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Runs - Precision Medicine 2019

absrun1

Results | Participants | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: absrun1
  • Participant: WCMC
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 252d9f25f4085972d83410a850d3f9da
  • Run description: The basic IR model without PM filter

absrun2

Results | Participants | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: absrun2
  • Participant: WCMC
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 5224535aa587316b6fc0921ca9a7f5a8
  • Run description: The basic IR model with PM filter

bc

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: bc
  • Participant: POZNAN
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: trials
  • MD5: 399b52944e7d7941209dd051dfe8f09b
  • Run description: several LGD models combined by borda count terrier implementation

BM25

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: BM25
  • Participant: ims_unipd
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 50abb960f69727da7caa0bb087b091da
  • Run description: BM25

bm25_6801

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: bm25_6801
  • Participant: CSIROmed
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/2/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 37605bfa2b10bbb1050be4b3ae2a2325
  • Run description: Solr's BM25 on default settings (k1=1.2,b=0.75)

bm25_ct_25

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: bm25_ct_25
  • Participant: CSIROmed
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/2/2019
  • Type: automatic
  • Task: trials
  • MD5: cf96d67b65e04e4718a007b5b7f219f7
  • Run description: Solr's BM25 (k1=1.2,b=0.75)

bm25_ct_f_61

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: bm25_ct_f_61
  • Participant: CSIROmed
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/2/2019
  • Type: automatic
  • Task: trials
  • MD5: e515eae7de2232b5a7e7318a951cdd0c
  • Run description: Solr's BM25 (k1=1.2,b=0.75), age and gender filtering

BM25ct

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: BM25ct
  • Participant: ims_unipd
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: trials
  • MD5: 8f7ecf0a130d73fed9065213a48fc249
  • Run description: BM25

BM25neop01

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: BM25neop01
  • Participant: ims_unipd
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 3d42a4445944ece2f1cb033f44f52226
  • Run description: BM25 + query rewriting

BM25neop01r

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: BM25neop01r
  • Participant: ims_unipd
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: trials
  • MD5: e14ab1286c802b69d465e6b1dcb4f8ed
  • Run description: BM25 + query rewriting

BM25neopcomd

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: BM25neopcomd
  • Participant: ims_unipd
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 831ca31f99d8cca77ae1806a06eb7cb0
  • Run description: BM25 + query rewriting

BM25neopgngm

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: BM25neopgngm
  • Participant: ims_unipd
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: abstracts
  • MD5: be73e5c9b0b7be22973f8ed6c306f023
  • Run description: BM25 + query rewriting

BM25solid01o

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: BM25solid01o
  • Participant: ims_unipd
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: trials
  • MD5: c6a46904da6d9168585f25f59adb2c66
  • Run description: BM25 + query rewriting

BM25solid01r

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: BM25solid01r
  • Participant: ims_unipd
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: trials
  • MD5: 8c5cb9f29aed6af288564b4077852c48
  • Run description: BM25 + query rewriting

cbnuCT1

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: cbnuCT1
  • Participant: cbnu
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/5/2019
  • Type: automatic
  • Task: trials
  • MD5: 17a91d763a2209f33e80928e10da0e54
  • Run description: third party (indri)

cbnuCT2

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: cbnuCT2
  • Participant: cbnu
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/5/2019
  • Type: automatic
  • Task: trials
  • MD5: 21214a21c16148bed3472073f9455b83
  • Run description: third party (indri)

cbnuCT3

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: cbnuCT3
  • Participant: cbnu
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/5/2019
  • Type: automatic
  • Task: trials
  • MD5: dabb3c40650232f21e891b6bd03666a0
  • Run description: third party (indri)

cbnuCT4

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: cbnuCT4
  • Participant: cbnu
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/5/2019
  • Type: automatic
  • Task: trials
  • MD5: f43b739e49b9e5ad3a26240997a42485
  • Run description: third party (indri)

