scholarly journals Information extraction and transparency in big data processing

2016 ◽  
Author(s):  
Ieuan Clay
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Processing ◽  
Information Extraction ◽  
Health Authority ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Research Community ◽  
Open Approach ◽  
Concerted Effort

Developing new endpoints for mobility is an important strategic aim for many groups both in industry and academia and the focus of a growing field. Bringing those novel endpoints to health authority acceptance for clinical decision making will require a concerted effort from this research community. This in turn will require openness and transparency; sharing data, methods and findings. Here we discuss challenges within the field to such an open approach and give examples of how they might be overcome.

scholarly journals Information extraction and transparency in big data processing

2016 ◽  
Author(s):  
Ieuan Clay
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Processing ◽  
Information Extraction ◽  
Health Authority ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Research Community ◽  
Open Approach ◽  
Concerted Effort

Developing new endpoints for mobility is an important strategic aim for many groups both in industry and academia and the focus of a growing field. Bringing those novel endpoints to health authority acceptance for clinical decision making will require a concerted effort from this research community. This in turn will require openness and transparency; sharing data, methods and findings. Here we discuss challenges within the field to such an open approach and give examples of how they might be overcome.

Mental Health Clinical Decision Support Exploiting Big Data

2019 ◽  
pp. 160-184 ◽  
Author(s):  
Jan Kalina
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

The complexity of clinical decision-making is immensely increasing with the advent of big data with a clinical relevance. Clinical decision systems represent useful e-health tools applicable to various tasks within the clinical decision-making process. This chapter is devoted to basic principles of clinical decision support systems and their benefits for healthcare and patient safety. Big data is crucial input for clinical decision support systems and is helpful in the task to find the diagnosis, prognosis, and therapy. Statistical challenges of analyzing big data in psychiatry are overviewed, with a particular interest for psychiatry. Various barriers preventing telemedicine tools from expanding to the field of mental health are discussed. The development of decision support systems is claimed here to play a key role in the development of information-based medicine, particularly in psychiatry. Information technology will be ultimately able to combine various information sources including big data to present and enforce a holistic information-based approach to psychiatric care.

scholarly journals Economic evaluations of big data analytics for clinical decision-making: a scoping review

2020 ◽  
Vol 27 (9) ◽  
pp. 1466-1475
Author(s):  
Lytske Bakker ◽  
Jos Aarts ◽  
Carin Uyl-de Groot ◽  
William Redekop
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Economic Impact ◽  
Scoping Review ◽  
Data Analytics ◽  
Clinical Decision Making ◽  
Big Data Analytics ◽  
Clinical Decision ◽  
Economic Evaluations ◽  
Definition Of

Abstract Objective Much has been invested in big data analytics to improve health and reduce costs. However, it is unknown whether these investments have achieved the desired goals. We performed a scoping review to determine the health and economic impact of big data analytics for clinical decision-making. Materials and Methods We searched Medline, Embase, Web of Science and the National Health Services Economic Evaluations Database for relevant articles. We included peer-reviewed papers that report the health economic impact of analytics that assist clinical decision-making. We extracted the economic methods and estimated impact and also assessed the quality of the methods used. In addition, we estimated how many studies assessed “big data analytics” based on a broad definition of this term. Results The search yielded 12 133 papers but only 71 studies fulfilled all eligibility criteria. Only a few papers were full economic evaluations; many were performed during development. Papers frequently reported savings for healthcare payers but only 20% also included costs of analytics. Twenty studies examined “big data analytics” and only 7 reported both cost-savings and better outcomes. Discussion The promised potential of big data is not yet reflected in the literature, partly since only a few full and properly performed economic evaluations have been published. This and the lack of a clear definition of “big data” limit policy makers and healthcare professionals from determining which big data initiatives are worth implementing.

Mental Health Clinical Decision Support Exploiting Big Data

2021 ◽  
pp. 983-1000
Author(s):  
Jan Kalina
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

The complexity of clinical decision-making is immensely increasing with the advent of big data with a clinical relevance. Clinical decision systems represent useful e-health tools applicable to various tasks within the clinical decision-making process. This chapter is devoted to basic principles of clinical decision support systems and their benefits for healthcare and patient safety. Big data is crucial input for clinical decision support systems and is helpful in the task to find the diagnosis, prognosis, and therapy. Statistical challenges of analyzing big data in psychiatry are overviewed, with a particular interest for psychiatry. Various barriers preventing telemedicine tools from expanding to the field of mental health are discussed. The development of decision support systems is claimed here to play a key role in the development of information-based medicine, particularly in psychiatry. Information technology will be ultimately able to combine various information sources including big data to present and enforce a holistic information-based approach to psychiatric care.

scholarly journals Quality Improvement through Intensive Care Registries: The Value of Big Data in Clinical Decision Making

2019 ◽  
Author(s):  
Ali Sanaei ◽  
Mohammad Mehdi Sepehri
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Critical Care ◽  
Intensive Care ◽  
Quality Improvement ◽  
Data Collection ◽  
Clinical Decision Making ◽  
Clinical Care ◽  
Clinical Decision ◽  
Electronic Database

