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研究生:陳瑞杰
研究生(外文):Ray-Jady Chen
論文名稱:探討台灣外傷系統之設計-資料探勘分析法於健康資料庫之應用
論文名稱(外文):Explore the Design of a Trauma System in Taiwan-The application of data mining to existing health databanks
指導教授:蔣以仁蔣以仁引用關係李友專李友專引用關係
指導教授(外文):I-Jen ChiangYu-Chuan Li
學位類別:碩士
校院名稱:臺北醫學大學
系所名稱:醫學資訊研究所
學門:醫藥衛生學門
學類:醫學技術及檢驗學類
論文種類:學術論文
論文出版年:2003
畢業學年度:91
語文別:英文
論文頁數:105
中文關鍵詞:外傷系統資料探勘決策樹分類法國家健康保險外傷登錄外傷嚴重度指數嚴重外傷死亡指標院前轉送
外文關鍵詞:Trauma SystemData MiningDecision Tree ClassifierNational Health Insurance (NHI)Trauma RegistryInjury Severity Score (ISS)Major Trauma Outcome PredictorPre-hospital Transfer
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背景:台灣地區每年因事故傷害死亡之人數達上萬人,且高居第四或第五大死亡原因。因衛生署中央健康保險局之資料,每年外傷病患之醫療申報費用共達147億,平均每天花費四仟萬元。本研究之主要方向為探討台灣地區嚴重外傷病患之重要死亡影響因子,並藉由決定樹分類法(decision tree classifier)以及邏輯迴歸分析法(logistic regression analysis)來探尋院前轉送、外傷嚴重度、醫療費用與治療結果間之相互關係。期望能提出有效的策略來改善現有外傷醫療,以及建立外傷系統。
方法:民國89年7月1日至90年12月31日間,經由中央健康保險局之重大外傷病患之資料與長庚林口醫學中心之外傷登錄資料結合。共收集550位重大外傷病患,其平均外傷嚴重度指數(Injury Severity Score, ISS)為21.6 ± 0.3。男女比為3.4比1,平均年齡為41.5 ± 0.9歲。急診檢傷分類第一級為242人,第二級為245人,第三級為63人。平均住院日數為25.2 ± 1.2天,範圍為1至227天。死亡人數共87人,死亡率為15.8%。至第三級照護外傷中心之前,到院延遲時間,依1與4小時,分為立即(immediately),早期(early)與晚期(late)三種型態。依到院前各級醫療院所之治療紀錄,分為直接(direct),間接(indirect)與延遲(delayed)轉送三種型態。數值均以mean ± SEM,以t檢定來檢驗連續變數,並以卡方檢定來檢驗級距變數。並採用商用資料探勘軟體Index Miner中之C4.5分類法,以及SPSS 10.0之邏輯迴歸分析模式來分析,其有意義值定為p< 0.05。
結果:本研究使用兩種以上分析法均能有效完成危險因子之分析。其中外傷嚴重度指數達25分或以上最具意義,且與改良式外傷指數(Revised Trauma Score, RTS)同時出現於兩種分析法中。年齡55歲或以上,簡易外傷指數(Abbreviated Injury Score, AIS)於頭部、腹部與體表傷害達5分,以及間接或延遲轉診等各種因子,均能有意義地使嚴重外傷病患死亡率提高。直接轉送死亡率最低達12.3%。如果550位嚴重外傷病患均能直接轉送至第三級照護之外傷中心,將可節省13%住院日數,以及15%醫療費用。
結論:本研究為國內首次結合健保申報資料與外傷登錄來分析外傷死亡影響因子。兩種分析法其功能為互補,且分析結果也與其他的研究報告吻合。結合以上兩種資料,針對病患之死活,本研究對外傷嚴重度與醫療費用,均提出有意義建議,應可提供政府作為外傷醫療政策改善的參考。並且十分清楚地得到嚴重外傷病患,應避免間接與延遲轉送;而應於受傷後最短時間內抵達三級醫療之外傷中心,以降低死亡率。因此我們建議台灣應建立完整的外傷醫療系統,以期能有效降低外傷死亡率與醫療資源浪費。
Background: In Taiwan, injury remains a major disease with ten-thousand trauma death annularly. The injured patients consumed NT$ 14.7 billion per year, or an average of NT$ 40 million per day in Taiwan. The primary interest of this study was to identify the most significant influencing factors on mortality of patients with severe injury, and through the decision tree classifier and logistic regression analysis to identify the relationships between the pre-hospital transfer, injury severity, medical expenditures; and their outcomes. Then a recommendation to identify the most efficient strategies for organizing the delivery of acute trauma care was made, and the design of trauma system can be suggested.