cbnuSA1

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: cbnuSA1
  • Participant: cbnu
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/5/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 2d1aaecd53e986f55d63a95d5500e760
  • Run description: third party (indri)

cbnuSA2

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: cbnuSA2
  • Participant: cbnu
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/5/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 006d983afccb7d58e43bc98a36f3169a
  • Run description: third party (indri)

cbnuSA3

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: cbnuSA3
  • Participant: cbnu
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/5/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 4e1a036942ab02746b4eb49543822ad8
  • Run description: third party (indri)

cbnuSA4

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: cbnuSA4
  • Participant: cbnu
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/5/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 56b5cb4df360ac2d1dbdf4ad02cca840
  • Run description: third party (indri)

ccnl_sa1

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: ccnl_sa1
  • Participant: CCNL
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 050cde834e1f1cfb6dd1940198cb9811
  • Run description: We used the basic model bm25 provided on the luence platform to retrieve Scientific Abstracts and then use the SciBert as a reranking model to rerank the retrieval result.

ccnl_sa2

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: ccnl_sa2
  • Participant: CCNL
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 188733631e65789dfec129e871e57a3c
  • Run description: We used the basic model bm25 provided on the luence platform to retrieve Scientific Abstracts and then use the SciBert as a reranking model to rerank the retrieval result. We use 4000 documents for each topic, which is different from ccnl_sa1

ccnl_sa3

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: ccnl_sa3
  • Participant: CCNL
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/5/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 2f0c128eda76886f658cc2c26eeb9c2e
  • Run description: We used the basic model bm25 provided on the luence platform to retrieve Scientific Abstracts and then use the SciBert as a reranking model to rerank the retrieval result. When we use diseases, the synonyms of diseases, genes and the sysnonyms of genes to train the reranking model.

ccnl_sa4

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: ccnl_sa4
  • Participant: CCNL
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/5/2019
  • Type: automatic
  • Task: abstracts
  • MD5: b9ea3278795fd01b99d92b5443ee076e
  • Run description: We used the basic model bm25 provided on the luence platform to retrieve Scientific Abstracts and then used the SciBert as a reranking model to rerank the retrieval result. We used diseases and genes of topics, titles of documents and abstracts of documents to train the reranking model.

ccnl_sa5

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: ccnl_sa5
  • Participant: CCNL
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/5/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 38ea81c14455fcefb1a7a0df799855f1
  • Run description: We used the basic model bm25 provided on the luence platform to retrieve Scientific Abstracts. We use two query to retrieve the document without any external resources. And then we searched the best weight to combine two retrieval result throught qrels of TREC 2017 Precision Medicine Track and qrels of TREC 2018 Precision Medicine Track

ccnl_trials1

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: ccnl_trials1
  • Participant: CCNL
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/3/2019
  • Type: automatic
  • Task: trials
  • MD5: 392675876e4880944495789bcf668e37
  • Run description: We used the basic model bm25 provided on the luence platform to retrieve clinical trials and then use the SciBert as a reranking model to rerank the retrieval result. The SciBert is a deep learning method and pretrained on biomedical literatures.

ccnl_trials2

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: ccnl_trials2
  • Participant: CCNL
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: trials
  • MD5: 3d350009b77d442b222ea9372e1a6710
  • Run description: We used the basic model bm25 provided on the luence platform to retrieve clinical trials and then use the SciBert as a reranking model to rerank the retrieval result. The final numbers of reranking documents is 4000 for each topic, which is different from ccnl_trial1

cl_base_rr

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: cl_base_rr
  • Participant: ECNU-ICA
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: trials
  • MD5: 47e52afd16d135383de150eadc391e35
  • Run description: BM25 with custom rerank method

cl_ft_agg

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: cl_ft_agg
  • Participant: ECNU-ICA
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: trials
  • MD5: 12dba094c26c18189b44d4a34a954a2e
  • Run description: BM25 with custom rerank method

cl_ft_rr

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: cl_ft_rr
  • Participant: ECNU-ICA
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: trials
  • MD5: 58c6a6a883d32695cfd47f54eb479723
  • Run description: BM25 with custom rerank method

