Table 1:
Articles published for few-shot learning on medical data, their publication years, data sources, search engine, which downstream tasks the literature focus, size of the training set (number of the shot), number of the entities (for few-shot NER tasks), number of the classes (for few-shot classification tasks), type of data in training and test domain.
| Study | Data source | Research aim | Size of training set | Number of entities / classes | Dataset domain |
|---|---|---|---|---|---|
| Rios et al., 2018 [16] | MIMIC II [29] MIMIC III [30] | Multi-label Text Classification | Frequent group (all labels that occur >5 times), the few-shot group (labels that occur between 1 and 5 times), and the zero-shot group (labels that never occur in the training dataset), reconstructed | Not mentioned 1 | Medical, discharge summaries annotated with a set of ICD-9 diagnosis and procedure labels |
| Rios et al., 2018 [31] | MIMIC II [29] MIMIC III [30] | Multi-label Text Classification | Original dataset, with no reconstruction | Not mentioned 1 | Medical |
| Hofer et al., 2018 [10] | i2b2 2009 32 i2b2 2010 33 i2b2 2012 34 CoNLL-2003 [35] BioNLP-2016 [36] MIMIC-III [30] UK CRIS [37, 38] | NER | 10-shot | Not mentioned 1 | Medical and nonmedical (e.g, news) |
| Pham et al., 2018 [39] | The Europarl datasets [40] IWSLT17[41] The UFAL Medical Corpus HIML2017 dataset 3 | Neural Machine Translation (NMT) | One-Shot | N/A 2 | German→English: medical; English→Spanish, the proceedings of the European Parliament and data from TED |
| Yan et al., 2018 [42] | Multigames dataset [43] HCR dataset [44] SS-Tweet dataset [45] SemEval-2013 Dataset (SemEval b) [46] | Text Classification | Few-shot, but reconstructed | Multi games: 3 HCR: 5 SS-Tweet: 3 SemEval-2013 Dataset: 3 | Tweets about sentiment and games, and health Care Reform (HCR) data |
| Manousogiannis et al., 2019 [47] | Tweets (provided by SMM4H 2019) [48] | NER | Original dataset, with no reconstruction | 1, ADR with 319 MEDDRA codes | Medical (ADR) |
| Gao et al., 2019 [49] | FewRel dataset [50] | Relation Classification | 5-Way 1-Shot / 5-Way 5-Shot / 10-Way 1-Shot / 10-Way 5-Shot | 25 | Wikipedia corpus 10 and Wikidata knowledge bases |
| Lara-Clares et al., 2019 [51] | MEDDOCAN shared task dataset [52] | NER | 500 clinical cases, with no reconstruction | 29 | Clinical |
| Ferré et al., 2019 [53] | BB-norm dataset [54] | Entity Nor mal izat io n | Original dataset with no reconstruction and zero-shot | Not mentioned 1 | Biological |
| Hou et al., 2020 [55] | Snips dataset [56] | Slot Tagging (NER) | 1-shot and 5-shot | 7 | Six of Weather, Music, PlayList, Book (including biomedical), Search Screen (including biomedical), Restaurant and Creative Work. 11 |
| Sharaf et al., 2020 [57] | Ten different datasets collected from the Open Parallel Corpus (OPUS) [58] | Neural Machine Translation (NMT) | Sizes ranging from 4k to 64k training words (200 to 3200 sentences), but reconstructed | N/A 2 | Bible, European Central Bank, KDE, Quran, WMT news test sets, Books, European Medicines Agency (EMEA), Global Voices, Medical (ufal-Med), TED talks |
| Lu et al., 2020 [59] | MIMIC II [29] MIMIC III [30] EU legislation dataset [60] | Multi-label Text Classification | 5-shot for MIMIC II and III, 50-shot for EU legislation | MIMIC II: 9 MIMIC III: 15 EU legislation: 5 | Medical |
| Jia et al., 2020 [61] | BioNLP13PC BioNLP-13CG [62] CoNLL-2003 dataset [35] Broad Twitter dataset [63] Twitter dataset [64] CBS SciTech News dataset [65] | NER | Four few-shot (reconstructed) and zero-shot | CoNLL: 4 Broad Twitter: 3 Twitter: 4 BioNLP13PC: >=3 BioNLP13CG: >=3 CBS News: 4 | For the BioNLP dataset, BioNLP13PC as the source domain dataset; In the Broad Twitter dataset, the CoNLL-2003 as the source domain dataset; In the Twitter dataset,the CoNLL- 2003 as the source domain dataset |
| Chalkidis et al., 2020 [66] | EURLEX5TK [60] MIMIC III [30] AMAZON13K [67] | Multi-label Text Classification | The labels are divided into frequent (≥ 50), few-shot (≤ 50), and zero-shot | Not mentioned 1 | English legislative documents, English discharge summaries from US hospitals, English product descriptions from Amazon |
| Lwowski et al., 2020 [68] | Tweets about COVID-19 [69] | Text Classification | 100 tweets, with no reconstructed | 4 | Tweets about COVID-19 |
