LEADER 04453cam 2200625Ii 4500001 on1268150254 003 OCoLC 005 20221026165753.0 006 m o d 007 cr unu|||||||| 008 210914s2021 ncua obm 000 0 eng d 035 (Sirsi) o1268150254 035 (OCoLC)1268150254 040 ERE |beng |erda |cERE |dOCLCO |dOCLCQ |dOCLCO |dOCLCF |dERE |dUtOrBLW 043 n-us--- 049 EREE 090 R859.7.E43 100 1 Chilcoat, Elizabeth, |eauthor. |?UNAUTHORIZED 245 12 A clinical decision system with an interactive knowledge graph and cost optimization / |cby Elizabeth Chilcoat. 264 1 [Greenville, N.C.] : |b[East Carolina University], |c2021. 300 1 online resource (89 pages) : |bcolor illustrations 336 text |btxt |2rdacontent 337 computer |bc |2rdamedia 338 online resource |bcr |2rdacarrier 347 text file 347 |bPDF 347 |c2.505 MB 538 System requirements: Adobe Reader. 538 Mode of access: World Wide Web. 502 |bM.S. |cEast Carolina University |d2021 500 Presented to the faculty of the Department of Computer Science 500 Advisor: Kamran Sartipi 500 Title from PDF t.p. (viewed July 15, 2022). 520 3 Clinical decision support systems aim to improve access to relevant and practical clinical data to assist medical practitioners in diagnosis and treatment. However, the usefulness of such systems is limited due to the lack of effective user interactions and proper cost management for treatments. Medical price transparency has been an issue in the United States for many years. Well-meaning care providers may refer patients to specialists or order tests that are unexpectedly costly and may not be covered by the patient's insurance without knowing this is the case. This thesis proposes solutions to the above issues through allowing interactive navigation of a knowledge graph of medical conditions and symptoms and novel cost management decision support. A growing number of medical costs are now available to the public due to a new U.S. law. We propose utilizing newly available cost data to allow medical practitioners to be aware of and consider these costs when they are making decisions about a patient's care. To this end, we propose providing the ability to help filter possible conditions the patient may have by using information about the patient, including symptoms. This includes an easily navigable graph where each node presents the likelihood of a patient having certain medical conditions as each new symptom is learned. Once new information about the patient is exhausted, we propose finding the order to test that patient's possible conditions that minimizes the overall expected cost. We use a combination of synthetic data generation and realistic data collected from published papers to evaluate these approaches. Overall, we find that such a system would be beneficial for a non-trivial number of cases that medical clinics will will handle. However, it is most helpful for rarer instances where patients have few symptoms or uncommon medical conditions. 504 Includes bibliographical references. 650 0 Medical informatics |zUnited States. |=^A1005440 650 0 Medical records |zUnited States |xData processing. |=^A429942 650 0 Information visualization |zUnited States. |=^A595771 650 0 Information storage and retrieval systems |xMedical care, Cost of. |=^A1448801 653 Clinical Decision Support Systems 653 Monte Carlo tree search 653 Knowledge Graph 655 7 Academic theses. |2fast |0(OCoLC)fst01726453 655 7 Academic theses. |2lcgft 655 7 Thèses et écrits académiques. |2rvmgf |0(CaQQLa)RVMGF-000001173 655 2 Academic Dissertation. |0(DNLM)D019478 |?UNAUTHORIZED 700 1 Sartipi, Kamran, |edegree supervisor. |?UNAUTHORIZED 710 2 East Carolina University. |bDepartment of Computer Science. |?UNAUTHORIZED 856 40 |uhttp://hdl.handle.net/10342/9415 |zAccess via ScholarShip 949 |owjh 994 C0 |bERE 596 1 4 998 5668820