clinic_base

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: clinic_base
  • Participant: ECNU-ICA
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: trials
  • MD5: 5f1b129378dde418ec77e9d207af9369
  • Run description: BM25 with custom rerank method

clinic_ft

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: clinic_ft
  • Participant: ECNU-ICA
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: trials
  • MD5: 6c39d10eb8243071aa9d7f95ad538c45
  • Run description: BM25 with custom rerank method

default100k

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: default100k
  • Participant: UNIVAQ
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/1/2019
  • Type: automatic
  • Task: abstracts
  • MD5: b66e44152d1e729c2250ff79281c97ba
  • Run description: Deep learning models for gene-disease extraction, pm classification and demographic matching. Similarity score with parameter K=1.

default1m

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: default1m
  • Participant: UNIVAQ
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: abstracts
  • MD5: aefaa7bae80940fe1d810a075db159e4
  • Run description: Deep learning models for gene-disease extraction and pm classification. Fuzzy demographic matching. Similarity score with parameter K=1.

dfr_9464

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: dfr_9464
  • Participant: CSIROmed
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/2/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 23330a8a8c3593d850afe26b5abdb1a7
  • Run description: Solr's DFR (I(ne)BH2(c=1))

DFRInL2_f

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: DFRInL2_f
  • Participant: CSIROmed
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/2/2019
  • Type: automatic
  • Task: trials
  • MD5: cb216e8db5e1e50f3b4b8d30d543f2b8
  • Run description: Solr's DFR (I(n)LH2(c=1)) with age and gender filtering

Dutir_Cli1

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: Dutir_Cli1
  • Participant: DUTIR
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/1/2019
  • Type: automatic
  • Task: trials
  • MD5: f4f0f51bcc06e242b92da8d1d9da666e
  • Run description: In Clinical Trials task, we only use query template which provided by ElasticSearch to obtain retrieval results.

Dutir_Cli2

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: Dutir_Cli2
  • Participant: DUTIR
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/1/2019
  • Type: automatic
  • Task: trials
  • MD5: c94ee9fef2e24b74297ac989d2e39248
  • Run description: In Clinical Trials task, we only use query template which provided by ElasticSearch to obtain retrieval results.

DutirRun1

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: DutirRun1
  • Participant: DUTIR
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/1/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 7badc9d0f0cea3fbca2e0ca60906574a
  • Run description: First, we use a BM25 model to obtain initial retrieval results. Then, we use a deep learning method to reorder the initial retrieval results.

DutirRun2

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: DutirRun2
  • Participant: DUTIR
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/1/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 5149d7b9b55074fc64bac5991e844314
  • Run description: First, we use a BM25 model to obtain initial retrieval results. Then, we use a deep learning method to reorder the initial retrieval results.

DutirRun3

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: DutirRun3
  • Participant: DUTIR
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/1/2019
  • Type: automatic
  • Task: abstracts
  • MD5: bda0701eea972a1a0077f29e9159b8f1
  • Run description: First, we use a BM25 model to obtain initial retrieval results. Then, we use a deep learning method to reorder the initial retrieval results.

DutirRun4

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: DutirRun4
  • Participant: DUTIR
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/1/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 0d7c67c8a0e53e62d9edd8fec2a73ec6
  • Run description: First, we use a BM25 model to obtain initial retrieval results. Then, we use a deep learning method to reorder the initial retrieval results.

