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[{"key": "dc.contributor.advisor", "value": "Kypp\u00f6, Jorma", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Virtanen, Aleksis", "language": "", "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2020-12-18T06:16:52Z", "language": null, "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2020-12-18T06:16:52Z", "language": null, "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2020", "language": "", "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/73318", "language": null, "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "L\u00e4hitulevaisuudessa potentiaalisesti l\u00e4hes kaikkea mullistava koneoppiminen on ollut vuosikymmeni\u00e4 IT-alan toimijoiden mieless\u00e4, mutta vasta viimeisen\u00e4 vuosikymmenen\u00e4 se on kyetty kunnolla ottamaan k\u00e4ytt\u00f6\u00f6n, kun tietokonelaitteistot ovat kehittyneet jatkuvasti tehokkaammiksi. Koneoppimisen kontekstissa voidaan n\u00e4hd\u00e4 p\u00e4tev\u00e4n, ett\u00e4 mit\u00e4 enemm\u00e4n dataa, sit\u00e4 paremmin koneoppimisj\u00e4rjestelm\u00e4 menestyy. Terveydenhuollossa riitt\u00e4\u00e4 dataa niin potilas-, l\u00e4\u00e4ke- kuin diagnoositietojen lis\u00e4ksi l\u00e4\u00e4ketieteellisen kuvantamisen tuloksena muodostuvissa kuvissa. Koneoppimisen perinpohjainen ja tehokas valjastaminen terveydenhuollon tueksi saa aikaan taloudellisia s\u00e4\u00e4st\u00f6j\u00e4 tehokkaampien hoitoratkaisujen kautta, ihmishenkien s\u00e4\u00e4stymist\u00e4 tarkempien diagnoosien kautta ja jokaiselle keventynytt\u00e4 mielentilaa, kun sairauksia voidaan ennustaa paremmin, jolla mahdollistetaan aikaisempi diagnoosi ja hoito. Tutkielma k\u00e4vi l\u00e4pi koneoppimisen m\u00e4\u00e4ritelm\u00e4n ja muutaman tavallisen koneoppimismenetelm\u00e4n toiminnan pintapuolisesti. My\u00f6s terveydenhuollon dataa ja digitalisaatiota k\u00e4siteltiin, sill\u00e4 niiden voidaan n\u00e4hd\u00e4 olevan selkeit\u00e4 edellytyksi\u00e4 koneoppimisen omaksunnalle. P\u00e4\u00e4osassa on tutkielman nykyhetken selvitys koneoppimisen k\u00e4ytt\u00f6kohteista terveydenhuollon piiriss\u00e4 ja koneoppimisratkaisujen diagnostisesta tarkkuudesta. K\u00e4sitellyill\u00e4 aloilla, joita ovat farmasia, farmakologia, neurologia, onkologia ja kardiologia, koneoppineet j\u00e4rjestelm\u00e4t saavuttivat vaihtelevaa tarkkuutta. Parhaimmillaan koneoppimisen hy\u00f6dynt\u00e4minen johti ammattilaisia parempaan tarkkuuteen rytmih\u00e4iri\u00f6n havaitsemisessa ja luokittelussa. Systemaattisen kirjallisuuskatsauksen kautta tutkielman tavoitteena on olla laaja, j\u00e4sennetty kokonaisuus, joka on helposti luettavissa ja jonka lukeminen mahdollistaa alan ulkopuolisillekin lukijoille pintapuolisesti kattavan k\u00e4sityksen aihealueesta.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Machine learning is in the process of transforming almost everything. It has been in the minds of information technology actors for decades, but only in the most recent decade has it properly been engaged with developments in computer hardware resulting in perpetually higher performance. In the context of machine learning it stands that with more data comes ever improving ability for machine learning to succeed. There is an abundance of data relating to patients, medicine and diagnostics in addition to data in the form of images taken as part of medical imaging. Exhaustive and effective harnessing of machine learning brings about financial savings through more effective healthcare solutions, saved lived through more accurate diagnoses and, for everyone, a lightened state of mind as diseases can better predicted, allowing for earlies diagnosis and treatment. This thesis went over the definition of machine learning and the operation of a few common machine learning methods superficially. Healthcare data and digitalization were also addressed as they can considered clear prerequisites for the adoption of machine learning. The focus of the thesis was a present-day review of the applications of machine learning in healthcare and the diagnostic performance of machine learning solutions. In the fields covered, which include pharmacy, pharmacology, neurology, oncology and cardiology, machine learning solutions performed varyingly. At its best, a machine learning solution outperformed radiologists in the detection and classification of arrhythmia. As a result of systematic literature review, the objective of the thesis is to present a broad, structured body, which can be read at ease, and which allows readers outside the field to have a superficial yet comprehensive understanding of the topic.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by Miia Hakanen (mihakane@jyu.fi) on 2020-12-18T06:16:52Z\nNo. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2020-12-18T06:16:52Z (GMT). No. of bitstreams: 0\n Previous issue date: 2020", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "30", "language": "", "element": "format", "qualifier": "extent", "schema": "dc"}, {"key": "dc.language.iso", "value": "fin", "language": null, "element": "language", "qualifier": "iso", "schema": "dc"}, {"key": "dc.rights", "value": "In Copyright", "language": "en", "element": "rights", "qualifier": null, "schema": "dc"}, {"key": "dc.title", "value": "Koneoppiminen terveydenhuollon tukena", "language": "", "element": "title", "qualifier": null, "schema": "dc"}, {"key": "dc.type", "value": "bachelor thesis", "language": null, "element": "type", "qualifier": null, "schema": "dc"}, {"key": "dc.identifier.urn", "value": "URN:NBN:fi:jyu-202012187264", "language": "", "element": "identifier", "qualifier": "urn", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Bachelor's thesis", "language": "en", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Kandidaatinty\u00f6", "language": "fi", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Informaatioteknologian tiedekunta", "language": "fi", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Faculty of Information Technology", "language": "en", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Informaatioteknologia", "language": "fi", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Information Technology", "language": "en", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "Jyv\u00e4skyl\u00e4n yliopisto", "language": "fi", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "University of Jyv\u00e4skyl\u00e4", "language": "en", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Tietoj\u00e4rjestelm\u00e4tiede", "language": "fi", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Information Systems Science", "language": "en", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "yvv.contractresearch.funding", "value": "0", "language": "", "element": "contractresearch", "qualifier": "funding", "schema": "yvv"}, {"key": "dc.type.coar", "value": "http://purl.org/coar/resource_type/c_7a1f", "language": null, "element": "type", "qualifier": "coar", "schema": "dc"}, {"key": "dc.rights.accesslevel", "value": "openAccess", "language": null, "element": "rights", "qualifier": "accesslevel", "schema": "dc"}, {"key": "dc.type.publication", "value": "bachelorThesis", "language": null, "element": "type", "qualifier": "publication", "schema": "dc"}, {"key": "dc.subject.oppiainekoodi", "value": "601", "language": "", "element": "subject", "qualifier": "oppiainekoodi", "schema": "dc"}, {"key": "dc.subject.yso", "value": "koneoppiminen", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "terveydenhuolto", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "teko\u00e4ly", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.rights.url", "value": "https://rightsstatements.org/page/InC/1.0/", "language": null, "element": "rights", "qualifier": "url", "schema": "dc"}]
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