Semi manual data processing

Important: Do not relay blindly on the score or other image statistics as a means of selecting the best crystal - always inspect the results displayed in the Cassette Details page. Figure Cassette details navigation in Web-Ice Screening tab: Header displays the image header; Spot Statistics displays the results of image analysis before autoindexing; Crystal image shows a camera shot of the crystal; Autoindex shows the autoindex results and image score; Details is a directory browser and image display tool to inspect log and output files.

Figure How to generate a new run in Web-Ice. Figure Selecting a beamline in Web-Ice. Important: Never collect data without inspecting the test diffraction images and the predicted pattern.

Figure Logging to the data processing servers from the Linux Xfce panel. Figure System load window. For additional help setting up data collection, please consult the Blu-Ice documentation. Automated crystal screening The high throughput screening system implemented at SSRL makes it possible to automatically collect and analyze test images and fully characterize the sample in a semi- or fully automated fashion.

In sequential file organization , records are stored in a sorted order using a. In random file organization , records are stored in the file randomly and accessed directly, while in indexed —sequential , the records are stored sequentially but accessed directly using an index. Records are in a file are stored and accessed one after another on a storage medium iv Indexed sequencial file organization method.

Similar to sequential method, only that an index is used to enable the computer to locate individual records on the storage media. Strictly speaking, it is a processing mode : the execution of a series of programs each on a set or "batch" of inputs, rather than a single input which would instead be a custom job.

However, this distinction has largely been lost, and the series of steps in a batch process are often called a "job" or "batch job". Disadvantages - Users are unable to terminate a process during execution, and have to wait until execution completes.

Kirjaudu Luo tili. Introduction Data procesing refers to the transformating raw data into meaningful output. Processing is the transformation of the input data to a more meaningful form information in the CPU Output is the production of the required information, which may be input in future.

The difference between data collection and data capture. Underflow Truncation: 0. Human error, whether malicious or unintentional. Transfer errors, including unintended alterations or data compromise during transfer from one device to another. Compromised hardware, such as a device or disk crash. Computer Studies.

Form 1. FORM 2. Harvard Dataverse: Appendix for base review. Harvard Dataverse: Available datasets for SR automation. Data are available under the terms of the Creative Commons Attribution 4. Background: The reliable and usable semi automation of data extraction can support the field of systematic review by reducing the workload required to gather information about the conduct and results of the included studies. This living systematic review examines published approaches for data extraction from reports of clinical studies.

Full text screening and data extraction are conducted within an open-source living systematic review application created for the purpose of this review. This iteration of the living review includes publications up to a cut-off date of 22 April Results: In total, 53 publications are included in this version of our review.

Over 30 entities were extracted, with PICOs population, intervention, comparator, outcome being the most frequently extracted. Conclusions: This living systematic review presents an overview of semi automated data-extraction literature of interest to different types of systematic review. We identified a broad evidence base of publications describing data extraction for interventional reviews and a small number of publications extracting epidemiological or diagnostic accuracy data.

The lack of publicly available gold-standard data for evaluation, and lack of application thereof, makes it difficult to draw conclusions on which is the best-performing system for each data extraction target. With this living review we aim to review the literature continually. In a systematic review, data extraction is the process of capturing key characteristics of studies in structured and standardised form based on information in journal articles and reports. It is a necessary precursor to assessing the risk of bias in individual studies and synthesising their findings.

Interventional, diagnostic, or prognostic systematic reviews routinely extract information from a specific set of fields that can be predefined. The data extraction task can be time-consuming and repetitive when done by hand. This creates opportunities for support through intelligent software, which identify and extract information automatically.

When applied to the field of health research, this semi-automation sits at the interface between evidence-based medicine EBM and data science, and as described in the following section, interest in its development has grown in parallel with interest in AI in other areas of computer science.

This review is, to the best of our knowledge, the only living systematic review of data extraction methods. We have identified four previous reviews of tools and methods, 2 — 5 two documents providing overviews and guidelines relevant to our topic, 6 , 7 and an ongoing effort to list published tools for different parts of the systematic reviewing process.

A recent systematic review of machine-learning for systematic review automation, published in Portuguese, included 35 publications. The authors examined journals in which publications about systematic review automation are published, and conducted a term-frequency and citation analysis.

They categorised papers by systematic review task, and provided a brief overview of data extraction methods. In , Tsafnat et al. The reviewers focused on tasks related to PICO classification and supporting the screening process. Beller et al. They conclude that tools facilitating screening are widely accessible and usable, while data extraction tools are still at piloting stages or require a higher amount of human input.

