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2. The Consequences of the Framing of Machine Learning Risk Prediction Models: Evaluation of Sepsis in General Wards

Author

Lauritsen, Simon Meyer, Thiesson, Bo, Jørgensen, Marianne Johansson, Riis, Anders Hammerich, Espelund, Ulrick Skipper, Weile, Jesper Bo, and Lange, Jeppe

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning
Abstract

Objectives: To evaluate the consequences of the framing of machine learning risk prediction models. We evaluate how framing affects model performance and model learning in four different approaches previously applied in published artificial-intelligence (AI) models. Setting and participants: We analysed structured secondary healthcare data from 221,283 citizens from four Danish municipalities who were 18 years of age or older. Results: The four models had similar population level performance (a mean area under the receiver operating characteristic curve of 0.73 to 0.82), in contrast to the mean average precision, which varied greatly from 0.007 to 0.385. Correspondingly, the percentage of missing values also varied between framing approaches. The on-clinical-demand framing, which involved samples for each time the clinicians made an early warning score assessment, showed the lowest percentage of missing values among the vital sign parameters, and this model was also able to learn more temporal dependencies than the others. The Shapley additive explanations demonstrated opposing interpretations of SpO2 in the prediction of sepsis as a consequence of differentially framed models. Conclusions: The profound consequences of framing mandate attention from clinicians and AI developers, as the understanding and reporting of framing are pivotal to the successful development and clinical implementation of future AI technology. Model framing must reflect the expected clinical environment. The importance of proper problem framing is by no means exclusive to sepsis prediction and applies to most clinical risk prediction models.

Published
2021

11. Using Explainable Artificial Intelligence to Predict Potentially Preventable Hospitalizations

Author

Riis, Anders Hammerich, Kristensen, Pia Kjær, Lauritsen, Simon Meyer, Thiesson, Bo, and Jørgensen, Marianne Johansson

Subjects
potentially preventable hospitalizations, prediction model, machine learning, explainable artificial intelligence, Public Health, Environmental and Occupational Health
Abstract

Background: The increasing aging population and limited health care resources have placed new demands on the healthcare sector. Reducing the number of hospitalizations has become a political priority in many countries, and special focus has been directed at potentially preventable hospitalizations. Objectives: We aimed to develop an artificial intelligence (AI) prediction model for potentially preventable hospitalizations in the coming year, and to apply explainable AI to identify predictors of hospitalization and their interaction. Methods: We used the Danish CROSS-TRACKS cohort and included citizens in 2016-2017. We predicted potentially preventable hospitalizations within the following year using the citizens' sociodemographic characteristics, clinical characteristics, and health care utilization as predictors. Extreme gradient boosting was used to predict potentially preventable hospitalizations with Shapley additive explanations values serving to explain the impact of each predictor. We reported the area under the receiver operating characteristic curve, the area under the precision-recall curve, and 95% confidence intervals (CI) based on five-fold cross-validation. Results: The best performing prediction model showed an area under the receiver operating characteristic curve of 0.789 (CI: 0.782-0.795) and an area under the precision-recall curve of 0.232 (CI: 0.219-0.246). The predictors with the highest impact on the prediction model were age, prescription drugs for obstructive airway diseases, antibiotics, and use of municipality services. We found an interaction between age and use of municipality services, suggesting that citizens aged 75+ years receiving municipality services had a lower risk of potentially preventable hospitalization. Conclusion: AI is suitable for predicting potentially preventable hospitalizations. The municipality-based health services seem to have a preventive effect on potentially preventable hospitalizations.

Published
2023
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31. Healthcare Use in the Five Years Before a First Infertility Diagnosis: A Danish Register-Based Case–Control Study in the CROSS-TRACKS Cohort

Author

Ebdrup,Ninna Hinchely, Riis,Anders Hammerich, Ramlau-Hansen,Cecilia Høst, Bay,Bjørn, Lyngsø,Julie, Rytter,Dorte, Jørgensen,Marianne Johansson, Knudsen,Ulla Breth, Ebdrup,Ninna Hinchely, Riis,Anders Hammerich, Ramlau-Hansen,Cecilia Høst, Bay,Bjørn, Lyngsø,Julie, Rytter,Dorte, Jørgensen,Marianne Johansson, and Knudsen,Ulla Breth