| Hou et al., 2020 [9] | Dialogue utterances from the AIUI open dialogue platform of iFlytek4 | Dialogue Language Understanding: includes two sub-tasks: Intent Detection (classification) and Slot Tagging (sequence labeling) | 1-shot, 3-shot, 5-shot and 10-shot | Train Domains: 45 Dev Domains: 5 Test Domains: 9 | General dialogue (including health domain) |
| Chen et al., 2020 [70] | WIKIBIO dataset [71] | Natural Language Generation (NLG) | Dataset sizes: 50, 100, 200 and 500, with no reconstruction | N/A 2 | Books, Songs and Human domain (including biomedical) |
| Vaci et al., 2020 [72] | UK-CRIS system that provides a means of searching and analysing deidentified clinical case records from 12 National Health Service Mental Health Trusts [37, 38] | NER | Original dataset, with no reconstruction | 7 | Clinical |
| Huang et al., 2020 [73] | 10 public datasets | NER | 5-shot, 10%, 100% | CoNLL: 4 Onto: 18 WikiGold: 4 WNUT: 6 Movie: 12 Restaurant: 8 SNIPS: 53 AT IS: 79 Multiwoz: 14 I2B2: 23 | 10 public datasets, different domains |
| Chen et al., 2020 [74] | MRI image dataset MRI text reports5 | Text Classifie ait ion | Original dataset, with no reconstruction | Not mentioned 1 | MRI data |
| Yin et al., 2020 [75] | MLEE [76] BioNLP’13-GE [62] | Sequence Tagging (NER) | 5-way-10-shot, 5-way-15-shot, and 5-way-20-shot | 5 | Biological event |
| Goodwin et al., 2020 [77] | Tensor Flow DataSets catalogue6 | Abstractive Summarization | Zero-shot and 10-shot | N/A 2 | 3 general domain & 1 consumer health |
| Yang et al., 2020 [78] | OntoNotes 5.0 [79] CoNLL-2003 [35] I2B2 2014 [80] WNUT 2017 [81]11 | NER | 1-shot and 5-shot | Onto: 18 CoNLL: 4 I2B2–14: 23 WNUT: 6 | Three of general, news, medical and social media |
| Hartmann et al., 2021 [82] | The IULA dataset [83] The NUBES dataset [84] The FRENCH dataset [85] Negation Scope Resolution datasets | NER | Zero-shot, with no reconstruction | 1, Negation | No training data for the clinical datasets |
| Fi vez et al., 2021 [86] | SNOMED-CT17 ICD-10 | Name Normalization | Zero-shot, with no reconstructed | N/A 2 | Biomedical |
| Lu et al., 2021 [87] | Constructed a dataset 8 based on Wei bo for the research of few-shot rumor detection, and use PHEME dataset [88] | Rumor Detection (NER) | For the Weibo dataset: 2-way 3-event 5-shot 9-query; for PHEME dataset: 2-way 2-event 5-shot 9-query | Weibo: 14 PHEME: 5 | Source posts and comments from Sina Weibo related to COVID-19 |
| Ma et al., 2021 [89] | CCLE CERES-corrected CRISPR gene disruption scores G DSC1000 dataset PDTC dataset PDX dataset9 | Drug-response Predictions | 1-shot, 2-shot, 5-shot, and 10-shot | N/A 2 | Biomedical |
| Kormilitzin et al., 2021 [90] | MIMIC-III [30] UK-CRIS datasets [37, 38] | NER | 25%, 50%, 75% and 100% of the training set, with no reconstruction | 7 | Electronic health record |
| Guo et al., 2021 [91] | BioNLP Shared Task 2011 and 2019 [54] structured biological datasets | NER | 100%, 75%, 50%, 25%, 0% of training set, with no reconstructed | Not mentioned 1 | Biomedical entities |
| Lee et al., 2021 [92] | COVID19-Scientific [93] COVID19-Social [94] FEVER [95] | Fact- Checki ng (close to Text Classification) | 2-shot, 10-shot, and 50-shot | Not mentioned 1 | Facts about COVID-19 |
| Fi vez et al., 2021 [96] | ICD-10 SNOMED-CT7 | Name Nor mal izat io n | 15-shot | N/A 2 | Biomedical |
| Xiao et al., 2021 [97] | FewRel dataset | Relation Classification | 5-Way-1-Shot 5-Way-5-Shot 10-Way-l-Shot 10-Way-5-Shot | Not mentioned 1 | Wikipedia and Wikidata |
| Ziletti et al., 2021 [98] | MedDRA ontology | Medical Coding / classification | Zero-shot Few-shot | 26,000 distinct classes | Synonyms and biomedical text |
| Ye et al., 2021 [99] | Huggingface Datasets | Cross-task Generalization | Few-shot More-shot | N/A 2 | 160 datasets |
| Aly et al., 2021 [100] | OntoNotes-ZS MedMentions-ZS | NER and classification | zero-shot | Train: 19 classes Dev: 12 classes Test: 12 classes | General, Biomedical |
| Wright et al., 2021 [101] | Curated a dataset of paired sentences from abstracts and associated press releases, labeled by experts for exaggeration based on their claim strength, and ScienceDaily | Information Extraction | 100-shot | N/A 2 | A science reporting website which aggregates and re-releases press releases from a variety of sources |