The systematic reviews from to present an overview of classical machine learning and natural language processing NLP methods applied to tasks such as data mining in the field of evidence-based medicine.

At the time of publication of these documents, methods such as topic modelling Latent Dirichlet Allocation and support vector machines SVM were considered state-of-the art for language models. The age of these publications means that the latest static or contextual embedding-based and neural methods are not included. These newer methods, 9 however, are used in contemporary systematic review automation software which will be reviewed in the scope of this living review.

We aim to review published methods and tools aimed at automating or semi-automating the process of data extraction in the context of a systematic review of medical research studies. We will do this in the form of a living systematic review, keeping information up to date and relevant to the challenges faced by systematic reviewers at any time.

Our objectives in reviewing this literature are two-fold. First, we want to examine the methods and tools from the data science perspective, seeking to reduce duplicate efforts, summarise current knowledge, and encourage comparability of published methods.

Second, we seek to highlight the added value of the methods and tools from the perspective of systematic reviewers who wish to use semi automation for data extraction, i. Is it reliable? We address these issues by summarising important caveats discussed in the literature, as well as factors that facilitate the adoption of tools in practice.

This review was conducted following a preregistered and published protocol. Any deviations from the protocol have been described below. We are conducting a living review because the field of systematic review semi automation is evolving rapidly along with advances in language processing, machine-learning and deep-learning. The process of updating started as described in the protocol 11 and is shown in Figure 1. Articles from the dblp and IEEE are added every two months.

This image is reproduced under the terms of a Creative Commons Attribution 4. The decision for full review updates is made every six months based on the number of new publications added to the review.

For more details about this, please refer to the protocol or to the Cochrane living systematic review guidance. In between updates, the screening process and current state of the data extraction is visible via the living review website.

We searched five electronic databases, using the search methods previously described in our protocol. Searches on the arXiv computer science and dblp were conducted on full database dumps using the search functionality described by McGuinness and Schmidt.

Originally, we planned to include a full literature search from the Web of Science Core Collection. This reduced the Web of Science Core Collection publications to abstracts, which were added to the studies in the initial screening step. The dataset, code and weights of trained models are available in Underlying data: Appendix C. This decision was made to facilitate continuous reference retrieval.

Screening and data extraction were conducted as stated in the protocol. In short, we initially screened all retrieved publications using the Abstrackr tool. All abstracts were screened by two independent reviewers. Conflicting judgements were resolved by the authors who made the initial screening decisions. Full texts screening was conducted in a similar manner to abstract screening but used our web application for living systematic reviews described in the following section.

We previously developed a web application to automate reference retrieval for living review updates see Software availability 13 , to support both abstract and full text screening for review updates, and to manage the data extraction process throughout. This web application is already in use by another living review. All extracted data are stored in a database.

Figures and tables can be exported on a daily basis and the progress in between review updates is shared on our living review website. The full spreadsheet of items extracted from each included reference is available in the Underlying data. We automated the export of PDF reports for each included publication. Calculation of percentages, export of extracted text, and creation of figures was also automated.

All data and code are free to access. In the protocol we stated that data would be available via an OSF repository. Instead, the full review data are available via the Harvard Dataverse, as this repository allows us to keep an assigned DOI after updating the repository with new content for each iteration of this living review.

We also stated that we would screen all publications from the Web of Science search. We added a data extraction item for the type of information which a publication mines e.

P, IC, O into the section of primary items of interest, and we moved the type of input and output format from primary to secondary items of interest. We decided not to speculate if a dataset is likely to be available in the future and chose instead to record if the dataset was available at the time when we tried to access it. In this current version of the review we did not yet contact the authors of the included publications. This decision was made due to time constraints, however reaching out to authors is planned as part of the first update to this living review.

Our database searches identified 10, publications after duplicates were removed see Figure 2. We identified an additional 23 publications by screening the bibliographies of included publications, in addition to reviewing the tools contained in the SRToolbox.

For future review updates we will adapt the search strategies and conduct searches in sources such as the ACL. This iteration of the living review includes 53 publications, summarised in Table 1 in Underlying data Twelve of these were among the additional 23 publications.

In total, 79 publications were excluded at the full text screening stage, with the most common reason for exclusion being that a study did not fit target entities or target data.