DutirRun5

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: DutirRun5
  • Participant: DUTIR
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/1/2019
  • Type: automatic
  • Task: abstracts
  • MD5: dbd604843d067fd1ac301650a107df6e
  • Run description: First, we use a BM25 model to obtain initial retrieval results. Then, we use a deep learning method to reorder the initial retrieval results.

eged

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: eged
  • Participant: Brown
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: trials
  • MD5: bb9f55a76dc0f6f484fa0902c4bd8215
  • Run description: BM25F, Whoosh

egnd

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: egnd
  • Participant: Brown
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: trials
  • MD5: 290f0a6a75362d4dc5189c80bb354521
  • Run description: BM25F, Whoosh

et_8435

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: et_8435
  • Participant: CSIROmed
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/2/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 02986011cf5fbbecb4b54833ab5195d0
  • Run description: Solr's BM25 for the first retrieval step, LTR based on Extra Trees model (sklearn implementation) used for re-ranking

imi_mug1

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: imi_mug1
  • Participant: imi_mug
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/2/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 0b2e06f6544986fe547c4b32cb49dc2d
  • Run description: Elasticsearch-based query templates

imi_mug2

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: imi_mug2
  • Participant: imi_mug
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/2/2019
  • Type: automatic
  • Task: abstracts
  • MD5: fdbf8f41015d8e108a4ab9f79ef90b7e
  • Run description: Elasticsearch-based query templates

imi_mug2_t

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: imi_mug2_t
  • Participant: imi_mug
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/2/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 889a220eb469bfc3d2f2f1639b06e544
  • Run description: Elasticsearch-based query templates

imi_mug3

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: imi_mug3
  • Participant: imi_mug
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/2/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 4e10079b691b1332d332740dbe7de521
  • Run description: Elasticsearch-based query templates

imi_mug3_t

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: imi_mug3_t
  • Participant: imi_mug
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/2/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 7e6ac97f880dde992d9f55eb0a49ee5b
  • Run description: Elasticsearch-based query templates

jlctgenes

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: jlctgenes
  • Participant: julie-mug
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/5/2019
  • Type: automatic
  • Task: trials
  • MD5: 4d51c072617c23f7ad9f5ecbcce0d386
  • Run description: Same as jlctphrase, but with extra matching on automatically extracted genes.

jlctletor

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: jlctletor
  • Participant: julie-mug
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/5/2019
  • Type: automatic
  • Task: trials
  • MD5: aa3b9dc1d6545569a3077ba73133c552
  • Run description: ElasticSearch 5.4.0, factory defaults (BM25, k1=1.2, b=0.75), RankLib LambdaMART trained on TRECPM 2017 and 2018 clinical trials gold data

jlctltrin

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: jlctltrin
  • Participant: julie-mug
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/5/2019
  • Type: automatic
  • Task: trials
  • MD5: da45bd285ddd1a1c508a69366e56fdef
  • Run description: ElasticSearch 5.4.0, factory defaults (BM25, k1=1.2, b=0.75), RankLib LambdaMART trained on internal development data for the 2019 topics

jlctphrase

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: jlctphrase
  • Participant: julie-mug
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/5/2019
  • Type: automatic
  • Task: trials
  • MD5: 9885a55ca0a52be34c86e08eb7c01ca5
  • Run description: ElasticSearch 5.4.0, factory defaults (BM25, k1=1.2, b=0.75)

jlctprec

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: jlctprec
  • Participant: julie-mug
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/5/2019
  • Type: automatic
  • Task: trials
  • MD5: a4393f7260e14774100d8258f8ba9a01
  • Run description: Same as jlctphrase, but with no match_all clause

jlpmcommon2

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: jlpmcommon2
  • Participant: julie-mug
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/5/2019
  • Type: automatic
  • Task: abstracts
  • MD5: d9ddfbfeec93103be46af27cfd6c79cf
  • Run description: REPLACEMENT FOR jlpmcommon RUN ElasticSearch 5.4.0, factory defaults (BM25, k1=1.2, b=0.75)

jlpmletor

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: jlpmletor
  • Participant: julie-mug
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/5/2019
  • Type: automatic
  • Task: abstracts
  • MD5: a6556fd34d606241f8131fdc2c7fa383
  • Run description: ElasticSearch 5.4.0, factory defaults (BM25, k1=1.2, b=0.75), RankLib LambdaMART trained on TRECPM 2017 and 2018 scientific abstract gold data