Background: Quality of Intensive care has got more attention in case of the high cost of healthcare and the potential for harm. Poor-quality care causes high cost and quality improvement initiatives in the ICU lead to an improvement in outcomes as well as a decrease in costs. One of the crucial tools that allow physicians and nurses to monitor change in a quality improvement effort is the development of an electronic database for data collection and reporting. The objective of Intensive Care Registries is to create a high-quality registry of patients through a collaboration of academic health centers performing uniform data collection with the purpose of improving the quality and accuracy of healthcare decisions and provide a data-driven clinical decision support system for critical care medicine. Methods: This article reviews real-world data sources in healthcare and considers registry as the main tool to address health services and outcomes research questions in critical care, and briefly describes objective, inputs and outputs of intensive care registries. As it can be comprehended from library research, the combination of patient clinical care data, quality parameters, and ICU operating costs, integrated into an electronic database, provides a valuable tool for quality improvement and overall efficiency of offered care. Results: Using Big Data effectively within ICUs for supporting clinical decision making can lead to predict numerous diseases and help to discover new patterns in healthcare. The ability to process multiple high-speed clinical data streams from multiple centers could dramatically improve both healthcare efficiency and patient outcomes. Conclusion: To gain this goal, developing reliable and standardized health analytics platforms as well as quality improvement processes that translate analytical results into new clinical guidelines, is recommended.

scholarly journals Application of machine learning missing data imputation techniques in clinical decision making: taking the discharge assessment of patients with spontaneous supratentorial intracerebral hemorrhage as an example

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Huimin Wang ◽  
Jianxiang Tang ◽  
Mengyao Wu ◽  
Xiaoyu Wang ◽  
Tao Zhang
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Missing Data ◽  
Intracerebral Hemorrhage ◽  
Data Processing ◽  
Missing Values ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Machine Learning Algorithms ◽  
Traditional Methods

Abstract Background There are often many missing values in medical data, which directly affect the accuracy of clinical decision making. Discharge assessment is an important part of clinical decision making. Taking the discharge assessment of patients with spontaneous supratentorial intracerebral hemorrhage as an example, this study adopted the missing data processing evaluation criteria more suitable for clinical decision making, aiming at systematically exploring the performance and applicability of single machine learning algorithms and ensemble learning (EL) under different data missing scenarios, as well as whether they had more advantages than traditional methods, so as to provide basis and reference for the selection of suitable missing data processing method in practical clinical decision making. Methods The whole process consisted of four main steps: (1) Based on the original complete data set, missing data was generated by simulation under different missing scenarios (missing mechanisms, missing proportions and ratios of missing proportions of each group). (2) Machine learning and traditional methods (eight methods in total) were applied to impute missing values. (3) The performances of imputation techniques were evaluated and compared by estimating the sensitivity, AUC and Kappa values of prediction models. (4) Statistical tests were used to evaluate whether the observed performance differences were statistically significant. Results The performances of missing data processing methods were different to a certain extent in different missing scenarios. On the whole, machine learning had better imputation performance than traditional methods, especially in scenarios with high missing proportions. Compared with single machine learning algorithms, the performance of EL was more prominent, followed by neural networks. Meanwhile, EL was most suitable for missing imputation under MAR (the ratio of missing proportion 2:1) mechanism, and its average sensitivity, AUC and Kappa values reached 0.908, 0.924 and 0.596 respectively. Conclusions In clinical decision making, the characteristics of missing data should be actively explored before formulating missing data processing strategies. The outstanding imputation performance of machine learning methods, especially EL, shed light on the development of missing data processing technology, and provided methodological support for clinical decision making in presence of incomplete data.

scholarly journals Usefulness of Cochrane Reviews in Clinical Guideline Development—A Survey of 585 Recommendations

2022 ◽  
Vol 19 (2) ◽  
pp. 685
Author(s):  
Christoffer Bruun Korfitsen ◽  
Marie-Louise Kirkegaard Mikkelsen ◽  
Anja Ussing ◽  
Karen Christina Walker ◽  
Jeanett Friis Rohde ◽  
...  
Keyword(s):  
Decision Making ◽  
Clinical Practice ◽  
Clinical Practice Guidelines ◽  
Practice Guidelines ◽  
Health Authority ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Evidence Based ◽  
Cochrane Reviews ◽  
Danish Health

The Danish Health Authority develops clinical practice guidelines to support clinical decision-making based on the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system and prioritizes using Cochrane reviews. The objective of this study was to explore the usefulness of Cochrane reviews as a source of evidence in the development of clinical recommendations. Evidence-based recommendations in guidelines published by the Danish Health Authority between 2014 and 2021 were reviewed. For each recommendation, it was noted if and how Cochrane reviews were utilized. In total, 374 evidence-based recommendations and 211 expert consensus recommendations were published between 2014 and 2021. Of the 374 evidence-based recommendations, 106 included evidence from Cochrane reviews. In 28 recommendations, all critical and important outcomes included evidence from Cochrane reviews. In 36 recommendations, a minimum of all critical outcomes included evidence from Cochrane reviews, but not all important outcomes. In 33 recommendations, some but not all critical outcomes included evidence from Cochrane reviews. Finally, in nine recommendations, some of the important outcomes included evidence from Cochrane reviews. In almost one-third of the evidence-based recommendations, Cochrane reviews were used to inform clinical recommendations. This evaluation should inform future evaluations of Cochrane review uptake in clinical practice guidelines concerning outcomes important for clinical decision-making.

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