Predictive Analytics of Readmission Risk in Hospitals for an Intelligent Decision Support System:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
Hamburg
Verlag Dr. Kovač
2021
|
| Ausgabe: | 1. Auflage |
| Schriftenreihe: | Studien zur Wirtschaftsinformatik
107 |
| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis |
| Beschreibung: | XXV, 233 Seiten Illustrationen, Diagramme 21 cm x 14.8 cm, 345 g |
| ISBN: | 9783339119704 3339119708 |
Internformat
MARC
| LEADER | 00000nam a22000008cb4500 | ||
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| 100 | 1 | |a Eigner, Isabella |0 (DE-588)1229000534 |4 aut | |
| 245 | 1 | 0 | |a Predictive Analytics of Readmission Risk in Hospitals for an Intelligent Decision Support System |c Isabella Eigner |
| 250 | |a 1. Auflage | ||
| 264 | 1 | |a Hamburg |b Verlag Dr. Kovač |c 2021 | |
| 300 | |a XXV, 233 Seiten |b Illustrationen, Diagramme |c 21 cm x 14.8 cm, 345 g | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Studien zur Wirtschaftsinformatik |v 107 | |
| 502 | |b Dissertation |c Friedrich-Alexander-Universität Erlangen-Nürnberg |d 2020 | ||
| 650 | 0 | 7 | |a Entscheidungsprozess |0 (DE-588)4121202-2 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Krankenhaus |0 (DE-588)4032786-3 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Entlassungsplanung |0 (DE-588)4582491-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Risikopatient |0 (DE-588)4126574-9 |2 gnd |9 rswk-swf |
| 653 | |a Predictive Analytics | ||
| 653 | |a Machine Learning | ||
| 653 | |a Healthcare | ||
| 653 | |a Gesundheitswesen | ||
| 653 | |a Decision Support Systems | ||
| 653 | |a Entscheidungsunterstützung | ||
| 653 | |a Krankenhaus | ||
| 653 | |a Wirtschaftsinformatik | ||
| 655 | 7 | |0 (DE-588)4113937-9 |a Hochschulschrift |2 gnd-content | |
| 689 | 0 | 0 | |a Krankenhaus |0 (DE-588)4032786-3 |D s |
| 689 | 0 | 1 | |a Risikopatient |0 (DE-588)4126574-9 |D s |
| 689 | 0 | 2 | |a Entlassungsplanung |0 (DE-588)4582491-5 |D s |
| 689 | 0 | 3 | |a Entscheidungsprozess |0 (DE-588)4121202-2 |D s |
| 689 | 0 | 4 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
| 689 | 0 | |5 DE-604 | |
| 710 | 2 | |a Verlag Dr. Kovač |0 (DE-588)16100321-7 |4 pbl | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-3-339-11971-1 |
| 830 | 0 | |a Studien zur Wirtschaftsinformatik |v 107 |w (DE-604)BV012163672 |9 107 | |
| 856 | 4 | 2 | |m X:MVB |q text/html |u https://www.verlagdrkovac.de/978-3-339-11970-4.htm |3 Inhaltstext |
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Datensatz im Suchindex
| _version_ | 1804182246895124480 |
|---|---|
| adam_text | CONTENTS
LIST
OF
FIGURES
XIII
LIST
OF
TABLES
XVII
LIST
OF
ABBREVIATIONS
XXI
1
INTRODUCTION
1
1.1
MOTIVATION
AND
RELEVANCE
.............................................................
1
1.2
OBJECTIVE
AND
RESEARCH
QUESTIONS
................................................
3
1.3
RESEARCH
DESIGN
AND
STRUCTURE
OF
THE
THESIS
.............................
4
2
THEORETICAL
AND
CONCEPTUAL
FOUNDATIONS
9
2.1
INTELLIGENT
CLINICAL
DECISION
SUPPORT
SYSTEMS
.............................
9
2.1.1
DEFINITION
................................................................................
9
2.1.2
APPLICATIONS
.........................................................................
11
2.2
DIAGNOSIS-RELATED
GROUPS
................................................................
12
2.2.1
DEFINITION
................................................................................
12
2.2.2
AUSTRALIAN
REFINED
DIAGNOSIS-RELATED
GROUPS
.....................
14
2.2.3
WEIGHTED
INLIER
EQUIVALENT
SEPARATION
..............................
