Recommender systems in the healthcare domain: state-of-the-art and research issues
Abstract Nowadays, a vast amount of clinical data scattered across different sites on the Internet hinders users from finding helpful information for their well-being improvement. Besides, the overload of medical information (e.g., on drugs, medical tests, and treatment suggestions) have brought man...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Tran, Thi Ngoc Trang [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2020 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© The Author(s) 2020 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Journal of intelligent information systems - Springer US, 1992, 57(2020), 1 vom: 17. Dez., Seite 171-201 |
|---|---|
| Übergeordnetes Werk: |
volume:57 ; year:2020 ; number:1 ; day:17 ; month:12 ; pages:171-201 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10844-020-00633-6 |
|---|
| Katalog-ID: |
OLC2126997928 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2126997928 | ||
| 003 | DE-627 | ||
| 005 | 20230505123355.0 | ||
| 007 | tu | ||
| 008 | 230505s2020 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s10844-020-00633-6 |2 doi | |
| 035 | |a (DE-627)OLC2126997928 | ||
| 035 | |a (DE-He213)s10844-020-00633-6-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 070 |a 020 |a 004 |q VZ |
| 084 | |a 24,1 |2 ssgn | ||
| 084 | |a 54.00 |2 bkl | ||
| 100 | 1 | |a Tran, Thi Ngoc Trang |e verfasserin |0 (orcid)0000-0002-3550-8352 |4 aut | |
| 245 | 1 | 0 | |a Recommender systems in the healthcare domain: state-of-the-art and research issues |
| 264 | 1 | |c 2020 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © The Author(s) 2020 | ||
| 520 | |a Abstract Nowadays, a vast amount of clinical data scattered across different sites on the Internet hinders users from finding helpful information for their well-being improvement. Besides, the overload of medical information (e.g., on drugs, medical tests, and treatment suggestions) have brought many difficulties to medical professionals in making patient-oriented decisions. These issues raise the need to apply recommender systems in the healthcare domain to help both, end-users and medical professionals, make more efficient and accurate health-related decisions. In this article, we provide a systematic overview of existing research on healthcare recommender systems. Different from existing related overview papers, our article provides insights into recommendation scenarios and recommendation approaches. Examples thereof are food recommendation, drug recommendation, health status prediction, healthcare service recommendation, and healthcare professional recommendation. Additionally, we develop working examples to give a deep understanding of recommendation algorithms. Finally, we discuss challenges concerning the development of healthcare recommender systems in the future. | ||
| 650 | 4 | |a Health recommender systems | |
| 650 | 4 | |a Food recommendation | |
| 650 | 4 | |a Drug recommendation | |
| 650 | 4 | |a Health status prediction | |
| 650 | 4 | |a Healthcare service recommendation | |
| 650 | 4 | |a Healthcare professionals recommendation | |
| 700 | 1 | |a Felfernig, Alexander |4 aut | |
| 700 | 1 | |a Trattner, Christoph |4 aut | |
| 700 | 1 | |a Holzinger, Andreas |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of intelligent information systems |d Springer US, 1992 |g 57(2020), 1 vom: 17. Dez., Seite 171-201 |w (DE-627)171028333 |w (DE-600)1141899-0 |w (DE-576)03304032X |x 0925-9902 |7 nnns |
| 773 | 1 | 8 | |g volume:57 |g year:2020 |g number:1 |g day:17 |g month:12 |g pages:171-201 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s10844-020-00633-6 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a SSG-OLC-BUB | ||
| 912 | |a SSG-OPC-BBI | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_2244 | ||
| 936 | b | k | |a 54.00 |q VZ |
| 951 | |a AR | ||
| 952 | |d 57 |j 2020 |e 1 |b 17 |c 12 |h 171-201 | ||
| author_variant |
t n t t tnt tntt a f af c t ct a h ah |
|---|---|
| matchkey_str |
article:09259902:2020----::eomnessesnhhatcrdmisaefha |
| hierarchy_sort_str |
2020 |
| bklnumber |
54.00 |
| publishDate |
2020 |
| allfields |
10.1007/s10844-020-00633-6 doi (DE-627)OLC2126997928 (DE-He213)s10844-020-00633-6-p DE-627 ger DE-627 rakwb eng 070 020 004 VZ 24,1 ssgn 54.00 bkl Tran, Thi Ngoc Trang verfasserin (orcid)0000-0002-3550-8352 aut Recommender systems in the healthcare domain: state-of-the-art and research issues 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2020 Abstract Nowadays, a vast amount of clinical data scattered across different sites on the Internet hinders users from finding helpful information for their well-being improvement. Besides, the overload of medical information (e.g., on drugs, medical tests, and treatment suggestions) have brought many difficulties to medical professionals in making patient-oriented decisions. These issues raise the need to apply recommender systems in the healthcare domain to help both, end-users and medical professionals, make more efficient and accurate health-related decisions. In this article, we provide a systematic overview of existing research on healthcare recommender systems. Different from existing related overview papers, our