Methods: From July 1, 2000 to December 31, 2001, using the merged data from the BNHI and the Linkou CGMH, 550 major trauma patients with a mean ISS of 21.6 ± 0.3 were studied. The blunt forces have caused more than 95% of injuries in our major trauma patients. The male to female ratio was 3.4 to 1, and the mean age was 41.5 ± 0.9 years. The category of ED triage of those severe injured patients was III in 63, II in 245, and I in 242. The mean length of hospital stay was 25.2 ± 1.2 days, ranged from 1 to 227 days. The overall mortality rate was 15.8% (87/550). The time lag prior to arrival at a definitive trauma institute was categorized into three types, as immediate (<1hour), early (1- 4 hours), and late (> 4 hours) transfer after injury. The prior utilization of medical facility was also categorized as direct, indirect, and delayed types of transfer. Data was expressed as mean ± SEM. Student’s t-test was used to compare the continuous variables, and the Chi-square test was used to compare the categorical variables. The influencing factors on trauma mortality were studied by two methods: (1) a C4.5 decision tree classifier from a commercial data mining tool, Index Miner, (2) forward stepwise logistic regression model performed by using the SPSS statistical package (Release 10.0, SPSS Inc., Chicago, IL). Statistical significance was considered at p < 0.05.
Results: This research shows that the decision tree and logistic regression are both effective for the factor analysis. The ISS 25 or greater were found most significant, and then followed by the RTS over both analytic methods. Patients with a age of 55 or greater (OR=3.3), a head injury with an AIS score of 5 (OR=13.1), an abdominal injury with an AIS score of 5 (OR=26.2), an external injury with an AIS score of 5 (OR=66.9), an indirect transfer (OR=3.7), and a delayed transfer (OR=2.9), would significantly encounter a worsening prognosis after sustaining a severe trauma. Patients with similar injury severity received the direct, indirect, and delayed transfer and resulted in 12.3%, 19.5%, and 14.7% mortality rate, respectively. If all the major trauma patients were directly transferred to the tertiary trauma care center, 13% of hospitalization days and 15% medical expenditures could be saved.
Conclusion: This study is the first to use the data of medical claims and trauma registry to analyze the factors affecting trauma outcomes in Taiwan. These two analytic methods are complementary and reasonable models for outcome prediction for this study. Linking trauma registries with medical claims data provides much significant information about injury severity, outcome, and medical expenditures to this study. This data linkage offers the opportunity for improving our trauma care, provides data for government for further policy making.
This study distinctly shows that only the avoidance of the indirect and delayed transfer and transfers the patients to the appropriate tertiary trauma center in the shortest time can decrease the mortality after injury. We conclude that implementation of an organized system of trauma care can result in a measurable decrease in trauma mortality and effective utilization of the medical resources.