15
2.3
HOSPITAL
READMISSIONS
...................................................................
17
2.3.1
DEFINITION
................................................................................
17
2.3.2
READMISSION
POLICIES
.............................................................
18
2.3.3
STRATEGIES
FOR
READMISSION
REDUCTION
.................................
23
2.4
PREDICTIVE
ANALYTICS
......................................................................
25
2.4.1
DEFINITION
................................................................................
25
2.4.2
IMBALANCED
DATA
...................................................................
27
2.4.3
METHODS
................................................................................
30
X
CONTENTS
2.4.4
MODEL
EVALUATION
...............................................................
37
2.5
RELATED
WORK
...................................................................................
40
2.5.1
PREDICTION
MODELS
FOR
UNPLANNED
HOSPITAL
READMISSIONS
43
2.5.2
BENCHMARK
MODELS
................................................................
49
2.5.3
EXPLANATORY
MODELS
FOR
AIHW
READMISSION
GROUPS
.
.
52
2.6
INTERIM
CONCLUSION
..........................................................................
57
3
PREDICTION
MODELS
FOR
RISK
OF
READMISSION
59
3.1
OBJECTIVE
AND
METHOD
...................................................................
59
3.2
GOAL
DEFINITION
................................................................................
64
3.3
DATA
COLLECTION
AND
STUDY
DESIGN
..................................................
66
3.4
DATA
PREPARATION
.............................................................................
67
3.4.1
DATA
SELECTION
......................................................................
67
3.4.2
FEATURE
CREATION
...................................................................
70
3.4.3
DATA
CLEANING
......................................................................
73
3.4.4
EXPLORATORY
DATA
ANALYSIS
................................................
75
3.4.5
CHOICE
OF
VARIABLES
................................................................
85
3.5
MODEL
DEVELOPMENT
..........................................................................
86
3.5.1
DATASET
SPLIT
.........................................................................
89
3.5.2
SAMPLING
................................................................................
91
3.5.3
FEATURE
SELECTION
...................................................................
92
3.5.4
HYPERPARAMETER
TUNING
......................................................
94
3.5.5
MODEL
BUILDING
......................................................................
95
3.6
BASE
CLASSIFIERS
................................................................................
95
3.6.1
NAIVE
BAYES
.........................................................................
96
3.6.2
K-NEAREST
NEIGHBOUR
.........................................................
97
3.6.3
LOGISTIC
REGRESSION
................................................................
99
3.6.4
DECISION
TREE
.............................................................................
101
3.6.5
ARTIFICIAL
NEURAL
NETWORK
......................................................
103
3.6.6
SUPPORT
VECTOR
MACHINE
.........................................................
105
3.7
ENSEMBLE
CLASSIFIERS
...........................................................................
107
3.7.1
SAMPLING
..................................................................................
109
CONTENTS
XI
3.7.2
BAGGING
......................................................................................
109
3.7.3
BOOSTING
...................................................................................
ILL
3.8
MODEL
EVALUATION,
INTERPRETATION,
AND
SELECTION
..........................
112
3.8.1
PREDICTIVE
PERFORMANCE
OF
MODELS
...........................................
112
3.8.2
PREDICTORS
FOR
READMISSION
....................................................
118
3.9
INTERIM
CONCLUSION
.............................................................................
120
4
INTELLIGENT
CLINICAL
DECISION
SUPPORT
SYSTEM
123
4.1
OBJECTIVE
AND
METHOD
......................................................................
123
4.2
REQUIREMENTS
......................................................................................
126
4.2.1
APPROACH
...................................................................................
126
4.2.2
DECISION
MAKERS
IN
PATIENT
DISCHARGE
.....................................
127
4.2.3
FUNCTIONAL
REQUIREMENTS
...................................................
128
4.2.4
NON-FUNCTIONAL
REQUIREMENTS
................................................
134
4.3
SYSTEM
COMPONENTS
.........................................................................
135
4.3.1
OVERVIEW
...................................................................................
135
4.3.2
PATIENT
AND
EPISODE
MANAGEMENT
.........................................
137
4.3.3
READMISSION
RISK
CHART
.........................................................
138
4.3.4
COST
CHART
................................................................................
140
4.3.5
REIMBURSEMENT
CHART
.............................................................
141
4.3.6
DISCHARGE
CHECKLIST
................................................................
