks. 5. The method of claim 1, wherein the step of learning further comprises customizing a plurality of operations to at least one parameter chosen from a group consisting of preferences of a user, habits of a user, medical specialties of a user, patient populations of a user, preferences of a group of users, habits of a group of users, medical specialties of a group of users, and patient populations of a group of users. 6. The method of claim 1, further comprising the step of executing the at least one user-decision, after the step of receiving the at least one user-decision. 7. The method of claim 1, further comprising executing the at least one predicted medical decision, before the step of receiving the at least one user-decision. 8. The method of claim 1, wherein the graphical medical record interface includes an electronic medical chart graphical user interface. 9. A software program, embodied on a computer-readable medium, incorporating the method recited in claim 1. 10. A method for adaptively supporting medical decisions of at least one user, comprising: a. receiving data at a host computer from a graphical interface implemented on an interface device; b. transmitting the data to at least one neural network; c. predicting at least one medical decision, via the at least one neural network based on the received data; d. displaying the at least one predicted medical decision in the graphical interface implement on the interface device; e. receiving at least one user-decision from the at least one user via the graphical interface implemented on the interface device; g. learning to predict the at least one user-decision at the host computer based on the received data and the at least one user-decision; and h. wherein learning comprises updating the at least one neural network. 11. The method of claim 10, wherein the step of receiving data further comprises receiving data via a wireless communication means. 12. The method of claim 11, wherein the wireless communication means is chosen from a group consisting of infrared signals, radio signals, and pulse codes. 13. The method of claim 10, wherein the step of learning further comprises customizing a plurality of operations to at least one parameter chosen from a group consisting of preferences of a user, habits of a user, medical specialties of a user, patient populations of a user, preferences of a group of users, habits of a group of users, medical specialties of a group of users, and patient populations of a group of users. 14. The method of claim 10, further comprising the step of executing the at least one user-decision, after the step of receiving the at least one user-decision. 15. The method of claim 10, further comprising executing the at least one predicted medical decision, before the step of receiving the at least one user-decision. 16. The method of claim 10, wherein the graphical interface implemented on the interface device includes an electronic medical chart graphical user interface. 17. A software program, embodied on a computer-readable medium, incorporating the method recited in claim 10. 18. A computer-implemented method for adaptively supporting medical decisions of at least one user, comprising a. receiving at least one first quantity of computer readable data associated with a medical workflow; b. receiving at least one user-decision associated with the medical workflow from a first at least one user via a graphical medical records interface; c. learning to predict the at least one received user-decision based on the at least one first quantity of computer readable data and the at least one user-decision by adapting a computer implemented prediction model; d. receiving at least one second quantity of computer readable data associated with the medical workflow; e. predicting at least one medical decision based on the at least one second quantity of computer readable data using the computer implemented prediction model, the at least one medical decision being associated with the medical workflow; f. displaying the at least one predicted medical decision via the graphical medical records interface; and g. receiving at least one second user-decision associated with the medical workflow via the graphical medical records interface. 19. The method of claim 18, wherein the step of receiving the at least one second quantity of computer readable data further comprises receiving data via a wireless communication means. 20. The method of claim 19, wherein the wireless communication means is chosen from a group consisting of infrared signals, radio signals, and pulse codes. 21. The method of claim 18, wherein the step of learning further comprises updating at least one learning module chosen from a group consisting of behavioral models, rule-based algorithms, learning-based algorithms, and neural networks. 22. The method of claim 18, wherein the step of learning further comprises customizing a plurality of operations to at least one parameter chosen from a group consisting of preferences of a user, habits of a user, medical specialties of a user, patient populations of a user, preferences of a group of users, habits of a group of users, medical specialties of a group of users, and patient populations of a group of users. 23. The method of claim 18, wherein the method is implemented on at least one portable computing device. 24. The method of claim 18, wherein the method is implemented on a host computer; the host computer receives data from at least one portable computing device; and the at least one portable computing device receives and displays output from the host computer. 25. The method of claim 18, further comprising the step of executing the at least one user-decision, after the step of receiving the at least one user-decision. 26. The method of claim 18, further comprising automatically executing the at least one predicted medical decision, before the step of receiving the at least one user-decision. 27. The method of claim 18, wherein the first at least one user comprises a specialist in a field of medicine. 28. The method of claim 18, wherein the first at least one user comprises a billing specialist or a coding specialist. 