Diagnosis assistance system and diagnosis assistance device

Patent metadata

Field	Value
Publication number	US-2021217523-A1
Application number	US-201917056232-A
Country	US
Kind code	A1
Filing date	Apr 15, 2019
Priority date	May 18, 2018
Publication date	Jul 15, 2021
Grant date	—

Abstract

A diagnosis assistance system ( 100 ) includes a determiner ( 21 ) configured to receive trained model information (M) generated by a trained model generator ( 11 ) via an external network ( 30 ), the determiner ( 21 ) being configured to determine, based on the received trained model information (M), a presence or absence of a disease in a patient (P 2 ) who is not included in a patient group (PF).

First claim

1 . A diagnosis assistance system comprising:
a storage configured to store biological information about a patient group; a trained model generator configured to generate trained model information derived from a pattern included in the biological information about the patient group, by machine learning based on the biological information about the patient group stored in the storage; and a determiner configured to receive the trained model information generated by the trained model generator via an external network, the determiner being configured to determine, based on the received trained model information, a presence or absence of a disease in a patient with an unknown disease state.

2 . The diagnosis assistance system according to claim 1 , wherein
the storage is configured to store electronic clinical record data with identification information about an individual patient included in the patient group and the biological information about the individual patient being described therein; and the trained model generator is configured to extract the biological information about the individual patient from the electronic clinical record data stored in the storage, and to generate the trained model information based on the extracted biological information about the individual patient.

3 . The diagnosis assistance system according to claim 2 , wherein the trained model generator is configured to generate the trained model information based on the biological information about the individual patient extracted from the electronic clinical record data stored in the storage, and analytical information about a biological sample of the individual patient associated with the electronic clinical record data.

4 . The diagnosis assistance system according to claim 1 , wherein the determiner includes a first trained model update unit configured to update the trained model information received via the external network based on the biological information about a patient with a known presence or absence of the disease, who is not included in the patient group.

5 . The diagnosis assistance system according to claim 1 , further comprising:
a transmitter configured to transmit the biological information about a patient with a known presence or absence of the disease, who is not included in the patient group, to the trained model generator via the external network in a state in which identification information about the patient has been removed from the biological information, wherein the trained model generator includes a second trained model update unit configured to update the trained model information based on the biological information transmitted from the transmitter.

6 . The diagnosis assistance system according to claim 1 , wherein the trained model information is exported from the trained model generator and imported into the determiner via the external network.

7 . The diagnosis assistance system according to claim 1 , wherein
the biological information about the patient group includes data on a presence or absence of liver cancer, HCV antibody, HBs antigen, age, gender, height, weight, albumin, total bilirubin, AST, ALT, ALP, GGT, a platelet, AFP, an L3 fraction, and DCP; and the determiner is configured to determine the presence or absence of liver cancer in the patient with the unknown disease state based on the trained model information received via the external network.

8 . A diagnosis assistance device comprising:
a storage configured to store biological information about a patient group; and a trained model generator configured to generate trained model information derived from a pattern included in the biological information about the patient group, by machine learning based on the biological information about the patient group stored in the storage; wherein the trained model information generated by the trained model generator is exported to an outside via an external network.

9 . The diagnosis assistance device according to claim 8 , wherein
the storage is configured to store electronic clinical record data with identification information about an individual patient included in the patient group and the biological information about the individual patient being described therein; and the trained model generator is configured to extract the biological information about the individual patient from the electronic clinical record data stored in the storage, and to generate the trained model information based on the extracted biological information about the individual patient.

10 . The diagnosis assistance device according to claim 9 , wherein the trained model generator is configured to generate the trained model information based on the biological information about the individual patient extracted from the electronic clinical record data stored in the storage, and analytical information about a biological sample of the individual patient associated with the electronic clinical record data.

11 . A diagnosis assistance method comprising:
generating trained model information derived from a pattern included in biological information about a patient group, by machine learning based on the biological information about the patient group stored in a storage; and receiving the generated trained model information via an external network and determining, based on the received trained model information, a presence or absence of a disease in a patient with an unknown disease state.

12 . A diagnosis assistance method comprising:
generating trained model information derived from a pattern included in biological information about a patient group, by machine learning based on the biological information about the patient group stored in a storage; and exporting the generated trained model information to an outside via an external network.

13 . A diagnosis assistance method comprising:
generating trained model information derived from a pattern included in biological information about a patient group, by machine learning based on the biological information about the patient group stored in a storage; exporting the generated trained model information to an outside via an external network; and receiving the generated trained model information via the external network and determining, based on the received trained model information, a presence or absence of a disease in a patient with an unknown disease state.

Assignees

• Shimadzu Corp
• Univ Tokyo

Inventors

• Morimoto Kentaro
• Sato Masaya
• Yatomi Yutaka
• Tateishi Ryosuke
• Koike Kazuhiko

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/0499

  Feedforward networks · CPC title