article provides insights into recommendation scenarios and recommendation approaches. Examples thereof are food recommendation, drug recommendation, health status prediction, healthcare service recommendation, and healthcare professional recommendation. Additionally, we develop working examples to give a deep understanding of recommendation algorithms. Finally, we discuss challenges concerning the development of healthcare recommender systems in the future. Health recommender systems Food recommendation Drug recommendation Health status prediction Healthcare service recommendation Healthcare professionals recommendation Felfernig, Alexander aut Trattner, Christoph aut Holzinger, Andreas aut Enthalten in Journal of intelligent information systems Springer US, 1992 57(2020), 1 vom: 17. Dez., Seite 171-201 (DE-627)171028333 (DE-600)1141899-0 (DE-576)03304032X 0925-9902 nnns volume:57 year:2020 number:1 day:17 month:12 pages:171-201 https://doi.org/10.1007/s10844-020-00633-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OPC-BBI GBV_ILN_70 GBV_ILN_2244 54.00 VZ AR 57 2020 1 17 12 171-201 |
| spelling |
10.1007/s10844-020-00633-6 doi (DE-627)OLC2126997928 (DE-He213)s10844-020-00633-6-p DE-627 ger DE-627 rakwb eng 070 020 004 VZ 24,1 ssgn 54.00 bkl Tran, Thi Ngoc Trang verfasserin (orcid)0000-0002-3550-8352 aut Recommender systems in the healthcare domain: state-of-the-art and research issues 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2020 Abstract Nowadays, a vast amount of clinical data scattered across different sites on the Internet hinders users from finding helpful information for their well-being improvement. Besides, the overload of medical information (e.g., on drugs, medical tests, and treatment suggestions) have brought many difficulties to medical professionals in making patient-oriented decisions. These issues raise the need to apply recommender systems in the healthcare domain to help both, end-users and medical professionals, make more efficient and accurate health-related decisions. In this article, we provide a systematic overview of existing research on healthcare recommender systems. Different from existing related overview papers, our article provides insights into recommendation scenarios and recommendation approaches. Examples thereof are food recommendation, drug recommendation, health status prediction, healthcare service recommendation, and healthcare professional recommendation. Additionally, we develop working examples to give a deep understanding of recommendation algorithms. Finally, we discuss challenges concerning the development of healthcare recommender systems in the future. Health recommender systems Food recommendation Drug recommendation Health status prediction Healthcare service recommendation Healthcare professionals recommendation Felfernig, Alexander aut Trattner, Christoph aut Holzinger, Andreas aut Enthalten in Journal of intelligent information systems Springer US, 1992 57(2020), 1 vom: 17. Dez., Seite 171-201 (DE-627)171028333 (DE-600)1141899-0 (DE-576)03304032X 0925-9902 nnns volume:57 year:2020 number:1 day:17 month:12 pages:171-201 https://doi.org/10.1007/s10844-020-00633-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OPC-BBI GBV_ILN_70 GBV_ILN_2244 54.00 VZ AR 57 2020 1 17 12 171-201 |
| allfields_unstemmed |
10.1007/s10844-020-00633-6 doi (DE-627)OLC2126997928 (DE-He213)s10844-020-00633-6-p DE-627 ger DE-627 rakwb eng 070 020 004 VZ 24,1 ssgn 54.00 bkl Tran, Thi Ngoc Trang verfasserin (orcid)0000-0002-3550-8352 aut Recommender systems in the healthcare domain: state-of-the-art and research issues 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2020 Abstract Nowadays, a vast amount of clinical data scattered across different sites on the Internet hinders users from finding helpful information for their well-being improvement. Besides, the overload of medical information (e.g., on drugs, medical tests, and treatment suggestions) have brought many difficulties to medical professionals in making patient-oriented decisions. These issues raise the need to apply recommender systems in the healthcare domain to help both, end-users and medical professionals, make more efficient and accurate health-related decisions. In this article, we provide a systematic overview of existing research on healthcare recommender systems. Different from existing related overview papers, our article provides insights into recommendation scenarios and recommendation approaches. Examples thereof are food recommendation, drug recommendation, health status prediction, healthcare service recommendation, and healthcare professional recommendation. Additionally, we develop working examples to give a deep understanding of recommendation algorithms. Finally, we discuss challenges concerning the development of healthcare recommender systems in the future. Health recommender systems Food recommendation Drug recommendation Health status prediction Healthcare service recommendation Healthcare professionals recommendation Felfernig, Alexander aut Trattner, Christoph aut Holzinger, Andreas aut Enthalten in Journal of intelligent information systems Springer US, 1992 57(2020), 1 vom: 17. Dez., Seite 171-201 (DE-627)171028333 (DE-600)1141899-0 (DE-576)03304032X 0925-9902 nnns volume:57 year:2020 number:1 day:17 month:12 pages:171-201 https://doi.org/10.1007/s10844-020-00633-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OPC-BBI GBV_ILN_70 GBV_ILN_2244 54.00 VZ AR 57 2020 1 17 12 171-201 |