TABLE OF CONTENTS
CHAPTER I 1
INTRODUCTION 1
1.1 TRAUMA IS A MAJOR DISEASE IN TAIWAN 1
1.2 TRAUMA SYSTEM CAN IMPROVE TRAUMA OUTCOME 1
1.3 NATIONAL HEALTH INSURANCE PROGRAM IN TAIWAN 2
1.4 EXISTING MEDICAL DATA 3
1.5 TRAUMA OUTCOME PREDICTORS 3
1.6 DATA MINING 4
1.7 OBJECTIVES 5
CHAPTER II 6
LITERATURE REVIEW 6
2.1 TRAUMA EPIDEMIOLOGY IN UNITED STATES 6
2.2 DEFINITION OF PATIENTS WITH MAJOR TRAUMA 6
2.3 TRIMODAL DISTRIBUTION OF TRAUMA DEATH 6
2.4 ORIGINS OF TRAUMA CARE SYSTEM 7
2.5 TRAUMA SYSTEM DESIGNATION 7
2.5.1 TRAUMA CENTER 8
2.5.2 TRAUMA SYSTEM 8
2.6 BENEFITS OF TRAUMA SYSTEM 9
2.7 TRAUMA SCORING SYSTEM 10
2.8 TIME FACTOR IN TRAUMA QUALITY IMPROVEMENT 11
2.9 ECONOMIC IMPACTS OF TRAUMA 11
2.10 DATA MINING AND DECISION TREE CLASSIFIER 12
CHAPTER III 14
MATERIALS AND METHODS 14
3.1 DATA SOURCES 14
3.1.1 NATIONAL HEALTH INSURANCE RESEARCH DATABASE: 14
3.1.2 INSTITUTIONAL TRAUMA REGISTRY 14
3.2 DATA LINKAGE 15
3.2.1 STEP 1: LOAD, EXTRACT AND EXPORT OF NHI DATA 15
3.2.2 STEP 2: INSTITUTIONAL TRAUMA REGISTRY 17
3.3 TRAUMA SCORING 22
3.3.1 REVISED TRAUMA SCORE 22
3.3.2 REVISED TRAUMA SCORE CALCULATION 22
3.3.3 ABBREVIATED INJURY SCALE AND INJURY SEVERITY SCORE 23
3. 3.4 ABBREVIATED INJURY SCALE AND INJURY SEVERITY SCORE CALCULATION 24
3.3.5 TRAUMA INJURY SEVERITY SCORE (TRISS) 25
3.3.6 TRAUMA INJURY SEVERITY SCORE (TRISS) METHOD 25
3.4 PRE-HOSPITAL TRANSFER 27
3.4.1 CATEGORIZATION BY TIME LAG 28
3.4.2 CATEGORIZATION BY FACILITY UTILIZATION 29
3.5 DESCRIPTION OF DATA ELEMENTS 29
3.6 TECHNIQUE OF DATA MINING 31
3.7 DATA ANALYSIS 33
3.7.1 STATISTICAL ANALYSIS 33
3.7.2 DECISION TREE ANALYSIS 33
CHAPTER IV 35
RESULTS 35
4.1 SUMMARY OF SAMPLE SELECTION (FIGURE 4.1) 35
4.3 TYPES OF PRE-HOSPITAL TRANSFER AND TIME LAG AFTER INJURY (TABLE 4.3) 38
4.4 ED TRIAGE CATEGORY, PHYSIOLOGICAL PARAMETERS, AND RTS (TABLE 4.4.1 & 4.4.2) 40
4.5 AIS AND ISS (TABLE 4.5) 44
4.6 NO. OF OPERATIVE PROCEDURES, LENGTH OF STAY, MEDICAL EXPENDITURES 46
4.7 MORTALITY RATE AND RISK FACTORS BY LOGISTIC REGRESSION ANALYSIS 46
4.8 MEDICAL EXPENDITURE & LOS BETWEEN SURVIVORS AND NONSURVIVORS 49
4.9 COMPARISON OF CLINICAL DATA AMONG THREE TYPES OF PATIENT TRANSFER 51
4.10 COMPARISON OF MEDICAL EXPENDITURES AND LENGTH OF STAY AMONG THREE TYPES OF PATIENT TRANSFER 53
4.11 DECISION TREE ANALYSIS 55
CHAPTER V 60
DISCUSSION 60
5.1 DATA ADVANTAGES AND LIMITATION 60
5.2 FACTOR ANALYSIS ON TRAUMA OUTCOME 62
5.3 INJURY SEVERITY SCORE AND AIS 62
5.4 RTS 63
5.5 TIME FACTOR 64
5.5.1 TIME LAG AFTER INJURY 64
5.5.2 TYPES OF PRE-HOSPITAL TRANSFER 65
5.6 AGE 67
5.7 LENGTH OF THE ICU STAYS 68
5.8 COMPARISON OF LOS AND EXPENDITURE BETWEEN THREE TYPES OF PRE-HOSPITAL TRANSFER 68
5.9 COMPARISON BETWEEN TWO ANALYTIC METHODS 70
5.9.1 ROC CURVE 70
5.9.2 TREE PRUNING 70
