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pytorch-survival
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flchain_pycox

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train · 6.52k rows

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age float32 50 101	sex float32 0 1	sample.yr int64 2k 2k	kappa float32 0.01 20.5	lambda float32 0.04 26.6	flc.grp int64 1 10	creatinine float32 0.4 10.8	mgus float32 0 1	event_time float32 0 5.17k	event_indicator float32 0 1
97	0	1,997	5.7	4.86	10	1.7	0	85	1
92	0	2,000	0.87	0.683	1	0.9	0	1,281	1
94	0	1,997	4.36	3.85	10	1.4	0	69	1
92	0	1,996	2.42	2.22	9	1	0	115	1
93	0	1,996	1.32	1.69	6	1.1	0	1,039	1
90	0	1,997	2.01	1.86	9	1	0	1,355	1
90	0	1,996	0.43	0.88	1	0.8	0	2,851	1
90	0	1,999	2.47	2.7	10	1.2	0	372	1
93	0	1,996	1.91	2.18	9	1.2	0	3,309	1
91	0	1,996	0.791	2.22	6	0.8	0	1,326	1
96	0	1,996	2.38	2.17	9	1.3	0	2,776	1
90	0	1,996	3.96	3.95	10	1.1	0	1,046	1
90	0	1,996	0.83	1.4	3	0.9	0	667	1
97	0	1,996	4.53	4.08	10	2	0	51	1
90	0	1,996	2.71	1.81	9	1.1	0	1,286	1
93	0	1,997	1.6	1.75	7	0.9	0	1,914	1
95	0	1,997	1.88	2.43	9	1.9	0	874	1
94	0	1,996	2.6	6.7	10	1.8	0	91	1
94	0	1,996	1.8	2.29	9	1	0	1,903	1
92	0	1,996	0.601	1.44	2	1	0	2,000	1
94	0	1,997	2.31	2.08	9	0.9	0	71	1
90	0	1,996	2.19	2.45	9	1.1	0	2,845	0
90	0	1,997	0.802	1.05	2	1.2	0	2,586	1
94	0	1,996	1.31	1.85	7	1	0	24	1
101	0	1,996	1.54	1.73	7	1	0	4	1
90	0	1,996	1.48	1.53	6	1.2	0	2,718	1
91	0	1,996	1.13	1.32	4	1.5	0	85	1
93	0	1,996	1.57	2.25	8	0.8	0	2,498	0
95	0	1,996	2.92	2.91	10	2	0	0	1
91	0	1,996	0.43	0.94	1	0.9	0	2,905	0
93	0	1,996	4.18	4.28	10	1.1	0	371	1
96	0	1,997	3.35	2.34	10	2.4	0	23	1
90	0	1,998	1.08	1.69	5	0.8	0	1,521	1
96	0	1,997	2.1	2.29	9	1.3	0	691	1
92	0	1,997	1.69	1.91	8	1.1	0	1,584	1
91	0	1,996	3.36	3.16	10	1.2	0	283	1
95	0	1,998	2.41	3.35	10	0.9	0	590	1
90	0	1,997	2.26	2.13	9	0.9	0	702	1
92	0	1,996	2.46	2.03	9	1.4	0	227	1
90	0	1,996	2.12	1.5	8	1.3	0	89	1
93	0	1,997	2.76	2.24	10	1.5	0	2,218	1
90	0	1,996	2.48	1.9	9	1.3	0	997	1
94	0	1,997	4.84	6.31	10	1.3	0	553	1
94	0	1,996	3.94	2.7	10	1.5	0	1,754	1
90	0	1,997	4.53	6.88	10	1	0	225	1
97	0	1,996	3.07	2.85	10	1	0	322	1
91	0	1,997	2.74	3.41	10	0.9	0	704	1
92	0	1,996	1.34	1.49	6	0.9	0	881	1
92	0	1,996	2.24	2.72	10	1.2	0	897	1
91	0	1,996	5.15	5.08	10	2.5	0	500	1
94	0	1,998	6.97	7.97	10	2	0	4	1
100	0	1,999	1.82	3.25	10	1.6	0	0	1
90	0	1,997	1.29	2.23	8	1	0	262	1
99	0	1,996	1.3	1.92	7	1	0	1,960	1
96	0	1,995	1.39	2.13	8	1.3	0	937	1
94	0	1,996	1.01	1	2	0.9	0	1,079	1
94	0	1,996	2.17	2.4	9	1	0	48	1
92	0	1,998	1.56	1.51	7	1.1	0	1,298	1
94	0	1,996	3.17	4.14	10	2	0	77	1
90	0	1,996	0.829	0.614	1	1.1	0	2,047	1
93	0	1,996	2.72	3.11	10	1.5	0	455	1
96	0	1,996	1.28	1.7	6	1.1	0	567	1
92	0	1,996	3.12	2.39	10	1.3	0	687	1
95	0	1,996	1.46	1.38	6	0.8	0	630	1
92	0	1,997	1.24	1.03	3	0.7	0	387	1
91	0	1,996	2.04	2.74	10	1.3	0	663	1
93	0	1,996	1.85	1.51	7	1.1	0	1,817	1
93	0	1,996	2.36	2.5	10	1.1	0	899	1
90	0	1,998	3.09	2.89	10	1.4	0	3,230	1
90	0	1,996	4.04	6.77	10	2.1	0	598	1
90	0	1,998	3.02	2.04	10	1.2	0	266	1
96	0	1,996	4.53	3.95	10	1.8	0	673	1
94	0	1,997	4.54	4.81	10	2	0	18	1
90	0	1,995	1.87	1.87	8	1	0	2,048	1
90	0	1,996	2.1	2.8	10	1.6	0	1,207	1
91	0	1,995	3.14	2.85	10	1.1	0	315	0
90	0	1,997	1.76	2.72	9	0.8	0	335	1
97	0	1,996	2.04	3.06	10	1.1	0	108	1
92	0	1,996	2.61	3.48	10	1	0	166	0
93	1	2,000	2.84	2.2	10	1.5	0	191	1
90	1	1,996	6.04	4	10	2.5	0	28	1
96	1	1,996	5.93	5.06	10	1.2	0	705	1
94	1	1,997	1.54	1.9	8	1.6	0	1,126	1
90	1	1,998	3.39	3.42	10	1.8	0	2,743	1
90	1	1,997	1.49	1.91	8	1.3	0	2,177	1
90	1	1,998	1.77	1.92	8	1.1	0	1,069	1
93	1	1,996	3.53	3.83	10	1	0	28	1
90	1	1,996	2.24	3.62	10	1.4	0	213	1
92	1	1,997	1.66	1.51	7	1.4	0	1,457	1
92	1	1,996	4.18	3.09	10	1.6	0	436	1
96	1	1,996	2.01	6.24	10	2	0	57	1
93	1	1,996	2.44	2.12	9	1.3	0	34	1
91	1	1,996	1.88	1.35	7	1	0	2,887	1
90	1	1,996	2.24	2.07	9	1.8	0	750	1
97	1	1,996	3.53	3.9	10	1.1	0	32	1
92	1	1,996	2.4	2.57	10	1	0	984	1
90	1	1,997	2.99	3.32	10	1.6	0	1,210	1
92	1	1,996	2.86	2.99	10	1.5	0	267	1
91	1	1,999	2.47	2.53	10	1.3	0	2,488	1
92	1	1,996	3.07	3.81	10	1.3	0	877	1

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