| allfieldsGer |
10.1007/s10844-020-00633-6 doi (DE-627)OLC2126997928 (DE-He213)s10844-020-00633-6-p DE-627 ger DE-627 rakwb eng 070 020 004 VZ 24,1 ssgn 54.00 bkl Tran, Thi Ngoc Trang verfasserin (orcid)0000-0002-3550-8352 aut Recommender systems in the healthcare domain: state-of-the-art and research issues 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2020 Abstract Nowadays, a vast amount of clinical data scattered across different sites on the Internet hinders users from finding helpful information for their well-being improvement. Besides, the overload of medical information (e.g., on drugs, medical tests, and treatment suggestions) have brought many difficulties to medical professionals in making patient-oriented decisions. These issues raise the need to apply recommender systems in the healthcare domain to help both, end-users and medical professionals, make more efficient and accurate health-related decisions. In this article, we provide a systematic overview of existing research on healthcare recommender systems. Different from existing related overview papers, our article provides insights into recommendation scenarios and recommendation approaches. Examples thereof are food recommendation, drug recommendation, health status prediction, healthcare service recommendation, and healthcare professional recommendation. Additionally, we develop working examples to give a deep understanding of recommendation algorithms. Finally, we discuss challenges concerning the development of healthcare recommender systems in the future. Health recommender systems Food recommendation Drug recommendation Health status prediction Healthcare service recommendation Healthcare professionals recommendation Felfernig, Alexander aut Trattner, Christoph aut Holzinger, Andreas aut Enthalten in Journal of intelligent information systems Springer US, 1992 57(2020), 1 vom: 17. Dez., Seite 171-201 (DE-627)171028333 (DE-600)1141899-0 (DE-576)03304032X 0925-9902 nnns volume:57 year:2020 number:1 day:17 month:12 pages:171-201 https://doi.org/10.1007/s10844-020-00633-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OPC-BBI GBV_ILN_70 GBV_ILN_2244 54.00 VZ AR 57 2020 1 17 12 171-201 |
| allfieldsSound |
10.1007/s10844-020-00633-6 doi (DE-627)OLC2126997928 (DE-He213)s10844-020-00633-6-p DE-627 ger DE-627 rakwb eng 070 020 004 VZ 24,1 ssgn 54.00 bkl Tran, Thi Ngoc Trang verfasserin (orcid)0000-0002-3550-8352 aut Recommender systems in the healthcare domain: state-of-the-art and research issues 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2020 Abstract Nowadays, a vast amount of clinical data scattered across different sites on the Internet hinders users from finding helpful information for their well-being improvement. Besides, the overload of medical information (e.g., on drugs, medical tests, and treatment suggestions) have brought many difficulties to medical professionals in making patient-oriented decisions. These issues raise the need to apply recommender systems in the healthcare domain to help both, end-users and medical professionals, make more efficient and accurate health-related decisions. In this article, we provide a systematic overview of existing research on healthcare recommender systems. Different from existing related overview papers, our article provides insights into recommendation scenarios and recommendation approaches. Examples thereof are food recommendation, drug recommendation, health status prediction, healthcare service recommendation, and healthcare professional recommendation. Additionally, we develop working examples to give a deep understanding of recommendation algorithms. Finally, we discuss challenges concerning the development of healthcare recommender systems in the future. Health recommender systems Food recommendation Drug recommendation Health status prediction Healthcare service recommendation Healthcare professionals recommendation Felfernig, Alexander aut Trattner, Christoph aut Holzinger, Andreas aut Enthalten in Journal of intelligent information systems Springer US, 1992 57(2020), 1 vom: 17. Dez., Seite 171-201 (DE-627)171028333 (DE-600)1141899-0 (DE-576)03304032X 0925-9902 nnns volume:57 year:2020 number:1 day:17 month:12 pages:171-201 https://doi.org/10.1007/s10844-020-00633-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OPC-BBI GBV_ILN_70 GBV_ILN_2244 54.00 VZ AR 57 2020 1 17 12 171-201 |
| language |
English |
| source |
Enthalten in Journal of intelligent information systems 57(2020), 1 vom: 17. Dez., Seite 171-201 volume:57 year:2020 number:1 day:17 month:12 pages:171-201 |
| sourceStr |
Enthalten in Journal of intelligent information systems 57(2020), 1 vom: 17. Dez., Seite 171-201 volume:57 year:2020 number:1 day:17 month:12 pages:171-201 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Health recommender systems Food recommendation Drug recommendation Health status prediction Healthcare service recommendation Healthcare professionals recommendation |
| dewey-raw |
070 |
| isfreeaccess_bool |
false |
| container_title |
Journal of intelligent information systems |