jlpmltrin

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: jlpmltrin
  • Participant: julie-mug
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/5/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 57f1b33ae116477c5be59fe72981f30a
  • Run description: ElasticSearch 5.4.0, factory defaults (BM25, k1=1.2, b=0.75), RankLib LambdaMART trained on internal development data for the 2019 topics

jlpmtrboost

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: jlpmtrboost
  • Participant: julie-mug
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/5/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 491f8afe3748d576347831b16fafabbe
  • Run description: Same as jlpmtrcommon, but with filtered focused treatments. ElasticSearch 5.4.0, factory defaults (BM25, k1=1.2, b=0.75), RankLib LambdaMART trained on internal development data for the 2019 topics

jlpmtrcommon

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: jlpmtrcommon
  • Participant: julie-mug
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/5/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 52b0290d566b85057da4ce6a5bcd6156
  • Run description: ElasticSearch 5.4.0, factory defaults (BM25, k1=1.2, b=0.75)

MedIR1

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: MedIR1
  • Participant: CincyMedIR
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 1b8d427cfac59ee2d63186f369a961a8
  • Run description: BM25 similarity (BM25) and LMDirichlet Model (LMDirichlet). All with default settings in Elasticsearch

MedIR2

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: MedIR2
  • Participant: CincyMedIR
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/3/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 008b2b3d92f8893481663e043f9475f4
  • Run description: BM25 similarity (BM25)-currently the default setting in Elasticsearch

MedIR3

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: MedIR3
  • Participant: CincyMedIR
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/2/2019
  • Type: automatic
  • Task: abstracts
  • MD5: bd39be6bf9391127db2880bf1c853622
  • Run description: BM25 similarity (BM25)-currently the default setting in Elasticsearch

MedIR4

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: MedIR4
  • Participant: CincyMedIR
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/2/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 447f6654b3109a02743958d5e823db51
  • Run description: BM25 similarity (BM25) - currently the default setting in Elasticsearch

MedIR5

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: MedIR5
  • Participant: CincyMedIR
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/2/2019
  • Type: automatic
  • Task: abstracts
  • MD5: f1d815eb8aa8c032e3c0bee6b4f66dd5
  • Run description: BM25 similarity (BM25): the default setting in Elasticsearch

ngnd

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: ngnd
  • Participant: Brown
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: trials
  • MD5: a63246f412d51f7262060d4316047415
  • Run description: BM25F, Whoosh

rf1_f_100

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: rf1_f_100
  • Participant: CSIROmed
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/2/2019
  • Type: automatic
  • Task: trials
  • MD5: b49434519175d669c77877a1a811f89a
  • Run description: Solr's BM25 with ExtraTrees (100 trees) LTR re-ranking and age/gender filtering

rf2_f_50

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: rf2_f_50
  • Participant: CSIROmed
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/2/2019
  • Type: automatic
  • Task: trials
  • MD5: 32e365bf4828674c2a24efc80ff7340e
  • Run description: Solr's BM25 with ExtraTrees (300 trees) LTR re-ranking and age/gender filtering

rrf_1

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: rrf_1
  • Participant: Brown
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: trials
  • MD5: 75353e48db105558d4fb6308c476171b
  • Run description: BM25F, Whoosh

rrf_2

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: rrf_2
  • Participant: Brown
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: trials
  • MD5: b6b0946abfdbfdf9c1773412f8783252
  • Run description: BM25F, Whoosh

run3

Results | Participants | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: run3
  • Participant: WCMC
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: abstracts
  • MD5: fc13b94d51646ce886dd1b8c6894eaf0
  • Run description: Ensemble of deep model learning models without PM filter

run4

Results | Participants | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: run4
  • Participant: WCMC
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 1948ce0de32571ea5254a15f0db98fd3
  • Run description: The ensemble of deep model learning models with PM filter

run5

Results | Participants | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: run5
  • Participant: WCMC
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: abstracts
  • MD5: b9d4f237abbbfd0ae8ee383696feca21
  • Run description: The ensemble of deep model learning models with the ensemble PM filter

sa_base

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: sa_base
  • Participant: ECNU-ICA
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 8cb3a96f9c2158c25f68ce1bf04d76fd
  • Run description: BM25 with custom rerank method

sa_base_rr

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: sa_base_rr
  • Participant: ECNU-ICA
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 261a458b21fabdab1f46a36a35598c58
  • Run description: BM25 with custom rerank method