144
4.3.7
PREDICTION
MODEL
MANAGEMENT
.............................................
145
4.4
ARCHITECTURE
.........................................................................................
146
4.4.1
SOFTWARE
ARCHITECTURE
.............................................................
146
4.4.2
DATA
ARCHITECTURE
...................................................................
148
4.4.3
INTERACTION
ARCHITECTURE
.........................................................
150
4.5
APPLICATION
FLOW
................................................................................
152
4.5.1
USE
CASE
...................................................................................
152
4.5.2
PATIENT
AND
EPISODE
MANAGEMENT
.........................................
152
4.5.3
COSTS,
REIMBURSEMENT,
AND
READMISSION
RISK
VISUALISATION
154
4.5.4
DISCHARGE
CHECKLIST
................................................................
158
4.6
EVALUATION
............................................................................................
160
XII
CONTENTS
4.6.1
STUDY
DESIGN
AND
SETTING
......................................................
160
4.6.2
RESULTS
......................................................................................
166
4.7
INTERIM
CONCLUSION
............................................................................
171
5
DISCUSSION
AND
CONCLUSION
173
5.1
SUMMARY
.............................................................................................
173
5.2
IMPLICATIONS
..........................................................................................
177
5.3
LIMITATIONS
AND
FUTURE
RESEARCH
...................................................
178
BIBLIOGRAPHY
181
APPENDIX
219
|
| adam_txt |
CONTENTS
LIST
OF
FIGURES
XIII
LIST
OF
TABLES
XVII
LIST
OF
ABBREVIATIONS
XXI
1
INTRODUCTION
1
1.1
MOTIVATION
AND
RELEVANCE
.
1
1.2
OBJECTIVE
AND
RESEARCH
QUESTIONS
.
3
1.3
RESEARCH
DESIGN
AND
STRUCTURE
OF
THE
THESIS
.
4
2
THEORETICAL
AND
CONCEPTUAL
FOUNDATIONS
9
2.1
INTELLIGENT
CLINICAL
DECISION
SUPPORT
SYSTEMS
.
9
2.1.1
DEFINITION
.
9
2.1.2
APPLICATIONS
.
11
2.2
DIAGNOSIS-RELATED
GROUPS
.
12
2.2.1
DEFINITION
.
12
2.2.2
AUSTRALIAN
REFINED
DIAGNOSIS-RELATED
GROUPS
.
14
2.2.3
WEIGHTED
INLIER
EQUIVALENT
SEPARATION
.
15
2.3
HOSPITAL
READMISSIONS
.
17
2.3.1
DEFINITION
.
17
2.3.2
READMISSION
POLICIES
.
18
2.3.3
STRATEGIES
FOR
READMISSION
REDUCTION
.
23
2.4
PREDICTIVE
ANALYTICS
.
25
2.4.1
DEFINITION
.
25
2.4.2
IMBALANCED
DATA
.
27
2.4.3
METHODS
.
30
X
CONTENTS
2.4.4
MODEL
EVALUATION
.
37
2.5
RELATED
WORK
.
40
2.5.1
PREDICTION
MODELS
FOR
UNPLANNED
HOSPITAL
READMISSIONS
43
2.5.2
BENCHMARK
MODELS
.
49
2.5.3
EXPLANATORY
MODELS
FOR
AIHW
READMISSION
GROUPS
.
.
52
2.6
INTERIM
CONCLUSION
.
57
3
PREDICTION
MODELS
FOR
RISK
OF
READMISSION
59
3.1
OBJECTIVE
AND
METHOD
.
59
3.2
GOAL
DEFINITION
.
64
3.3
DATA
COLLECTION
AND
STUDY
DESIGN
.
66
3.4
DATA
PREPARATION
.
67
3.4.1
DATA
SELECTION
.
67
3.4.2
FEATURE
CREATION
.
70
3.4.3
DATA
CLEANING
.
73
3.4.4
EXPLORATORY
DATA
ANALYSIS
.
75
3.4.5
CHOICE
OF
VARIABLES
.
85
3.5
MODEL
DEVELOPMENT
.
86
3.5.1
DATASET
SPLIT
.
89
3.5.2
SAMPLING
.
91
3.5.3
FEATURE
SELECTION
.
92
3.5.4
HYPERPARAMETER
TUNING
.
