Patent SG-11201909950Q-A (SG)
Title: Identifying entities in electronic medical records
Inventor: CAO SHAOSHENG
Filed: 2019-04-25
Abstract: Trainingtext extraction Text segmentation 104 Word vector training (based on, for example, cw2vec and n-stroke vector training) 106 BiLSTM-CRF model training 111 BiLSTM-CRF model parameter - classify terms from a written diagnosis containing Chinese characters n-stroke vector 110 Word vector 108 -.0!C N M O C (12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization International Bureau (43) International Publication Date 18 July 2019 (18.07.2019) WIPO I PCT 1111111111111101111111111111111011111010111111111111111111111111111101111111011110111111 (10) International Publication Number WO 2019/137562 A2 (51) International Patent Classification: Not classified (21) International Application Number: PCT/CN2019/084197 (22) International Filing Date: 25 April 2019 (25.04.2019) (25) Filing Language: English (26) Publication Language: English (71) Applicant: ALIBABA GROUP HOLDING LIMITED [—/CN]; Fourth Floor, One Capital Place, P.O. BOX 847, George Town, Grand Cayman (KY). (72) Inventors: CAO, Shaosheng; Alibaba Group Legal De- partment 5/F, Building 3, No.969 West Wen Yi Road, Yu Hang District, Hangzhou, Zhejiang 311121 (CN). ZHOU, Jun; Alibaba Group Legal Department 5/F, Building 3, No.969 West Wen Yi Road, Yu Hang District, Hangzhou, Zhejiang 311121 (CN). (74) Agent: BEIJING BESTIPR INTELLECTUAL PROP- ERTY LAW CORPORATION; Room 409, Tower B, Ka With Building, No. 9 Shangdi 3rd Street, Haidian District, Beijing 100085 (CN). (81) Designated States (unless otherwise indicated, for every kind of national protection available): AE, AG, AL, AM, AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, HR, HU, ID, IL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, (54) Title: IDENTIFYING ENTITIES IN ELECTRONIC MEDICAL RECORDS FIG. 1 100 (57) : Disclosed herein are methods, systems, and apparatus, including computer programs encoded on computer storage media, for entity prediction. One of the methods includes performing word segmentation on text to be predicted to obtain a plurality of words. For each word of the plurality of words, a determination is made whether the word has a pre-trained word vector. In response to determining that the word has a pre-trained word vector, the pre-trained word vector for the word is obtained. In response to determining that the word does not have a pre-trained word vector, a word vector for the word is determined based on a pre-trained stroke vector. The word vector and the pre-trained stroke vector are trained based on a text sample and a word vector model. An entity associated with the text is predicted by inputting word vectors of the plurality of words into an entity prediction model. [Continued on next page] WO 2019/137562 A2 I IIIII 10111110111111 VIII 111111111111010111 VIII 11111 II III IIIII IIIII SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (84) Designated States (unless otherwise indicated, for every kind of regional protection available): ARIPO (BW, GH, GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, TJ, TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LT, LU, LV, MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TR), OAPI (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, KM, ML, MR, NE, SN, TD, TG). Published: upon request of the applicant, before the expiration of the time limit referred to in Article 21(2)(a) without international search report and to be republished upon receipt of that report (Rule 48.2(g))