Abstract

Ninna Hinchely Ebdrup,1– 3 Anders Hammerich Riis,4,5 Cecilia Høst Ramlau-Hansen,2 Bjørn Bay,1,6 Julie Lyngsø,7 Dorte Rytter,2 Marianne Johansson Jørgensen,4 Ulla Breth Knudsen1,3 1Department of Obstetrics and Gynecology, Fertility Clinic, Horsens Regional Hospital, Horsens, Denmark; 2Department of Public Health, Aarhus University, Aarhus, Denmark; 3Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; 4Department of Research, Regional Hospital Horsens, Horsens, Denmark; 5Enversion A/S, Aarhus, Denmark; 6Maigaard Fertility Clinic, Aarhus, Denmark; 7Department of Obstetrics and Gynecology, Aarhus University Hospital, Aarhus, DenmarkCorrespondence: Ninna Hinchely Ebdrup, Department of Obstetrics and Gynecology, Fertility Clinic, Horsens Regional Hospital, Sundvej 30, Horsens, 8700, Denmark, Tel +45 28 47 21 11, Email ninebd@clin.au.dkPurpose: Infertility may affect somatic and mental health later in life. Nevertheless, health status before diagnosed infertility is sparsely studied in women. We aimed to describe healthcare use in primary and secondary care before a first infertility diagnosis and compare use between cases and controls.Materials and Methods: The case–control study was based on register data and used incidence density sampling. From the CROSS-TRACKS Cohort, we included women residing in the Horsens area in Denmark in 2012– 2018 (n = 54,175). Eligible women were aged 18– 40 years, nulliparous, and living in heterosexual relationships. Cases were women with a first infertility diagnosis in the Danish National Patient Registry (index date). Five controls were matched on age, birth year, and calendar time. Through linkage to Danish national health registries, we identified general practitioner (GP) attendance, paraclinical examinations, hospital contacts, diagnoses, and redeemed prescriptions. Healthcare use from one year to five years before index date was compared with conditional logistic regression.Results: We identified 711 cas

Published
2022

38. Dietary folate intake and fecundability in two preconception cohorts

Author

Cueto, Heidi T, primary, Jacobsen, Bjarke H, additional, Laursen, Anne Sofie Dam, additional, Riis, Anders H, additional, Hatch, Elizabeth E, additional, Wise, Lauren A, additional, Trolle, Ellen, additional, Sørensen, Henrik Toft, additional, Rothman, Kenneth J, additional, Wesselink, Amelia K, additional, Willis, Sydney, additional, Johannesen, Benjamin R, additional, and Mikkelsen, Ellen M, additional

Published
2022
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48. Risk of asbestosis, mesothelioma, other lung disease or death among motor vehicle mechanics:A 45-year Danish cohort study

Author

Thomsen, Reimar Wernich, Riis, Anders Hammerich, Flachs, Esben Meulengracht, Garabrant, David H., Bonde, Jens Peter Ellekilde, Toft Sørensen, Henrik, Thomsen, Reimar Wernich, Riis, Anders Hammerich, Flachs, Esben Meulengracht, Garabrant, David H., Bonde, Jens Peter Ellekilde, and Toft Sørensen, Henrik

Abstract

The risk of asbestosis, malignant mesothelioma and lung cancer among motor vehicle mechanics is of concern because of potential exposure to chrysotile asbestos during brake, clutch and gasket repair and maintenance. Asbestos has also been used in insulation and exhaust systems. We examined the long-term risk of incident mesothelioma, lung cancer, asbestosis and other lung diseases and mortality due to mesothelioma, lung cancer, asbestosis and other lung diseases in a nationwide cohort of all men registered as motor vehicle mechanics since 1970 in Denmark. This was compared with the corresponding risk in a cohort of male workers matched 10:1 by age and calendar year, with similar socioeconomic status (instrument makers, dairymen, upholsterers, glaziers, butchers, bakers, drivers, farmers and workers in the food industry, trade or public services). Our study included 138 559 motor vehicle mechanics (median age 24 years; median follow-up 20 years (maximum 45 years)) and 1 385 590 comparison workers (median age 25 years; median follow-up 19 years (maximum 45 years)). Compared with other workers, vehicle mechanics had a lower risk of morbidity due to mesothelioma/pleural cancer (n=47 cases) (age-adjusted and calendar-year-adjusted HR=0.74 (95% CI 0.55 to 0.99)), a slightly increased risk of lung cancer (HR=1.09 (95% CI 1.03 to 1.14)), increased risk of asbestosis (HR=1.50 (95% CI 1.10 to 2.03)) and a chronic obstructive pulmonary disease risk close to unity (HR=1.02 (95% CI 0.99 to 1.05)). Corresponding HRs for mortality were 0.86 (95% CI 0.64 to 1.15) for mesothelioma/pleural cancer, 1.06 (95% CI 1.01 to 1.12) for lung cancer, 1.79 (95% CI 1.10 to 2.92) for asbestosis, 1.06 (95% CI 0.86 to 1.30) for other lung diseases caused by external agents and 1.00 (95% CI 0.98 to 1.01) for death due to all causes. We found that the risk of asbestosis was increased among vehicle mechanics. The risk of malignant mesothelioma/pleural cancers was not increased among vehicle mechani

Published
2021

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