94
3.5.5
MODEL
BUILDING
.
95
3.6
BASE
CLASSIFIERS
.
95
3.6.1
NAIVE
BAYES
.
96
3.6.2
K-NEAREST
NEIGHBOUR
.
97
3.6.3
LOGISTIC
REGRESSION
.
99
3.6.4
DECISION
TREE
.
101
3.6.5
ARTIFICIAL
NEURAL
NETWORK
.
103
3.6.6
SUPPORT
VECTOR
MACHINE
.
105
3.7
ENSEMBLE
CLASSIFIERS
.
107
3.7.1
SAMPLING
.
109
CONTENTS
XI
3.7.2
BAGGING
.
109
3.7.3
BOOSTING
.
ILL
3.8
MODEL
EVALUATION,
INTERPRETATION,
AND
SELECTION
.
112
3.8.1
PREDICTIVE
PERFORMANCE
OF
MODELS
.
112
3.8.2
PREDICTORS
FOR
READMISSION
.
118
3.9
INTERIM
CONCLUSION
.
120
4
INTELLIGENT
CLINICAL
DECISION
SUPPORT
SYSTEM
123
4.1
OBJECTIVE
AND
METHOD
.
123
4.2
REQUIREMENTS
.
126
4.2.1
APPROACH
.
126
4.2.2
DECISION
MAKERS
IN
PATIENT
DISCHARGE
.
127
4.2.3
FUNCTIONAL
REQUIREMENTS
.
128
4.2.4
NON-FUNCTIONAL
REQUIREMENTS
.
134
4.3
SYSTEM
COMPONENTS
.
135
4.3.1
OVERVIEW
.
135
4.3.2
PATIENT
AND
EPISODE
MANAGEMENT
.
137
4.3.3
READMISSION
RISK
CHART
.
138
4.3.4
COST
CHART
.
140
4.3.5
REIMBURSEMENT
CHART
.
141
4.3.6
DISCHARGE
CHECKLIST
.
144
4.3.7
PREDICTION
MODEL
MANAGEMENT
.
145
4.4
ARCHITECTURE
.
146
4.4.1
SOFTWARE
ARCHITECTURE
.
146
4.4.2
DATA
ARCHITECTURE
.
148
4.4.3
INTERACTION
ARCHITECTURE
.
150
4.5
APPLICATION
FLOW
.
152
4.5.1
USE
CASE
.
152
4.5.2
PATIENT
AND
EPISODE
MANAGEMENT
.
152
4.5.3
COSTS,
REIMBURSEMENT,
AND
READMISSION
RISK
VISUALISATION
154
4.5.4
DISCHARGE
CHECKLIST
.
158
4.6
EVALUATION
.
160
XII
CONTENTS
4.6.1
STUDY
DESIGN
AND
SETTING
.
160
4.6.2
RESULTS
.
166
4.7
INTERIM
CONCLUSION
.
171
5
DISCUSSION
AND
CONCLUSION
173
5.1
SUMMARY
.
173
5.2
IMPLICATIONS
.
177
5.3
LIMITATIONS
AND
FUTURE
RESEARCH
.