| authorswithroles_txt_mv |
Tran, Thi Ngoc Trang @@aut@@ Felfernig, Alexander @@aut@@ Trattner, Christoph @@aut@@ Holzinger, Andreas @@aut@@ |
| publishDateDaySort_date |
2020-12-17T00:00:00Z |
| hierarchy_top_id |
171028333 |
| dewey-sort |
270 |
| id |
OLC2126997928 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2126997928</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230505123355.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230505s2020 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10844-020-00633-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2126997928</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10844-020-00633-6-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">070</subfield><subfield code="a">020</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">24,1</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Tran, Thi Ngoc Trang</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-3550-8352</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Recommender systems in the healthcare domain: state-of-the-art and research issues</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2020</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Nowadays, a vast amount of clinical data scattered across different sites on the Internet hinders users from finding helpful information for their well-being improvement. Besides, the overload of medical information (e.g., on drugs, medical tests, and treatment suggestions) have brought many difficulties to medical professionals in making patient-oriented decisions. These issues raise the need to apply recommender systems in the healthcare domain to help both, end-users and medical professionals, make more efficient and accurate health-related decisions. In this article, we provide a systematic overview of existing research on healthcare recommender systems. Different from existing related overview papers, our article provides insights into recommendation scenarios and recommendation approaches. Examples thereof are food recommendation, drug recommendation, health status prediction, healthcare service recommendation, and healthcare professional recommendation. Additionally, we develop working examples to give a deep understanding of recommendation algorithms. Finally, we discuss challenges concerning the development of healthcare recommender systems in the future.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Health recommender systems</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Food recommendation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Drug recommendation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Health status prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Healthcare service recommendation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Healthcare professionals recommendation</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Felfernig, Alexander</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Trattner, Christoph</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Holzinger, Andreas</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of intelligent information systems</subfield><subfield code="d">Springer US, 1992</subfield><subfield code="g">57(2020), 1 vom: 17. Dez., Seite 171-201</subfield><subfield code="w">(DE-627)171028333</subfield><subfield code="w">(DE-600)1141899-0</subfield><subfield code="w">(DE-576)03304032X</subfield><subfield code="x">0925-9902</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:57</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:1</subfield><subfield code="g">day:17</subfield><subfield code="g">month:12</subfield><subfield code="g">pages:171-201</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10844-020-00633-6</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-BUB</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-BBI</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2244</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.00</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">57</subfield><subfield code="j">2020</subfield><subfield code="e">1</subfield><subfield code="b">17</subfield><subfield code="c">12</subfield><subfield code="h">171-201</subfield></datafield></record></collection>
|
| author |
Tran, Thi Ngoc Trang |
| spellingShingle |
Tran, Thi Ngoc Trang ddc 070 ssgn 24,1 bkl 54.00 misc Health recommender systems misc Food recommendation misc Drug recommendation misc Health status prediction misc Healthcare service recommendation misc Healthcare professionals recommendation Recommender systems in the healthcare domain: state-of-the-art and research issues |
| authorStr |
Tran, Thi Ngoc Trang |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)171028333 |
| format |
Article |
| dewey-ones |
070 - News media, journalism & publishing 020 - Library & information sciences 004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0925-9902 |
| topic_title |
070 020 004 VZ 24,1 ssgn 54.00 bkl Recommender systems in the healthcare domain: state-of-the-art and research issues Health recommender systems Food recommendation Drug recommendation Health status prediction Healthcare service recommendation Healthcare professionals recommendation |
| topic |