5.9.3 FACTORS DISCREPANCY 71
5.9.4 LIMITATION 72
CHAPTER VI 73
CONCLUSION AND RECOMMENDATION 73
REFERENCE 75
APPENDIX 1 LOGISTIC REGRESSION ANALYSIS BY SPSS 10.0 82
LOGISTIC REGRESSION 82
APPENDIX 2: LOGISTIC REGRESSION ANALYSIS (DICHOTOMY OF RTS AND ISS) 91
LOGISTIC REGRESSION 91
APPENDIX 4. TREE VIEW OF PRUNED MODEL 98
APPENDIX 5. UNPRUNED MODEL 99
1. National Academy of Science: Injury in America, A Continuing Health Problem. Washington, DC, National Academy Press, 1985
2. National Academy of Science/National Research Council (NAS/NRC): Accidental Death and Disability: The Neglected Disease of Modern Society. Washington, DC, Governmental Printing Office, 1966.
3. 衛生署,生命統計 2000.
4. 健保醫療統計年度報表,中央健保局,民國八十八年.
5. Chen RJ, fang JF, Lin BC, et al. Factors that influence the operative mortality after blunt hepatic injuries. Eur J Surg 1995; 161: 811-817.
6. Bazzoli, GJ, Madura, KJ, Cooper, GF, MacKenzie, EJ, Maier, RV. Progress in the Development of Trauma Systems in the United States. JAMA 1995; 273:395-401.
7. Shackford SR. The evolution of modern trauma care. Surg Clin North Am 1995; 75:147-156
8. Mullin RJ, Mann CM. Population-based research assessing the effectiveness of trauma system. J Trauma 1999; 47:S59-S66.
9. Committee on Trauma of the American College of Surgeons. Resources for optimal care of the injured patient. Chicago: American College of Surgeons; 1999.
10. Cales, RH, Trunkey, DD: Preventable Trauma Deaths, a Review of Trauma Care Systems Development. JAMA 1985; 254: 1059-1063.
11. Esposito, TJ, Nania, J, Maier, RV: State Trauma System Evaluation: A Unique and Comprehensive Approach. Annals of Emergency Medicine 1991; 21:4:351-357.
12. Esposito, TJ, Lazear, SE, Maier, RV: Trauma Care Systems Development: Evolution and Current Trends. Advances in Trauma and Critical Care 1991; 6:115-131.
13. Champion HR, Frey CF. Report on the major trauma outcome study. Presented at the American College of Surgeons Committee on Trauma. Chicago; 1986.
14. Shackford, SR, Hollingwork-Fridlund P, Cooper, GF, et al: The Effect of Regionalization upon the Quality of Trauma Care as Assessed by Concurrent Audit before and After Institution of a Trauma System: A Preliminary Report. J Trauma 1986; 26:812-820.
15. West, JG, Williams, MJ, Trunkey, DD, et al: Trauma Systems Current Status Future Challenges. JAMA 1988; 259:3597-3600.
16. Shackford SR, Hollingsworth-Fridlund P, Cooper GF, et al. The effect of regionalization upon the quality of trauma care as assessed by concurrent audit before and after institution of a trauma system: a preliminary report. J Trauma 1986; 26:812-820
17. Shackford SR, Baxt WG, Hoyt DB. et al. Impact of a trauma system on outcome of severely injured patients. Arch Surg 1987; 122:523-527.