SA_bc

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: SA_bc
  • Participant: POZNAN
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/5/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 5473314523fc94132786021dd053ba89
  • Run description: various models concatenated with borda count

SA_DPH_letor

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: SA_DPH_letor
  • Participant: POZNAN
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/5/2019
  • Type: automatic
  • Task: abstracts
  • MD5: cef071351c9ffc204fcd9d19f8ae1a7d
  • Run description: DPH model with l2r terrier implementation

sa_ft

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: sa_ft
  • Participant: ECNU-ICA
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: abstracts
  • MD5: babf906b1840066359a098ca31a8353b
  • Run description: BM25 with custom rerank method

sa_ft_rr

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: sa_ft_rr
  • Participant: ECNU-ICA
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: abstracts
  • MD5: fed5ca3365b76044f05b6ed1c296ec69
  • Run description: BM25 with custom rerank method

SA_LGD_letor

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: SA_LGD_letor
  • Participant: POZNAN
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/5/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 4285cf232279cb8836e9832b115435bb
  • Run description: LGD model with l2r terrier implementation

SAsimpleLGD

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: SAsimpleLGD
  • Participant: POZNAN
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/5/2019
  • Type: automatic
  • Task: abstracts
  • MD5: a439c020a34b106ca729495b3fbd999c
  • Run description: LGD model with no extensions terrier implementation

SIBTMct1

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: SIBTMct1
  • Participant: BITEM_PM
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 7/30/2019
  • Type: automatic
  • Task: trials
  • MD5: 493afd50b8acb53ae61701c57a7d71bb
  • Run description: Demographic datas: match or not discussed Disease: exact code Gene: exact code Other: exact or not discussed Boost: if Other is exact, a major boosting is applied to increase the score of the CT.

SIBTMct2

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: SIBTMct2
  • Participant: BITEM_PM
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 7/30/2019
  • Type: automatic
  • Task: trials
  • MD5: 6b63ff510a25097e03504df25cf9bf36
  • Run description: Demographic datas: match or not discussed Disease: exact code and extension to parents codes (direct parents, all parents, all children) thanks to NCI Thesaurus relation. Gene: exact code or regex version of the gene name Other: exact or not discussed Boost: - boosts are applied on some fields of the CT; - different boost in disease section, depending the relation with the exact disease (nearest / farest); - For the Other field, major boosting if other is exact.

SIBTMct3

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: SIBTMct3
  • Participant: BITEM_PM
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 7/30/2019
  • Type: automatic
  • Task: trials
  • MD5: 804c667695b9946abf0143da51013334
  • Run description: A run where Gene is not required and combined with ct2 (RunC). To avoid false positives, disease (exact or extended) and Other are required where Gene is not.

SIBTMct4

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: SIBTMct4
  • Participant: BITEM_PM
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 7/30/2019
  • Type: automatic
  • Task: trials
  • MD5: cf0f8d5fa25faf1e00da5a9361f7ee39
  • Run description: Demographic datas: match or not discussed Disease: exact code and extension to parents codes (direct parents, all parents, all children) thanks to NCI Thesaurus relation. Gene: exact code or regex version of the gene name. Other: exact, not discussed or extended version like synonyms of variants thanks to a list generated by our team or regex version. Boost: - boosts are applied on some fields of the CT; - different boost in disease section, depending the relation with the exact disease (nearest / farest); - For the Other field, major boosting if other is exact or extended.