ddc 070 ssgn 24,1 bkl 54.00 misc Health recommender systems misc Food recommendation misc Drug recommendation misc Health status prediction misc Healthcare service recommendation misc Healthcare professionals recommendation |
| topic_unstemmed |
ddc 070 ssgn 24,1 bkl 54.00 misc Health recommender systems misc Food recommendation misc Drug recommendation misc Health status prediction misc Healthcare service recommendation misc Healthcare professionals recommendation |
| topic_browse |
ddc 070 ssgn 24,1 bkl 54.00 misc Health recommender systems misc Food recommendation misc Drug recommendation misc Health status prediction misc Healthcare service recommendation misc Healthcare professionals recommendation |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Journal of intelligent information systems |
| hierarchy_parent_id |
171028333 |
| dewey-tens |
070 - News media, journalism & publishing 020 - Library & information sciences 000 - Computer science, knowledge & systems |
| hierarchy_top_title |
Journal of intelligent information systems |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)171028333 (DE-600)1141899-0 (DE-576)03304032X |
| title |
Recommender systems in the healthcare domain: state-of-the-art and research issues |
| ctrlnum |
(DE-627)OLC2126997928 (DE-He213)s10844-020-00633-6-p |
| title_full |
Recommender systems in the healthcare domain: state-of-the-art and research issues |
| author_sort |
Tran, Thi Ngoc Trang |
| journal |
Journal of intelligent information systems |
| journalStr |
Journal of intelligent information systems |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2020 |
| contenttype_str_mv |
txt |
| container_start_page |
171 |
| author_browse |
Tran, Thi Ngoc Trang Felfernig, Alexander Trattner, Christoph Holzinger, Andreas |
| container_volume |
57 |
| class |
070 020 004 VZ 24,1 ssgn 54.00 bkl |
| format_se |
Aufsätze |
| author-letter |
Tran, Thi Ngoc Trang |
| doi_str_mv |
10.1007/s10844-020-00633-6 |
| normlink |
(ORCID)0000-0002-3550-8352 |
| normlink_prefix_str_mv |
(orcid)0000-0002-3550-8352 |
| dewey-full |
070 020 004 |
| title_sort |
recommender systems in the healthcare domain: state-of-the-art and research issues |
| title_auth |
Recommender systems in the healthcare domain: state-of-the-art and research issues |
| abstract |
Abstract Nowadays, a vast amount of clinical data scattered across different sites on the Internet hinders users from finding helpful information for their well-being improvement. Besides, the overload of medical information (e.g., on drugs, medical tests, and treatment suggestions) have brought many difficulties to medical professionals in making patient-oriented decisions. These issues raise the need to apply recommender systems in the healthcare domain to help both, end-users and medical professionals, make more efficient and accurate health-related decisions. In this article, we provide a systematic overview of existing research on healthcare recommender systems. Different from existing related overview papers, our article provides insights into recommendation scenarios and recommendation approaches. Examples thereof are food recommendation, drug recommendation, health status prediction, healthcare service recommendation, and healthcare professional recommendation. Additionally, we develop working examples to give a deep understanding of recommendation algorithms. Finally, we discuss challenges concerning the development of healthcare recommender systems in the future. © The Author(s) 2020 |
| abstractGer |
Abstract Nowadays, a vast amount of clinical data scattered across different sites on the Internet hinders users from finding helpful information for their well-being improvement. Besides, the overload of medical information (e.g., on drugs, medical tests, and treatment suggestions) have brought many difficulties to medical professionals in making patient-oriented decisions. These issues raise the need to apply recommender systems in the healthcare domain to help both, end-users and medical professionals, make more efficient and accurate health-related decisions. In this article, we provide a systematic overview of existing research on healthcare recommender systems. Different from existing related overview papers, our article provides insights into recommendation scenarios and recommendation approaches. Examples thereof are food recommendation, drug recommendation, health status prediction, healthcare service recommendation, and healthcare professional recommendation. Additionally, we develop working examples to give a deep understanding of recommendation algorithms. Finally, we discuss challenges concerning the development of healthcare recommender systems in the future. © The Author(s) 2020 |
| abstract_unstemmed |