18. Sampalis J, Denis R, Frechette P, et al. Direct transport to tertiary trauma centers versus transfer lower level facilities: impact on mortality and morbidity among patients with major trauma. J Trauma 1997; 43:288—96.
19. 衛生署,健保法 1995.
20. Department of Health, Taiwan, ROC. http://www.doh.gov.tw/dohenglish/index.asp
21. Maull KI, Augenstein JS. Traumatic Informatics. 1997, 1st ED., Springer-Verlag Inc.
22. Rutledge R. The goals, development and use of trauma registries and trauma data sources in decision making in injury. Surg Clin North Am 1995; 75: 305-326.
23. Lestina DC, Miller TR, Smith GS. Creating Injury Episodes Using Medical Claims Data. J Trauma 1998; 45: 565-569.
24. Mitchell JB, Bubloz T, Paul JE, et al. Using Medicare claims for outcomes research. Med Care. 1993; 32:JS38.
25. Wennberg JE, Roos N, Sola L, et al. Use of claims data systems to evaluate health care outcomes: mortality and reoperation following prostatectomy. JAMA. 1987;257:933.
26. Quam L, Ellis LBM, Venus P, et al. Using claims data for epidemiologic research. Med Care. 1993; 31: 498-507.
27. Anderson G, Steinberg EP, Whittle J, Powe NR, Antebi A, Herbert R. Development of clinical and economic prognoses from Medicare claims data. JAMA. 1990; 263: 967-972.
28. Champion HR, Sacco WJ, Hunt TK. Trauma severity scoring to predict mortality. World J Surg 1983;7:4.
29. Baker SP, O’Neill B, Haddon W, et al. The injury severity score: A method for describing patients with multiple injuries and evaluating emergency care. J Trauma 1974; 14:187—196.
30. Champion HR, Copes WS, Sacco W, et al. Research in trauma outcomes using the MTOS database. AHCPR Grant HS 06721, 1994.
31. Champion HR, Sacco WJ, Copes WS. A revision of the trauma score. J Trauma 1989;29:623-4.
32. Morris JA Jr, MacKenzie EJ, Edelstein SL: The effect of preexisting conditions on mortality in trauma patients. JAMA 1990; 263:1942.
33. Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R, eds.: Advances in Knowledge Discovery and Data Mining. Menlo Park, CA: AAAI Press, 1996.
34. LavraˇNC “Selected techniques for data mining in medicine,” Artificial Intelligence in Medicine 1999; 16(1), pp. 3-23.
35. Lucas PJF., Abu-Hanna A. “Prognostic methods in medicine (editorial),” Artificial Intelligence in Medicine 1999;15(2), pp. 105-119.
36. Kukar M., Kononenko I., and Silvester T. “Machine learning in prognosis of the femoral neck fracture recovery,” Artificial Intelligence in Medicine 1996; 8, pp. 431-451.
37. Pilih I A., Mladeni´CD., LavraˇNC, and Prevec TS. “Using machine learning for outcome prediction of patients with severe head injury,” in Proceedings of the tenth Symposium on Computer-Based Medical Systems 1997., pp. 200-204.
38. Bajd T., Grobelnik MD, LavraˇNC, et al. “Machine learning for prediction of walking abilities in incomplete spinal cord injured patients,” in Proceedings of Computer-Aided 1997 Data Analysis in Medicine CADAM-97.
39. Viikki K, Kentala E, Juhola M, et al.. “Confounding values in decision trees constructed for six otoneu-rological diseases,” in Proceedings of the Fifth International Workshop on Intelligent Data Analysis in Medicine and Pharmacology.
40. Demsar J, Zupan B, Aoki N, Wall MJ, et al. “Feature mining and predictive model construction from severe trauma patient’s data,” International Journal of Medical Informatics 2001; 63, pp. 41-50.
