Patent AU-2019207309-C1 (AU)
Title: Identifying entities in electronic medical records
Inventor: CAO SHAOSHENG
IPC: G06F40/295
Filed: 2019-04-25
Granted: 2021-02-11
Abstract: INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) 
CORRECTED VERSION 
(19) World Intellectual Property 
(1) Organization11111111111111111111111I1111111111111ii111liiili 
International Bureau (10) International Publication Number 
(43) International Publication Date W O 2019/137562 A9 
18 July 2019 (18.07.2019) W IPO I PCT 
(51) InternationalPatent Classification: (72) Inventors: CAO, Shaosheng; Alibaba Group Legal De
Not classified partment 5/F, Building 3, No.969 West Wen Yi Road, Yu 
(21) International Application Number: Hang District, Hangzhou, Zhejiang 
311121 (CN). ZHOU, 
PCT/CN2019/084197 Jun; Alibaba Group Legal Department 5/F, Building 3, 
No.969 West Wen Yi Road, Yu Hang District, Hangzhou, 
(22) International Filing Date: Zhejiang 311121 (CN).  
25 April 2019 (25.04.2019) (74) Agent: BEIJING BESTIPR INTELLECTUAL PROP
(25) Filing Language: English ERTY LAW CORPORATION; Room 409, Tower B, Ka 
(26)PublicationLanguage: English Wal Building, No. 9 Shangdi 3rd Street, Haidian District, 
Beijing 100085 (CN).  
(71) Applicant: ALIBABA GROUP HOLDING LIMITED (81) Designated States (unless otherwise indicated, for every 
[-/CN]; Fourth Floor, One Capital Place, P.O. BOX 847, kind national protection available): AE, AG, AL, AM, 
GeorgeTown,GrandCayman(KY). 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, 
(54) Title: IDENTIFYING ENTITIES IN ELECTRONIC MEDICAL RECORDS 
Training text extraction 
Text segmentation 
- 104 
Word vector training (based on, for example, cw2vec 
and n-stroke vector training) 106 
Word vector n-stroke vector 
108 10 
BiLSTM-CRF model training 
BiLSTM-CRF model parameter - classify terms from a written diagnosis 
containing Chinese characters 
10 FIG. 1 
100 
(57) Abstract: Disclosed herein are methods, systems, and apparatus, including computer programs encoded on computer storage 
e 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.
W O 20 19/13 7562 A 9 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 
DZ, EC, EE, EG, ES, Fl, 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, 
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, Fl, 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 2](2)(a) 
- without international search report and to be republished 
upon receipt of that report (Rule 48.2(g)) 
- with information concerning authorization of rectification 
of an obvious mistake under Rule 91.3 (b) (Rule 48.2(i)) 
(48) Date of publication of this corrected version: 
13 February 2020 (13.02.2020) 
(15) Information about Correction: 
see Notice of 13 February 2020 (13.02.2020)