Masked Sentence Model Based on BERT for Move Recognition in Medical Scientific Abstracts

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The results of Exp3: based on MSM integrated information_

Label	P	R	F1	Support
Background	75.26	81.18	78.11	3,077
Objectives	78.08	61.98	69.10	2,333
Methods	92.98	97.48	95.17	9,884
Results	96.02	93.74	94.87	9,713
Conclusions	94.70	94.51	94.60	4,571
Avg / Total	91.22	91.30	91.15	29,578

Data format for integrating sentence content and context_

Label	The content & context of the sentence
Methods	We selected the major journals (11 journals) collecting papers (more than 7,000) over the last five years from the top members of the research community, and read and analyzed the papers (more than 200) covering the topics.
Methods	This survey aims at reviewing the literature related to Clinical Information Systems (CIS), Hospital Information Systems (HIS), Electronic Health Record (EHR) systems, and how collected data can be analyzed by Artificial Intelligence (AI) techniques. aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa. Then, we completed the analysis using search engines to also include papers from major conferences over the same five years. We defined a taxonomy of major features and research areas of CIS, HIS, EHR systems. We also defined a taxonomy for the use of Artificial Intelligence (AI) techniques on healthcare data. In the light of these taxonomies, we report on the most relevant papers from the literature. We highlighted some major research directions and issues which seem to be promising and to need further investigations over a medium- or long-term period.

The results of Exp2: based on the context of sentences_

Label	P	R	F1	Support
Background	72.27	79.72	75.82	3,077
Objectives	70.51	60.27	64.99	2,333
Methods	90.70	89.80	90.25	9,884
Results	87.71	89.20	88.45	9,713
Conclusions	90.19	89.30	89.74	4,571
Avg / Total	86.13	86.15	86.09	29,578

Data format of sentence content_

Label	The content of the sentence
Methods	We selected the major journals (11 journals) collecting papers (more than 7,000) over the last five years from the top members of the research community, and read and analyzed the papers (more than 200) covering the topics.

The results of Exp1: based on the content of sentences_

Label	P	R	F1	Support
Background	64.37	75.85	69.64	3,077
Objectives	73.55	56.97	64.20	2,333
Methods	92.42	94.97	93.68	9,884
Results	92.08	91.09	91.58	9,713
Conclusions	84.95	81.38	83.13	4,571
Avg / Total	86.75	86.61	86.53	29,578

Comparison of the results of the experiments_

Label	Exp1	Exp2	Exp3	Exp3-Exp1	Exp3-Exp2
	F1	F1	F1	+F1	+F1
Background	69.64	75.82	78.11	8.47	2.29
Objectives	64.20	64.99	69.10	4.9	4.11
Methods	93.68	90.25	95.17	1.49	4.92
Results	91.58	88.45	94.87	3.29	6.42
Conclusions	83.13	89.74	94.60	11.47	4.86
Avg / Total	86.53	86.09	91.15	4.62	5.06

Data format of the sentence’s context_

Label

The context of the sentence

Methods

This survey aims at reviewing the literature related to Clinical Information Systems (CIS), Hospital Information Systems (HIS), Electronic Health Record (EHR) systems, and how collected data can be analyzed by Artificial Intelligence (AI) techniques. aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa aaa. Then, we completed the analysis using search engines to also include papers from major conferences over the same five years. We defined a taxonomy of major features and research areas of CIS, HIS, EHR systems. We also defined a taxonomy for the use of Artificial Intelligence (AI) techniques on healthcare data. In the light of these taxonomies, we report on the most relevant papers from the literature. We highlighted some major research directions and issues which seem to be promising and to need further investigations over a medium- or long-term period.

PubMed 20k RCT results_

	Models	F1 (PubMed 20k RCT)
Our Model	MaskedSentenceModel_BERT	91.15
Others	HSLN-RNN (Jin and Szolovits, 2018) (SOTA)	92.6
	BERT-Base (Beltagy et al., 2018)	86.19
	Sci BERT (SciVocab) (Beltagy et al., 2018)	86.80
	Sci BERT (BaseVocab) (Beltagy et al., 2018)	86.81