41. Chen RJ, Chiang c I.J., Li Y.C., et al. “Utilizing decision tree classifier with optimal roc cut-off values in the analysis of determining factors for non-operative failure patients after major blunt hepatic trauma” in Workshop of the foundation of data mining and discovery in the 2002 IEEE International Conference on Data Mining (ICDM 2002), (Maebashi, Japan).
42. Pollock DA, McClain PW. Report from the 1988 trauma registry workshop, including recommendations for hospital-based trauma registries. J Trauma 1989; 29: 827-834.
43. American College of Surgeons: Major trauma outcome study. Chicago: American Colleg of Surgeons, 1989.
44. Trunkey DD. Trauma: a public health problem from Moore EE. Early care of injured patient. 4th ED, B.C. Decker, Inc. 1990. p. 1-11
45. Baker CC, Oppenheimer L, Stephens B, et al. Epidemiology of trauma deaths. Am J Surg 1980; 140:144.
46. Champion HR, Copes WS, Sacco WJ, Flanagan ME. Injury severity scoring from Moore EE. Early care of injured patient. 4th ED, B.C. Decker, Inc. 1990. p. 12-26.
47. Van Natta TL, Morris JA. Injury scoring and trauma outcomes. Mattox KL, Feliciano DV, Moore EE. Trauma , 4th ED, McGraw-Hill Inc. 2000, p.69-80.
48. Rutledge R. The Injury Severity Score is unable to differentiate between poor care and severe injury. J Trauma 1996; 40:944.
49. Boyd CR, Tolson MA, Copes WS. Evaluating trauma care: the TRISS method. J Trauma 1987; 27:370-378.
50. Champion HR, Copes WS, Sacco WJ, et al: The Major Trauma Outcome Study: Establishing national norms for trauma care. J Trauma 30:1356, 1990
51. Hill DA, Delaney LM, Roncal SD. A Chi-Square Automatic Interaction Detection (CHAID) Analysis of Factors Determining Trauma Outcomes. J Trauma 1997; 42:62-66
52. Booth FV. Quality improvement in trauma care. from Maull KM, Rodriguez A, Wiles III CE. Complications in trauma and critical care. 1st ED, W.B. Saunders Inc. 1996. p.552-558.
53. Branas CC, MacKenzie EL, ReVelle CS. A trauma resource allocation model for ambulances and hospitals. Health Services Research 2000; 35: 489-507.
54. Dunn LT. Secondary insults during the interhospital transfer of head-injured patients: an audit of transfers in the Mersey Regions. Injury 1997; 28: 427-431.
55. Jacobs LM. Economic effects of managed care. Surg Clin North Am 1999; 79: 1249-1257.
56. Rice DP, MacKenzie EJ, et al. Cost of injury in the United States: a report to congress: 1989. San Francisco, Institute for health and Aging, University of California, San Francisco; and Baltimore, Injury Prevention Center, John Hopkins University, 1989.
57. Champion HR, Mabee MS. An American crisis in trauma care reimbursement. Pub No. 3.90. Washington, DC, Washington Hospital Center, 1990.
58. Miller TR, Levy DT. Effect of regional trauma care systems on costs. Arch Surg 1990; 30: 188-193.
59. Taheri PA, Wahl WL, Butz DA, et al. Trauma service cost: the real story. J Trauma 1998; 227: 720: 725.
60. Murthy, SK. 1995. On Growing Better Decision Trees from Data . Ph.d. dissertation, The Johns Hopkins University, Baltimore, Maryland.
61. MurthySK, Kasif S, Salzberg S. 1994. “A system for induction of oblique decision trees,” Journal of Artificial Intelligence Research 2, pp. 1-32.
62. Quinlan JR. Induction of decision trees. Mach Learn 1986; 1(1): 81—106.
63. Quinlan JR. C4.5: Programs for Machine Learning. Los Altos, CA: Morgan Kaufmann, 1993.
64. Trunkey DD. Panel: Current status of trauma severity indices. J Trauma 1983; 23:185—188.
65. MacKenzie EJ, Shapiro S, Eastham J. Rating AIS severity using emergency department sheets versus inpatient charts. J Trauma 1985; 25:984—988.
