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- Hara, Konan, et al.
(författare)
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Claims-based algorithms for common chronic conditions were efficiently constructed using machine learning methods
- 2021
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Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 16:9, s. 1-19
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Tidskriftsartikel (refereegranskat)abstract
- Identification of medical conditions using claims data is generally conducted with algorithms based on subject-matter knowledge. However, these claims-based algorithms (CBAs) are highly dependent on the knowledge level and not necessarily optimized for target conditions. We investigated whether machine learning methods can supplement researchers' knowledge of target conditions in building CBAs. Retrospective cohort study using a claims database combined with annual health check-up results of employees' health insurance programs for fiscal year 2016-17 in Japan (study population for hypertension, N = 631,289; diabetes, N = 152,368; dyslipidemia, N = 614,434). We constructed CBAs with logistic regression, k-nearest neighbor, support vector machine, penalized logistic regression, tree-based model, and neural network for identifying patients with three common chronic conditions: hypertension, diabetes, and dyslipidemia. We then compared their association measures using a completely hold-out test set (25% of the study population). Among the test cohorts of 157,822, 38,092, and 153,608 enrollees for hypertension, diabetes, and dyslipidemia, 25.4%, 8.4%, and 38.7% of them had a diagnosis of the corresponding condition. The areas under the receiver operating characteristic curve (AUCs) of the logistic regression with/without subject-matter knowledge about the target condition were .923/.921 for hypertension, .957/.938 for diabetes, and .739/.747 for dyslipidemia. The logistic lasso, logistic elastic-net, and tree-based methods yielded AUCs comparable to those of the logistic regression with subject-matter knowledge: .923-.931 for hypertension; .958-.966 for diabetes; .747-.773 for dyslipidemia. We found that machine learning methods can attain AUCs comparable to the conventional knowledge-based method in building CBAs.
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| 2. |
- Ishiguro, Masateru, et al.
(författare)
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Polarimetric properties of the near-Sun asteroid (155140) 2005 UD in comparison with other asteroids and meteoritic samples
- 2022
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Ingår i: Monthly notices of the Royal Astronomical Society. - : Oxford University Press. - 0035-8711 .- 1365-2966. ; 509:3, s. 4128-4142
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Tidskriftsartikel (refereegranskat)abstract
- The investigation of asteroids near the Sun is important for understanding the final evolutionary stage of primitive Solar system objects. A near-Sun asteroid (NSA), (155140) 2005 UD, has orbital elements similar to those of (3200) Phaethon (the target asteroid for the JAXA’s DESTINY+ mission). We conducted photometric and polarimetric observations of 2005 UD and found that this asteroid exhibits a polarization phase curve similar to that of Phaethon over a wide range of observed solar phase angles (α = 20–105°) but different from those of (101955) Bennu and (162173) Ryugu (asteroids composed of hydrated carbonaceous materials). At a low phase angle (α ≲ 30°), the polarimetric properties of these NSAs (2005 UD and Phaethon) are consistent with anhydrous carbonaceous chondrites, while the properties of Bennu are consistent with hydrous carbonaceous chondrites. We derived the geometric albedo, pV ∼ 0.1 (in the range of 0.088–0.109); mean V-band absolute magnitude, HV = 17.54 ± 0.02; synodic rotational period, Trot=5.2388±0.0022h (the two-peaked solution is assumed); and effective mean diameter, Deff=1.32±0.06km. At large phase angles (α ≳ 80°), the polarization phase curve are likely explained by the dominance of large grains and the paucity of small micron-sized grains. We conclude that the polarimetric similarity of these NSAs can be attributed to the intense solar heating of carbonaceous materials around their perihelia, where large anhydrous particles with small porosity could be produced by sintering.
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| 4. |
- Lu, Haibo, et al.
(författare)
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Comparing machine learning-derived global estimates of soil respiration and its components with those from terrestrial ecosystem models
- 2021
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Ingår i: Environmental Research Letters. - : IOP Publishing. - 1748-9318 .- 1748-9326. ; 16:5
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Tidskriftsartikel (refereegranskat)abstract
- The CO2 efflux from soil (soil respiration (SR)) is one of the largest fluxes in the global carbon (C) cycle and its response to climate change could strongly influence future atmospheric CO2 concentrations. Still, a large divergence of global SR estimates and its autotrophic (AR) and heterotrophic (HR) components exists among process based terrestrial ecosystem models. Therefore, alternatively derived global benchmark values are warranted for constraining the various ecosystem model output. In this study, we developed models based on the global soil respiration database (version 5.0), using the random forest (RF) method to generate the global benchmark distribution of total SR and its components. Benchmark values were then compared with the output of ten different global terrestrial ecosystem models. Our observationally derived global mean annual benchmark rates were 85.5 ± 40.4 (SD) Pg C yr-1 for SR, 50.3 ± 25.0 (SD) Pg C yr-1 for HR and 35.2 Pg C yr-1 for AR during 1982-2012, respectively. Evaluating against the observations, the RF models showed better performance in both of SR and HR simulations than all investigated terrestrial ecosystem models. Large divergences in simulating SR and its components were observed among the terrestrial ecosystem models. The estimated global SR and HR by the ecosystem models ranged from 61.4 to 91.7 Pg C yr-1 and 39.8 to 61.7 Pg C yr-1, respectively. The most discrepancy lays in the estimation of AR, the difference (12.0-42.3 Pg C yr-1) of estimates among the ecosystem models was up to 3.5 times. The contribution of AR to SR highly varied among the ecosystem models ranging from 18% to 48%, which differed with the estimate by RF (41%). This study generated global SR and its components (HR and AR) fluxes, which are useful benchmarks to constrain the performance of terrestrial ecosystem models.
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