29. A software program, embodied on a computer-readable medium, incorporating the method recited in claim 18. 30. A computer-implemented method for adaptively supporting medical decisions, comprising a. receiving a first quantity of computer readable data associated with a medical workflow; b. predicting a first at least one medical decision associated with the medical workflow based on the computer readable data, via at least one rule-based algorithm; c. displaying the first at least one medical decision in a graphical medical interface; d. receiving at least one user-decision associated with the medical workflow from a first at least one user via the graphical medical interface; e. learning to predict the at least one user-decision based on the at least one user-decisions and the computer readable data, wherein learning to predict the at least one user-decisions includes adapting the at least one rule-based algorithm; f. receiving a second quantity of computer readable data associated with the medical workflow via the graphical medical interface; and g. predicting, via at least one learning-based algorithm, a second at least one medical decision associated with the medical workflow based on the second quantity of computer readable data. 31. The method of claim 30, further comprising displaying the second at least one medical decision. 32. The method of claim 30, wherein the step of receiving the second quantity of computer readable data further comprises receiving the second quantity of computer readable data via a wireless communication means. 33. The method of claim 32, wherein the wireless communication means is chosen from a group consisting of infrared signals, radio signals, and pulse codes. 34. The method of claim 30, wherein the method is implemented on at least one portable computing device. 35. The method of claim 30, wherein the method is implemented on a host computer, the host computer receives data from at least one portable computing device; and the at least one portable computing device receives and displays output from the host computer. 36. The method of claim 30, further comprising executing the first at least one medical decision, before the step of receiving the at least one user-decision. 37. The method of claim 30, further comprising the step of receiving a second at least one user-decision, after the step of predicting the second at least one medical decision. 38. The method of claim 37, further comprising the step of executing the second at least one user-decision, after the step of receiving the second at least one user-decision. 39. The method of claim 30, wherein the step of learning further comprises updating at least one learning module chosen from a group consisting of behavioral models, rule-based algorithms, learning-based algorithms, and neural networks. 40. The method of claim 30, wherein the step of learning further comprises customizing a plurality of operations to at least one parameter chosen from a group consisting of preferences of a user, habits of a user, medical specialties of a user, patient populations of a user, preferences of a group of users, habits of a group of users, medical specialties of a group of users, and patient populations of a group of users. 41. The method of claim 30, further comprising a. predicting, via the at least one rule-based algorithm, a third at least one medical decision; and b. displaying at least one predicted medical decision, chosen from a group consisting of the second at least one medical decision, the third at least one medical decision, and both the second and third at least one medical decisions. 42. The method of claim 41, further comprising executing the predicted medical decision chosen from the group consisting of the second at least one medical decision, the third at least one medical decision, and both the second and third at least one medical decisions. 43. The method of claim 41, wherein the predicted medical decision chosen from the group consisting of the second at least one medical decision, the third at least one medical decision is selected by at least one user, and both the second and third at least one medical decisions, is selected by at least one user. 44. The method of claim 41, wherein the predicted medical decision chosen from the group consisting of the second at least one medical decision, the third at least one medical decision, and both the second and third at least one medical decisions, is selected by a computing device. 45. The method of claim 41, further comprising the step of receiving a second at least one user-decision, after the step of predicting the third at least one medical decision. 46. The method of claim 45, further comprising the step of executing the second at least one user-decision after the step of receiving the second at least one user-decision. 47. The method of claim 45, further comprising learning to predict the second user-decision from the second quantity of data received. 48. The method of claim 47, wherein the step of learning further comprises updating at least one learning module chosen from a group consisting of behavioral models, rule-based algorithms, learning-based algorithms, and neural networks. 49. The method of claim 47, wherein the step of learning further comprises customizing a plurality of operations to at least one parameter chosen from a group consisting of preferences of a user, habits of a user, medical specialties of a user, patient populations of a user, preferences of a group of users, habits of a group of users, medical specialties of a group of users, and patient populations of a group of users. 50. The method of claim 30, further comprising displaying an electronic medical chart graphical user interface. 51. The method of claim 30, wherein the first at least one user comprises a specialist in a field of medicine. 52. The method of claim 30, wherein the first at least one user comprises a billing specialist or a coding specialist. 53. A software program, embodied on a computer-readable medium, incorporating the method recited in claim 30.
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