In most cases, this was due to the text-types mined in the publications. Electronic health records and non-trial data were common, and we created a list of datasets that would be excluded in this category see more information in Underlying data: Appendix B Some publications addressed the right kind of text but were excluded for not mining entities of interest to this review. Millard, Flach and Higgins 17 and Marshall, Kuiper and Wallace 18 looked at risk of bias classification, which is beyond the scope of this review.

Luo et al. Rathbone et al. We classified this article as not having any original data extraction approach because it does not create any structured outputs specific to P, IC, or O. Malheiros et al. Similarly, Fabbri et al. Other systematic reviewing tasks that can benefit from automation but were excluded from this review are listed in Underlying data: Appendix B.

Figure 3 shows aspects of the system architectures implemented in the included publications. A short summary of these for each publication is provided in Table 1 in Underlying data. Although SVM is also binary classifier, it was assigned as separate category due to its popularity. This figure shows that there is no obvious choice of system architecture for this task. Results are divided into different categories of machine-learning and natural language processing approaches and coloured by the year of publication.

More than one architecture component per publication is possible. They are frequently used in studies published between and now. Rule-bases, including approaches using heuristics, wordlists, and regular expressions, were one of the earliest techniques used for data extraction in EBM literature.

It remains one of the most frequently used approaches to automation. Although used more frequently in the past, the five publications published between and now that use this approach combine it with conditional random fields CRF , 24 use it alone, 25 , 26 use it with SVM 27 or use it with other binary classifiers. Recurrent neural networks RNN , CNN, and LSTM networks require larger amounts of training data, but by using embeddings or pre-training algorithms based on unlabelled data they have become increasingly more interesting in fields such as data extraction for EBM, where high-quality training data are difficult and expensive to obtain.

Precision i. This is reflected in Figure 4 , which shows that at least one of these metrics was used in almost all of the 53 included publications. Real-life evaluations, such as the percentage of outputs needing human correction, or time saved per article, were reported by one publication, 30 and an evaluation as part of a wider screening system was done in another.

There were several approaches and justifications of using macro- or micro-averaged precision, recall, or F1 scores in the included publications. Micro or macro scores are computed in multi-class cases, and the final scores can have a difference if the classes in a dataset are imbalanced as is the case in most datasets used in the included studies for this review.

Micro and macro scores were reported by, 30 , 41 whereas 26 , 42 reported micro across documents, and macro across the classes. Micro scores were used by 41 for class-level results.

Micro scores were also used by, 44 — 46 and were used in the evaluation script of. Most data extraction is carried out on abstracts See Table 1 in Underlying data , 86 and Table 5. Abstracts are the most practical choice, due to the possibility of exporting them along with literature search results from databases such as MEDLINE. Descriptions of the benefits of using full texts for data extraction include having access to a more complete dataset, while the benefits of using titles include lower complexity for the data extraction task.

Figure 6 shows that RCTs are the most common study design texts used for data extraction in the included publications see also extended Table 1 in Underlying data This is not surprising, because systematic reviews of interventions are the most common type of systematic review, and they are usually focusing on evidence from RCTs. Systematic reviews of diagnostic test accuracy are less frequent, and only one included publication specifically focused on text and entities related to these studies, 48 while another mentioned diagnostic procedures among other fields of interest.

Commonly, randomized controlled trials RCT text was at least one of the target text types used in the included publications. Mining P, IC, and O elements is the most common task performed in the literature of systematic review semi- automation see Table 1 in Underlying data , 86 and Figure 7.

However, some of the less-frequent data extraction targets in the literature can be categorised as sub-classes of a PICO. P, population; I, intervention; C, comparison; O, outcome. Notably, seven publications annotated or worked with datasets that differentiated between intervention and control arms. The key for this assembly type is that it includes both manual functions as well as machine-aided assembly.

This means the product is loaded into feed systems or can be transferred from another system that will automatically load into the next step of the assembly process.

The automated system then completes the entire assembly including testing, inspection and unloading. If there is human interaction, it can be as simple as responding to system prompts. When choosing an assembly system your budget and volumes largely dictate which type will work best for you.

At INVOTEC, we recommend our customers complete a return on investment calculation first to determine the balance between cost and features for them and their assembly system.

This will lead them towards a solution that meets their budgetary needs over the life of the machine as well as their manufacturing goals for the product. With that being said, each assembly type can be appropriate for different manufacturing goals.

Manual assembly systems, for example, are often used when a company wants to make their system more efficient, less cumbersome or reduce ergonomic issues for their workers. These systems could include newly designed fixtures or slight process changes.