SIBTMct5

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: SIBTMct5
  • Participant: BITEM_PM
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 7/30/2019
  • Type: automatic
  • Task: trials
  • MD5: 2bae18914adc15f67367bdf1f8939544
  • Run description: A run where Gene is not required and combined with ct4 (RunA). To avoid false positives, disease (exact or extended) and Other are required where Gene is not.

SIBTMlit1

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: SIBTMlit1
  • Participant: BITEM_PM
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/1/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 05a5f1336e8e2a37696c94ad806daa2a
  • Run description: First, four queries are executed (Disease + Gene + Variant; Disease + Gene; Gene + Variant; Disease + Variant) and results are combined with different weights. Results are then re-ranked based on 1) genes/diseases/drugs occurrences, 2) demographic occurrences (age and gender) and 3) the presence of some pre-determined positive and negative keywords.

SIBTMlit2

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: SIBTMlit2
  • Participant: BITEM_PM
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/1/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 29c1fbb6b6b8457c25be67a42028f7f4
  • Run description: This run re-ranks results of run SIBTMlit1. For each drug mentioned in the results, the list of abstracts mentioning this drug is collected. Results are normalized (each top-result for each drug gets a score of 1) to obtain as many drugs as possible in the top list. Results are then merged together. A linear combination is then performed with run SIBTMlit1.

SIBTMlit3

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: SIBTMlit3
  • Participant: BITEM_PM
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/1/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 532f6b329305d6f970425abae6276472
  • Run description: This run re-ranks results of run SIBTMlit1. For each abstract, the top-3 most-frequent oncology-related drugs mentioned in the abstract are selected. Results are then re-ranked. Only abstract who do provides a not-already seen drugs are kept in the top positions, while scores of abstracts with already mentioned drugs are downgraded.

SIBTMlit4

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: SIBTMlit4
  • Participant: BITEM_PM
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/1/2019
  • Type: automatic
  • Task: abstracts
  • MD5: e6cf53d861a18d33c1661ca0f778cee0
  • Run description: A variant of run SIBTMlit1 is used, in which exact results (Disease, Gene and Variant must be present in the abstract if mentioned in the topic) are ranked before not exact resuts (one entity is missing). The SIBTIMlit4 run re-ranks results of this variant of run SIBTMlit1, using the same strategy as run SIBTMlit2.

SIBTMlit5

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: SIBTMlit5
  • Participant: BITEM_PM
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/1/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 9f85135fe7cef620b3f2c18de9e811a5
  • Run description: A run is executed on full-text (PMC) instead of Medline. Results are then combined with the run SIBTMlit2. Scores of abstracts of run SIBTMlit2 also returned by the PMC run are boosted.

sils_run1

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: sils_run1
  • Participant: UNC_SILS
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: abstracts
  • MD5: beab280f3168fea2ee5e2e14c746d80b
  • Run description: Apache Lucene, with default settings of BM25 model

sils_run2

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: sils_run2
  • Participant: UNC_SILS
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 2a8e4ffa71190ea846b7de447196c620
  • Run description: Apache Lucene, with default settings of BM25 model

sils_run3

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: sils_run3
  • Participant: UNC_SILS
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: abstracts
  • MD5: dc58af8d6fe67621c4f6eebff0eea273
  • Run description: Apache Lucene, with default settings of BM25 model

sils_run4

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: sils_run4
  • Participant: UNC_SILS
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: abstracts
  • MD5: e30d2d20878783be36ab0f193bb344fa
  • Run description: Apache Lucene, with default settings of BM25 model

simple

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: simple
  • Participant: POZNAN
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: trials
  • MD5: 4a7dfc0dd59c63a65684102fe11e4b29
  • Run description: LGD model terrier implementation

simple_letor

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: simple_letor
  • Participant: POZNAN
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: trials
  • MD5: e5b8a958d2937fd68f864ccd03a7b881
  • Run description: LGD model with L2R terrier implementation

tk1allbinary

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: tk1allbinary
  • Participant: UNIVAQ
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/1/2019
  • Type: automatic
  • Task: trials
  • MD5: cf24f8b58ab1fe9c33b914c069208998
  • Run description: Deep learning models for gene-disease extraction and pm classification. Binary demographic matching. Similarity score with parameter K=1.

