Figure 1
Figure 2
Figure 3
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 |