Big data in healthcare: What is it used for?

Hermon, R.; Williams, P. John

doi:10.4225/75/57982b9431b48

Cited by 7 publications

(2 citation statements)

References 30 publications

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“…investigated improvement of algorithm performance with an increasing amount of data collected during clinical use of the algorithms. Overall, 45 studies (42%) used data collected in a multicenter approach, while 23 studies (22%) used public databases such as SEER [50] , ACS NSQIP [51] , and SRTR [52] . External validation was performed in 13 studies (12%), with a median of 164 patients (IQR = 104-387) included in the external validation.…”

Section: Study Characteristicsmentioning

confidence: 99%

Artificial intelligence for decision support in surgical oncology - a systematic review

Wagner¹,

Schulze²,

Haselbeck-Köbler³

et al. 2022

Art Int Surg

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Aim: We systematically review current clinical applications of artificial intelligence (AI) that use machine learning (ML) methods for decision support in surgical oncology with an emphasis on clinical translation. Methods: MEDLINE, Web of Science, and CENTRAL were searched on 19 January 2021 for a combination of AI and ML-related terms, decision support, and surgical procedures for abdominal malignancies. Data extraction included study characteristics, description of algorithms and their respective purpose, and description of key steps for scientific validation and clinical translation. Results: Out of 8302 articles, 107 studies were included for full-text analysis. Most of the studies were conducted in a retrospective setting (n = 105, 98%), with 45 studies (42%) using data from multiple centers. The most common tumor entities were colorectal cancer (n = 35, 33%), liver cancer (n = 21, 20%), and gastric cancer (n = 17, 16%). The most common prediction task was survival (n = 36, 34%), with artificial neural networks being the most common class of ML algorithms (n = 52, 49%). Key reporting and validation steps included, among others, a complete listing of patient features (n = 95, 89%), training of multiple algorithms (n = 73, 68%), external validation (n = 13, 12%), prospective validation (n = 2, 2%), robustness in terms of cross-validation or resampling (n = 89, 83%), treatment recommendations by ML algorithms (n = 9, 8%), and development of an interface (n = 12, 11%). Conclusion: ML for decision support in surgical oncology is receiving increasing attention with promising results, but robust and prospective clinical validation is mostly lacking. Furthermore, the integration of ML into AI applications is necessary to foster clinical translation.

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Section: Study Characteristicsmentioning

confidence: 99%

Artificial intelligence for decision support in surgical oncology - a systematic review

Wagner¹,

Schulze²,

Haselbeck-Köbler³

et al. 2022

Art Int Surg

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show abstract

“…Value consists of the worth of information to various stakeholders or decision makers [20]. Big data includes clinical data sourced from Computerized Physician Order Entry (CPOE) and Clinical Decision Support (CDS) systems, as well as patient information stored in electronic patient records (EPRs), and machine-generated/sensor data, including vital sign monitoring [12].…”

mentioning

confidence: 99%

Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives

Di Sarno,

Caroselli,

Tonin

et al. 2024

Preprint

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The dawn of Artificial Intelligence (AI) in healthcare stands as a milestone in medical innovation. A plethora of different medical fields are heavily involved and pediatric emergency medicine is no exception. These new tools do not merely provide more advanced and efficient systems for patients’ diagnosis, management and treatment. They rather concern a strict shift from traditional methods based upon broad categories towards a more personalized healthcare. AI offers many promises in pediatric emergency medicine with a wide range of applications involving clinical decision making, patients’ flows management and prioritization. Main barriers to a widespread diffusion involve technological challenges but also ethical issues and the paucity of extensive datasets in pediatric contexts. We conducted a narrative review structured in two parts. The first part explores the theoretical principles of AI, providing all the necessary background to feel confident with these new state-of-the-art tools. The second part presents an informative analysis of AI models in pediatric emergencies, pointing out the actual applications and challenges until future feasible research perspectives.

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Performance overview of an artificial intelligence in biomedics: a systematic approach

Patil

et al. 2018

Int. j. inf. tecnol.

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Big data in healthcare: What is it used for?

Cited by 7 publications

References 30 publications

Artificial intelligence for decision support in surgical oncology - a systematic review

Artificial intelligence for decision support in surgical oncology - a systematic review

Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives

Performance overview of an artificial intelligence in biomedics: a systematic approach

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