Abstract Nowadays, a vast amount of clinical data scattered across different sites on the Internet hinders users from finding helpful information for their well-being improvement. Besides, the overload of medical information (e.g., on drugs, medical tests, and treatment suggestions) have brought many difficulties to medical professionals in making patient-oriented decisions. These issues raise the need to apply recommender systems in the healthcare domain to help both, end-users and medical professionals, make more efficient and accurate health-related decisions. In this article, we provide a systematic overview of existing research on healthcare recommender systems. Different from existing related overview papers, our article provides insights into recommendation scenarios and recommendation approaches. Examples thereof are food recommendation, drug recommendation, health status prediction, healthcare service recommendation, and healthcare professional recommendation. Additionally, we develop working examples to give a deep understanding of recommendation algorithms. Finally, we discuss challenges concerning the development of healthcare recommender systems in the future. © The Author(s) 2020 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OPC-BBI GBV_ILN_70 GBV_ILN_2244 |
| container_issue |
1 |
| title_short |
Recommender systems in the healthcare domain: state-of-the-art and research issues |
| url |
https://doi.org/10.1007/s10844-020-00633-6 |
| remote_bool |
false |
| author2 |
Felfernig, Alexander Trattner, Christoph Holzinger, Andreas |
| author2Str |
Felfernig, Alexander Trattner, Christoph Holzinger, Andreas |
| ppnlink |
171028333 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s10844-020-00633-6 |
| up_date |
2024-07-04T09:12:08.314Z |
| _version_ |
1803639144650375168 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2126997928</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230505123355.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230505s2020 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10844-020-00633-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2126997928</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10844-020-00633-6-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">070</subfield><subfield code="a">020</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">24,1</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Tran, Thi Ngoc Trang</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-3550-8352</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Recommender systems in the healthcare domain: state-of-the-art and research issues</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2020</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Nowadays, a vast amount of clinical data scattered across different sites on the Internet hinders users from finding helpful information for their well-being improvement. Besides, the overload of medical information (e.g., on drugs, medical tests, and treatment suggestions) have brought many difficulties to medical professionals in making patient-oriented decisions. These issues raise the need to apply recommender systems in the healthcare domain to help both, end-users and medical professionals, make more efficient and accurate health-related decisions. In this article, we provide a systematic overview of existing research on healthcare recommender systems. Different from existing related overview papers, our article provides insights into recommendation scenarios and recommendation approaches. Examples thereof are food recommendation, drug recommendation, health status prediction, healthcare service recommendation, and healthcare professional recommendation. Additionally, we develop working examples to give a deep understanding of recommendation algorithms. Finally, we discuss challenges concerning the development of healthcare recommender systems in the future.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Health recommender systems</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Food recommendation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Drug recommendation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Health status prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Healthcare service recommendation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Healthcare professionals recommendation</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Felfernig, Alexander</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Trattner, Christoph</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Holzinger, Andreas</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of intelligent information systems</subfield><subfield code="d">Springer US, 1992</subfield><subfield code="g">57(2020), 1 vom: 17. Dez., Seite 171-201</subfield><subfield code="w">(DE-627)171028333</subfield><subfield code="w">(DE-600)1141899-0</subfield><subfield code="w">(DE-576)03304032X</subfield><subfield code="x">0925-9902</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:57</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:1</subfield><subfield code="g">day:17</subfield><subfield code="g">month:12</subfield><subfield code="g">pages:171-201</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10844-020-00633-6</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-BUB</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-BBI</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2244</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.00</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">57</subfield><subfield code="j">2020</subfield><subfield code="e">1</subfield><subfield code="b">17</subfield><subfield code="c">12</subfield><subfield code="h">171-201</subfield></datafield></record></collection>
|
| score |
7.3991175 |