Machine learning integrated patient flow simulation: why and how?

Figures & data

Figure 1. The conceptual model of machine learning integrated patient flow simulation.

Table 1. Transition matrices.

Departments	ADM	S1	S2	ICU1	ICU2	CD1	CD2	Others	Discharge
ADM		0.03	0.0	0.23	0.19	0.21	0.08	0.26	0.0
S1	0.01		0.0	0.12	0.23	0.1	0.04	0.49	0.01
S2	0.0	0.1		0.12	0.21	0.11	0.02	0.52	0.01
ICU1	0.03	0.09	0.01		0.0	0.23	0.01	0.63	0.0
ICU2	0.02	0.07	0.01	0.01		0.17	0.01	0.7	0.01
CD1	0.03	0.01	0.0	0.02	0.02		0.0	0.86	0.06
CD2	0.02	0.04	0.0	0.03	0.03	0.02		0.8	0.05
Others	0.03	0.03	0.01	0.07	0.1	0.53	0.07		0.15

Figure 2. Monthly patient inflow trend and autocorrelation results.

Table 2. BasIc statistic of monthly patient inflow.

Count	76
Mean	140
Std	64
Min	33
25%	94
50%	132
75%	182
Max	282

Table 3. Basic statistics of los and predicting features (except gender).

Features	Count	Mean	Std	Min	25%	50%	75%	Max
Age	2412	56	11	24	47	49	64	92
BMI	2412	30	7	18	30	30	30	283
Urea	2412	7	2	2	7	7	7	33
Triglycerides	2412	2	1	0	2	2	2	16
Creatinine	2412	103	63	45	85	103	103	946
Uric_Acid	2412	410	35	169	410	410	410	986
Iron	2412	7	2	3	6	7	7	43
Albumen	2412	39	3	22	39	39	39	56
Potassium	2412	4	0	2	4	4	5	7
Calcium_Ionized	2412	1	0	1	1	1	1	1
AST	2412	28	29	6	19	28	28	939
ALT	2412	30	26	2	19	30	30	838
Sodium	2412	140	2	124	140	140	140	151
Protein	2412	69	5	38	69	69	69	99
Coef_Atherogenicity	2412	4	1	1	4	4	4	11
C-reactive_Protein(CRP)	2412	22	21	0	22	22	22	291
Glomerular_Filtration_Rate	2412	68	7	17	68	68	68	131
WBC-Leukocytes	2412	8	2	2	6	8	9	68
HGB-Hemoglobin	2412	136	17	50	128	136	146	190
HCT-Hematocrit	2412	40	5	17	38	40	43	56
Troponin_I	2412	1	2	0	0	1	1	38
Lactat	2412	2	1	1	2	2	2	14
AP-Mean	2412	100	11	35	95	100	105	145
Bilirubin_Mean	2412	13	3	2	12	13	13	60
Acid_base_Mean	2412	3	1	−5	3	3	3	12
Prothrombin_Mean	2412	39	12	11	39	39	48	56
Cholesterol_Mean	2412	2	0	1	2	2	2	5
LoS	2412	11	9	0	4	8	14	77

Figure 3. Comparison of inflow prediction results from traditional and the new simulation model.

Table 4. Comparison of inflow prediction results from traditional and the new simulation model.

Comparison Analysis of Patient Inflow Prediction Models
Date	EX1	EX2	EX3	Poisson (Avg)	ML	Data	Error-Poisson	Error-ML
January 31 2016	271	239	244	251	227	239	12	−12
February 28 2016	226	225	220	224	261	263	−39	−2
March 31 2016	259	228	227	238	249	217	21	32
April 30 2016	244	238	241	241	236	239	2	−3
Mean Absolute Error							18.5	12.25

Figure 4. Variability of Poisson distribution experiment results.

Figure 5. Comparison of LoS prediction results from traditional and the new simulation model.

Table 5. Comparison of LoS prediction results from traditional and the new simulation model.

Classes	Validation for Distribution of LoS Classes in %
	Data	Discrete Probability (DSD)	ML	Discrete Probability-Error	ML-Error
1	0.77	0.76	0.84	−0.001	0.07
2	0.18	0.19	0.16	0.004	−0.03
3	0.04	0.03	0.01	−0.002	−0.03
4	0.01	0.01	0.00	−0.001	−0.01
Mean Absolute Error (MSE)				0.002	0.035