| Lee et al., 2021 [102] | C0NLLO3 Ontonotes 5.0 BC5CDR | NER | 2 5-shot and 50-shot | Not mentioned 1 | General 12 |
| Wang et al., 2022 [103] | i2b2 2010 dataset i2b2 2012 dataset MIMIC-III dataset BioScope NegEx Chia | Classification | Whole datasets but few-shot classes | Not mentioned 1 | Annotates a corpus 13 of assertions |
| Yan et al., 2022[104] | 677 full-text articles were obtained as neuroimaging corpora | NER | Whole datasets | 10 categories of neuroimaging entities and 55 categories of neuroimaging interactions | Neuroimaging entities and their interactions |
| Lin et al., 2022 [105] | Neuroimaging event mention set | Information Extraction | Whole datasets but few-shot classes | 788 “Activate” event mentions, 128 “Deactivate” event mentions, 1169 “Effect” event mentions, 665 “Perform Experiment” event mentions, 266 “Acquisition” event mentions, and 315 “Perform Analysis” event mentions. | Ne uro imaging event |
| Riveland et al., 2022 [106] | Psychophysical tasks | Classfication | Zero-shot | 4 categories | Psychophysical tasks |
| Navarro et al., 2022 [107] | 27 recorded conversations between general practitioners and patients at Primary Care facilities | Abstractive summarization | Zero-shot 10-shot 20-shot 50-shot | N/A 2 | Medical dialogues from various online chats |
| Das et al., 2022 [108] | OntoNotes C0NLLO3 WNUTir GUM Few-NERD | NER | 1–2 shot-5-way 5–10 shot-5-way 1–2 shot-10-way 5–10 shot-10-way | Not Mentioned 1 | General (OntoNotes 5.0), Medical (I2B2), News (C0NLLO3), Social (WNUT17) 14 |
| Ma et al., 2022 [109] | C0NLLO3 WNUT1T JNLPBA NCBI-disease 12B2–14 datasets | NER | 1-shot, 5-shot, 20-shot, 50-shot | Not mentioned 1 | General, Social, Biomedical |
| Par mar et al., 2022 [110] | 32 datasets | Multi-Task Learning | 32-shot, 100-shot, 1000-shot, 2000-shot | Not mentioned 1 | Biomedical and health data |
| Boulanger et al., 2022 [111] | I2B2 CoNLL | NER | 50-shot, 100-shot, 250-shot, 500-shot, 1000-shot | Not mentioned 1 | General and Biomedical data |
| Yeh et al., 2022 [112] | ChemProt dataset | Relation Extraction | Zero-shot | Not mentioned 1 | Scientific paper abstracts annotated with 6 relation types between the chemicals and genes in sentences |
| Pan et al., 2022 [113] | MoviesQA NewsQA BioQA CovidQA | Question Answering | Zero-shot | Not mentioned 1 | Movies, News, Biomedical, and COVID-19 domains |
| Wadden et al., 2022 [114] | Scientific claim verification datasets | Scientific claim verification | Zero-shot few-shot | N/A 2 | SCIFACT, Health Ver, COVIDFact, FEVER, EVIDENCE- INFERENCE, PUBMEDQA |
| Li et al., 2022[115] | FewRel 1.0 FewRel 2.0 | Relation classification | 5-way-1-shot, 5-way-5-shot, 10-way-1-shot, 10-way-5-shot | 100 relations split into training, validation and test sets with respectively 64, 16 and 20 relations without overlapping | Relations from PubMed articles |
| Zhang et al., 2022 [116] | 7 STS tasks STS 2012–2016 STS Benchmark S ICK-Relatedness | Natural Language Inference (NLI) | 16 labeled instances per class | Not mentioned 1 | News, Biomedical, Search snippets and Social media data |
1
The research aim of this paper is text classification or NER, but the size of training set is Not mentioned in the paper.
2
The research aim of this paper is neither text classification nor NER.
3
UFAL Medical Corpus v.1.0 and HIML2017 dataset: http://aiui.xfyun.cn/index-aiui. Last accessed November 22, 2021.
4
iFlytek: http://aiui.xfyun.cn/index-aiui. Last accessed November 22, 2021.
5
Those datasets are not released.
6
TensorFlow DataSets: https://www.tensorflow.org/datasets. Last accessed November 22, 2021.
7
SNOMED-CT1: https://www.snomed.org. Last accessed November 22, 2021.
8
A novel dataset proposed by this paper: https://github.com/jncsnlp/Sina-Weibo-Rumors-for-few-shot-learning-research. Last accessed November 22, 2021.
9
Links are provided in the original paper.
10
Test data is biomedical literature with UMLS, a large-scale biomedical knowledge base.
11
The remaining one class is used at test time.
12
PubMed articles and chemical-disease texts are used as additional test data.
13
Patient eligibility data and 3 assertion types are used for test data: Present, Absent & Possible
14
GUM, Few-NERD used as test data.