A classification framework for exploiting sparse multi-variate temporal features with application to adverse drug event detection in medical records

Bagattini, Francesco; Karlsson, Isak; Rebane, Jonathan; Papapetrou, Panagiotis

doi:10.1186/s12911-018-0717-4

BMC Medical Informatics and Decision Making

Table 5 AUC obtained by RF on 15 ADE datasets: 4 methods are compared, respectively sl, plain, mc and lr

From: A classification framework for exploiting sparse multi-variate temporal features with application to adverse drug event detection in medical records

		0.2	0.3	0.5	0.7	0.9	0.95	1.0
D61.1	plain	78.151	80.842	80.817	80.909	84.107	84.801	82.484
	mc	76.876	78.132	80.163	78.854	81.259	82.946	81.720
	lr	77.978	80.720	79.961	79.675	83.624	83.610	81.510
	sl	73.846	78.653	78.928	80.756	83.016	82.412	82.817
E27.3	plain	51.916	58.345	56.302	59.870	58.882	62.518	66.854
	mc	48.953	50.052	46.695	50.126	61.773	59.444	58.854
	lr	58.445	58.637	56.816	61.179	59.170	64.162	67.266
	sl	60.763	62.054	59.670	58.718	58.035	64.686	66.500
G62.0	plain	64.443	71.325	75.452	77.247	78.839	79.074	80.279
	mc	66.228	72.700	71.376	76.974	74.769	74.146	74.713
	lr	64.713	70.756	74.787	77.169	78.992	79.357	79.418
	sl	61.473	69.429	76.335	78.379	81.980	81.509	82.222
I95.2	plain	56.157	57.338	54.871	52.257	54.046	59.855	56.369
	mc	57.486	57.390	54.750	49.333	53.480	51.742	57.391
	lr	58.052	54.797	58.165	53.184	56.145	57.145	56.362
	sl	47.671	49.289	51.080	51.509	53.065	54.267	53.943
L27.0	plain	60.374	68.063	66.689	67.815	65.273	67.416	65.322
	mc	56.065	62.683	66.766	65.527	67.431	66.443	64.500
	lr	56.585	66.422	66.256	67.020	66.277	68.424	66.110
	sl	55.400	62.981	61.919	64.648	64.597	65.136	68.271
L27.1	plain	58.130	61.600	63.471	64.835	63.012	62.454	61.798
	mc	56.093	55.029	67.179	61.970	64.556	61.222	62.455
	lr	58.636	64.100	65.983	66.264	67.196	63.884	64.012
	sl	53.709	61.418	62.955	66.461	66.554	67.397	67.316
M80.4	plain	55.646	54.745	59.375	59.029	68.713	67.004	65.396
	mc	55.729	55.770	52.641	58.996	66.863	63.000	63.507
	lr	55.753	58.056	58.849	59.760	68.912	69.119	67.756
	sl	47.933	57.149	52.678	58.414	67.945	65.199	64.845
O35.5	plain	51.650	52.557	64.963	73.161	77.383	78.590	80.298
	mc	52.741	52.962	55.361	68.843	70.114	72.079	70.104
	lr	52.756	53.551	68.318	76.971	78.332	80.322	81.324
	sl	52.401	52.188	67.201	73.696	75.025	78.140	79.142
T78.2	plain	53.028	51.573	56.977	56.538	57.153	58.709	59.229
	mc	55.811	54.349	53.146	55.771	53.260	54.417	53.092
	lr	52.924	52.369	55.286	59.569	63.066	57.775	59.254
	sl	53.113	50.471	50.492	57.561	56.850	58.646	59.421
T78.3	plain	51.177	54.073	52.614	54.575	58.973	59.449	63.647
	mc	48.308	50.692	51.378	53.746	56.715	55.334	58.111
	lr	51.762	53.238	55.155	56.952	59.213	61.924	66.209
	sl	50.514	51.851	54.273	53.531	56.337	59.759	64.728
T78.4	plain	51.937	54.675	58.151	56.976	57.124	56.506	59.162
	mc	52.176	55.459	55.644	54.720	53.475	55.145	56.218
	lr	54.434	58.122	54.655	57.019	56.627	58.496	58.549
	sl	49.903	49.876	47.081	50.235	54.326	56.986	58.444
T80.1	plain	78.962	77.270	84.589	83.097	86.832	85.994	84.189
	mc	73.207	72.912	73.492	78.945	74.963	79.334	74.592
	lr	78.930	80.478	83.233	83.739	84.677	84.879	85.803
	sl	75.125	76.542	79.956	81.759	81.413	83.918	82.726
T80.8	plain	80.744	81.769	83.486	83.704	85.648	86.136	86.751
	mc	71.968	71.717	76.488	77.570	78.308	78.182	80.725
	lr	81.119	81.211	85.431	85.210	86.319	86.715	86.627
	sl	80.537	80.973	84.689	85.569	86.332	86.875	87.351
T88.6	plain	61.998	60.731	62.299	62.830	65.113	63.499	62.249
	mc	58.342	58.993	63.476	57.263	59.652	59.568	61.812
	lr	60.283	60.159	62.019	63.256	60.864	61.744	64.120
	sl	57.623	57.248	57.450	57.417	58.190	57.672	60.403
T88.7	plain	58.971	60.742	62.399	62.239	62.552	63.803	65.352
	mc	56.875	57.931	62.472	60.892	63.013	62.435	64.209
	lr	60.580	60.462	62.803	61.543	62.588	64.137	66.276
	sl	60.297	59.862	61.498	61.811	62.259	64.001	65.139

For each ADE, several sparsity threshold are considered, from 0.2 up to 1.0. The AUC reported for each combination is averaged by cross-validation on 10 different dataset splits. The best result is bold, and the highest AUC for each threshold underlined

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