66. Trunkey DD. Overview of trauma. Surg Clin North Am 1982; 62:3—7.
67. American Association for Automotive Medicine. The abbreviated injury scale (AIS)─1990 revision. Des Plaines, IL; 1990.
68. Senkowski CK, McKenney MG. Trauma scoring. J Am Coll. Surg 1999; 189: 491-503
69. Sacco WJ; MacKenzie EJ; Champion HR. Comparison of Alternative Methods for Assessing Injury Severity Based on Anatomic Descriptors. J Trauma; 47:441-446.
70. Chiang I.J., Lin T.Y., Index Miner: a data mining system, In Proceedings of the Annual International Computer Software and Applications Conference (COMPSAC’2001), Chicago, IL, 2001, 613-614.
71. Chiang IJ, The accuracy in Forecasts Between Conventional Classifiers and Fuzzy Classifiers: Applications to Physical Checkup Data, Bulletin of International Rough Set Society (IRSS), Vol 3, 1999.
72. Chiang I-J., Hsu JY-j, Fuzzy Classification Trees for Data Analysis, to be appeared in Fuzzy Sets and Systems 2002.
73. Chiang I.J., Hsu J.Y.J., Induction of Fuzzy Classification trees, In Proceedings of the First Pacific-Asian Conference on Knowledge Discovery and Data Mining (PAKDD’97), Singapore, 1997, 65-78.
74. Weiss SM. and Kulikowski CA.. 1991. Computer Systems That Learn , Morgan Kaufmann.
75. Shao, J. 1993. “Linear model selection by cross-validation,” Journal of the American Statistical Association 88, pp. 486-494.
76. Swets JA, Pickett RM. 1992. Evaluation of diagnostic systems: Methods from signal detection theory, Academic Press, New York.
77. Nathens AB, Jurkovich GJ, Maier RV, et al. Relationship between trauma center volume and outcomes. JAMA. 2001; 285:1164—1171.
78. Meredith JW; Evans G; Kilgo PD; et al. A Comparison of the Abilities of Nine Scoring Algorithms in Predicting Mortality. J Trauma 2002;53:621-629.
79. Healey C, Osler TM, Roghers FB, et al. Improving the Glasgow coma scale score: motor score alone is a better predictor. J Trauma 2003;54:671-680.
80. Mann NC, Mullin RJ, Hedges JR, et al. Mortality among seriously injured patients treated in remote rural trauma centers before and after implementation of a statewide trauma system. Medical care 2001; 39:643-653.
81. Sethi D, Aljunid S, Saperi SB, et al. Comparison of the effectiveness of major trauma services provided by tertiary and secondary hospitals in Malaysia. J Trauma 2002; 53:508-516.
82. National Center for Health Statistics, United States. Department of Health and Human Services. Public Health Services. Monthly vital statistics report advance report of final mortality statistics, 1992. 1994; 43:1-76.
83. Cestnik B, Kononenko I, Bratko I. ASSISTANT 86: a Knowledge Elicitation Tool for Sophisticated Users. In: Bratko I, Lavrac'' N, editors. Progress in Machine learning. Wilmslow: Sigma Press 1987 .
84. Fulda GJ, Tinkoff GH, Giberson F, et al. In-house trauma surgeons do not decrease mortality in a level I trauma center. J Trauma 2002;53:494-502.
85. West, John G, et al. Systems of trauma care. Arch Surg 1979;114:455.
86. MacKenzie EJ, Shapiro S, Siegel JH, et al. Functional recovery and medical costs of trauma: an analysis by type and severity of injury. J Trauma 1988; 28:281—97.
87. Rhodes M, Aronson J, Moerkirk G, et al. Quality of life after the trauma center. J Trauma 1988; 28:931—8.
88. Sampalis J, Denis R, Lavoie A, et al. Trauma care regionalization: a process outcome evaluation. J Trauma 1999; 46:565—79
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