tk1allfuzzy

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: tk1allfuzzy
  • Participant: UNIVAQ
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/1/2019
  • Type: automatic
  • Task: trials
  • MD5: 406f7c587f509d8a8cf357812d299f84
  • Run description: Deep learning models for gene-disease extraction and pm classification. Fuzzy demographic matching. Similarity score with parameter K=1.

tk3allfuzzy

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: tk3allfuzzy
  • Participant: UNIVAQ
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/1/2019
  • Type: automatic
  • Task: trials
  • MD5: b3e59eef6b9e42ce7db363f934b0852a
  • Run description: Deep learning models for gene-disease extraction and pm classification. Fuzzy demographic matching. Similarity score with parameter K=3.

tk3nodemogr

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: tk3nodemogr
  • Participant: UNIVAQ
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/1/2019
  • Type: automatic
  • Task: trials
  • MD5: e0a9f5604641c7fb2b1cca836fd569db
  • Run description: Deep learning models for gene-disease extraction and pm classification. Similarity score with parameter K=3.

tk3onlygnds

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: tk3onlygnds
  • Participant: UNIVAQ
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/1/2019
  • Type: automatic
  • Task: trials
  • MD5: f3bad806804ba765432d95ad5b23f546
  • Run description: Deep learning models for gene-disease extraction. Similarity score with parameter K=3.

top3fusion

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: top3fusion
  • Participant: ims_unipd
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: trials
  • MD5: 56e4f1b531f7a9198331348d72edb755
  • Run description: BM25 + query rewriting + rank fusion

top4fusion

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: top4fusion
  • Participant: ims_unipd
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 0e59c1cb23d71cd0aec98522b61cab48
  • Run description: BM25 + query rewriting + rank fusion

trial_run3

Results | Participants | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: trial_run3
  • Participant: WCMC
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: trials
  • MD5: d047be5b6f15fbecb5401393b6e997e3
  • Run description: The ensemble of deep learning models without PM filter

trial_run4

Results | Participants | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: trial_run4
  • Participant: WCMC
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: trials
  • MD5: 1647e775362220725692234a49e2dbfa
  • Run description: The ensemble of deep learning models with PM filter

trial_run5

Results | Participants | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: trial_run5
  • Participant: WCMC
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: trials
  • MD5: 2b8bce9e7a43706843136186ccae540f
  • Run description: The ensemble of deep learning models without the ensemble PM filter

trialsrun1

Results | Participants | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: trialsrun1
  • Participant: WCMC
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: trials
  • MD5: 3632a85606adb284222c8d62e2ad04fe
  • Run description: The basic model without pm filter

trialsrun2

Results | Participants | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: trialsrun2
  • Participant: WCMC
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: trials
  • MD5: 9149c0a06ba7de106496263bb16ac0fb
  • Run description: The basic model with pm filter

w2v_letor

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: w2v_letor
  • Participant: POZNAN
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: trials
  • MD5: 0824574bc238b52c4b1cbcabdd314049
  • Run description: LGD model with L2R terrier implementation

w2v_noletor

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: w2v_noletor
  • Participant: POZNAN
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/4/2019
  • Type: automatic
  • Task: trials
  • MD5: 70d3d0e770d8aa131095f13d5d3ceaec
  • Run description: simple LGD model terrier implementation

xgb_5113

Results | Participants | Proceedings | Input | Summary (trec_eval) | Summary (sample-eval) | Appendix

  • Run ID: xgb_5113
  • Participant: CSIROmed
  • Track: Precision Medicine
  • Year: 2019
  • Submission: 8/2/2019
  • Type: automatic
  • Task: abstracts
  • MD5: 380822a5dbe39346310f2f18aa594ff1
  • Run description: Solr's BM25 for the first retrieval step, LTR based on XGB model (xgboost implementation) used for re-ranking