178
BIBLIOGRAPHY
181
APPENDIX
219 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Eigner, Isabella |
| author_GND | (DE-588)1229000534 |
| author_facet | Eigner, Isabella |
| author_role | aut |
| author_sort | Eigner, Isabella |
| author_variant | i e ie |
| building | Verbundindex |
| bvnumber | BV047167658 |
| classification_rvk | QX 700 |
| ctrlnum | (OCoLC)1240402060 (DE-599)DNB1225222478 |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| edition | 1. Auflage |
| format | Thesis Book |
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| genre | (DE-588)4113937-9 Hochschulschrift gnd-content |
| genre_facet | Hochschulschrift |
| id | DE-604.BV047167658 |
| illustrated | Illustrated |
| index_date | 2024-07-03T16:41:55Z |
| indexdate | 2024-07-10T09:04:30Z |
| institution | BVB |
| institution_GND | (DE-588)16100321-7 |
| isbn | 9783339119704 3339119708 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032573206 |
| oclc_num | 1240402060 |
| open_access_boolean | |
| owner | DE-N2 DE-29 |
| owner_facet | DE-N2 DE-29 |
| physical | XXV, 233 Seiten Illustrationen, Diagramme 21 cm x 14.8 cm, 345 g |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Verlag Dr. Kovač |
| record_format | marc |
| series | Studien zur Wirtschaftsinformatik |
| series2 | Studien zur Wirtschaftsinformatik |
| spelling | Eigner, Isabella (DE-588)1229000534 aut Predictive Analytics of Readmission Risk in Hospitals for an Intelligent Decision Support System Isabella Eigner 1. Auflage Hamburg Verlag Dr. Kovač 2021 XXV, 233 Seiten Illustrationen, Diagramme 21 cm x 14.8 cm, 345 g txt rdacontent n rdamedia nc rdacarrier Studien zur Wirtschaftsinformatik 107 Dissertation Friedrich-Alexander-Universität Erlangen-Nürnberg 2020 Entscheidungsprozess (DE-588)4121202-2 gnd rswk-swf Krankenhaus (DE-588)4032786-3 gnd rswk-swf Entlassungsplanung (DE-588)4582491-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Risikopatient (DE-588)4126574-9 gnd rswk-swf Predictive Analytics Machine Learning Healthcare Gesundheitswesen Decision Support Systems Entscheidungsunterstützung Krankenhaus Wirtschaftsinformatik (DE-588)4113937-9 Hochschulschrift gnd-content Krankenhaus (DE-588)4032786-3 s Risikopatient (DE-588)4126574-9 s Entlassungsplanung (DE-588)4582491-5 s Entscheidungsprozess (DE-588)4121202-2 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Verlag Dr. Kovač (DE-588)16100321-7 pbl Erscheint auch als Online-Ausgabe 978-3-339-11971-1 Studien zur Wirtschaftsinformatik 107 (DE-604)BV012163672 107 X:MVB text/html https://www.verlagdrkovac.de/978-3-339-11970-4.htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032573206&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p vlb 20210116 DE-101 https://d-nb.info/provenance/plan#vlb |
| spellingShingle | Eigner, Isabella Predictive Analytics of Readmission Risk in Hospitals for an Intelligent Decision Support System Studien zur Wirtschaftsinformatik Entscheidungsprozess (DE-588)4121202-2 gnd Krankenhaus (DE-588)4032786-3 gnd Entlassungsplanung (DE-588)4582491-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Risikopatient (DE-588)4126574-9 gnd |
| subject_GND | (DE-588)4121202-2 (DE-588)4032786-3 (DE-588)4582491-5 (DE-588)4193754-5 (DE-588)4126574-9 (DE-588)4113937-9 |
| title | Predictive Analytics of Readmission Risk in Hospitals for an Intelligent Decision Support System |
| title_auth | Predictive Analytics of Readmission Risk in Hospitals for an Intelligent Decision Support System |
| title_exact_search | Predictive Analytics of Readmission Risk in Hospitals for an Intelligent Decision Support System |
| title_exact_search_txtP | Predictive Analytics of Readmission Risk in Hospitals for an Intelligent Decision Support System |
| title_full | Predictive Analytics of Readmission Risk in Hospitals for an Intelligent Decision Support System Isabella Eigner |
| title_fullStr | Predictive Analytics of Readmission Risk in Hospitals for an Intelligent Decision Support System Isabella Eigner |
| title_full_unstemmed | Predictive Analytics of Readmission Risk in Hospitals for an Intelligent Decision Support System Isabella Eigner |
| title_short | Predictive Analytics of Readmission Risk in Hospitals for an Intelligent Decision Support System |
| title_sort | predictive analytics of readmission risk in hospitals for an intelligent decision support system |
| topic | Entscheidungsprozess (DE-588)4121202-2 gnd Krankenhaus (DE-588)4032786-3 gnd Entlassungsplanung (DE-588)4582491-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Risikopatient (DE-588)4126574-9 gnd |
| topic_facet | Entscheidungsprozess Krankenhaus Entlassungsplanung Maschinelles Lernen Risikopatient Hochschulschrift |
| url | https://www.verlagdrkovac.de/978-3-339-11970-4.htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032573206&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV012163672 |
| work_keys_str_mv | AT eignerisabella predictiveanalyticsofreadmissionriskinhospitalsforanintelligentdecisionsupportsystem AT verlagdrkovac predictiveanalyticsofreadmissionriskinhospitalsforanintelligentdecisionsupportsystem |