9.1. Our system
1) [Miyachi et al., 2021]
Miyachi, Y., Torigoe, K., Ishii, O., 2021. Computer-Aided Decision Support System based on LTR algorithm - Collaboration of a clinician and the machine learning in the differential diagnosis -. Japan journal of medical informatics (Supplement CD-ROM) 41st, 801–806.
https://jglobal.jst.go.jp/detail?JGLOBAL_ID=202102273407233811
2) [Miyachi et al., 2022]
Miyachi, Y., Torigoe, K., Ishii, O., 2022. Clinical Decision Support System based on Learning to Rank - Improving diagnostic performance with Pointwise approach to Listwise approach -. In: Japanese Society for Artificial Intelligence Annual conference 2022. Japanese Society for Artificial Intelligence, Kyoto.
https://www.jstage.jst.go.jp/article/pjsai/JSAI2022/0/JSAI2022_4M1GS1001/_article/-char/ja/
3) [Kuriyama et al., 2019]
Kuriyama, Y., Sota, Y., Yano, A., Hideki, Y., Ishii, O., Saio, T., Torigoe, K., Ueda, T., Shimizu, T., Tokuda, Y., 2019. Better diagnostic performance using computer-assisted diagnostic support systems in internal medicine. Okayama Igakkai Zasshi (Journal of Okayama Medical Association).
https://www.jstage.jst.go.jp/article/joma/131/1/131_29/_article/-char/ja/
9.2. Diagnostic errors
4) [Kohn et al., 1999]
Kohn, L.T., Corrigan, J.M., Molla, S., 1999. To Err Is Human. Institute of Medicine (IOM). National Academies Press. Washington, D.C. 6.
https://www.ncbi.nlm.nih.gov/books/NBK225182/
5) [Balogh et al., 2016]
Balogh, E.P., Miller, B.T., Ball, J.R., 2016. Improving diagnosis in health care, Improving Diagnosis in Health Care.
https://www.ncbi.nlm.nih.gov/books/NBK338596/
6) [Shimizu, 2020]
Shimizu, T., 2020. Perspective: Al in diagnostic medicine. Japanese Journal of Allergology 69.
https://www.jstage.jst.go.jp/article/arerugi/69/8/69_658/_article/-char/ja/4
7) [Saposnik et al., 2016]
Saposnik, G., Redelmeier, D., Ruff, C.C., Tobler, P.N., 2016. Cognitive biases associated with medical decisions: a systematic review. BMC Med Inform Decis Mak 16.
https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-016-0377-1
9.3. Clinical Decision Support Systems, Diagnosis Decision Support Systems
8) [Berner, 2016]
Berner, E.S., 2016. Clinical decision support systems: theory and practice (Third Edition), Theory and Practice.
https://link.springer.com/book/10.1007/978-3-319-31913-1
9) [Sutton et al., 2020]
Sutton, R.T., Pincock, D., Baumgart, D.C., Sadowski, D.C., Fedorak, R.N., Kroeker, K.I., 2020. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med.
https://www.nature.com/articles/s41746-020-0221-y
10) [Schoonderwoerd et al., 2021]
Schoonderwoerd, T.A.J., Jorritsma, W., Neerincx, M.A., van den Bosch, K., 2021. Human-centered XAI: Developing design patterns for explanations of clinical decision support systems. International Journal of Human Computer Studies 154.
https://dl.acm.org/doi/10.1016/j.ijhcs.2021.102684
9.4. Differential diagnosis
11) [Stern et al., 2015]
Stern, S., Cifu, A., Altkorn, D., 2015. Symptom to Diagnosis, McGraw-Hill Education.
https://accessmedicine.mhmedical.com/book.aspx?bookID=2715
12) [Richens et al., 2020]
Richens, J.G., Lee, C.M., Johri, S., 2020. Improving the accuracy of medical diagnosis with causal machine learning. Nat Commun 11.
https://www.nature.com/articles/s41467-020-17419-7
13) [Schwartz et al., 2009]
Schwartz, A., Elstein, A.S., 2009. Clinical Problem Solving and Diagnostic Decision Making: A Selective Review of the Cognitive Research Literature. In: The Evidence Base of Clinical Diagnosis: Theory and Methods of Diagnostic Research: Second Edition.
https://pubmed.ncbi.nlm.nih.gov/11909793/
9.5. Conventional Clinical Decision Support Systems
14) [Isabel Pro n.d.]
Isabel Pro - the DDx Generator [WWW Document], n.d. URL https://www.isabelhealthcare.com/products/isabel-pro-ddx-generator (accessed 8.7.22).
15) [DXplain n.d.]
The Laboratory of Computer Science | DXplain [WWW Document], n.d. URL http://www.mghlcs.org/projects/dxplain/ (accessed 8.7.22).
16) [VisualDx n.d.]
VisualDx | Visual Clinical Decision Support System (CDSS) [WWW Document], n.d. URL https://www.visualdx.com/ (accessed 8.7.22).
17) [J-CaseMap n.d.]
J-CaseMap [WWW Document], n.d.
URL https://www.naika.or.jp/j-casemap/ (accessed 8.8.22).
9.6. Tensorflow
18) [Abadi et al., 2019]
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., 2019. Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv 2016. arXiv preprint arXiv:1603.04467.
https://arxiv.org/abs/1603.04467
19) [Pasumarthi et al., 2019]
Pasumarthi, R.K., Bruch, S., Wang, X., Li, C., Bendersky, M., Najork, M., Pfeifer, J., Golbandi, N., Anil, R., Wolf, S., 2019. TF-ranking: Scalable tensorflow library for learning-to-rank. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
https://arxiv.org/abs/1812.00073
9.7. Information Retrieval, Learning-to-Rank
20) [Liu, 2009]
Liu, T.Y., 2009. Learning to rank for Information Retrieval. Foundations and Trends in Information Retrieval 3.
https://dl.acm.org/doi/10.1561/1500000016
21) [Bruch et al., 2019]
Bruch, S., Zoghi, M., Bendersky, M., Najork, M., 2019. Revisiting approximate metric optimization in the age of deep neural networks. In: SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval.
https://dl.acm.org/doi/10.1145/3331184.3331347
22) [Bruch et al., 2020]
Bruch, S., Han, S., Bendersky, M., Najork, M., 2020. A stochastic treatment of learning to rank scoring functions. In: WSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining.
https://dl.acm.org/doi/10.1145/3336191.3371844
9.8. Case reports
23) [Fredrick et al., 2021]
Fredrick, T.W., Neto, M.B.B., Johnsrud, D.O., Camilleri, M., Chedid, V.G., 2021. Turning Purple with Pain. New England Journal of Medicine 385.
https://www.nejm.org/doi/full/10.1056/NEJMcps2105278
24) [Goldstein et al., 2021]
Goldstein, R.H., Mehan, W.A., Hutchison, B., Robbins, G.K., 2021. Case 24-2021: A 63-Year-Old Woman with Fever, Sore Throat, and Confusion. New England Journal of Medicine 385.
https://www.nejm.org/doi/full/10.1056/NEJMcpc2107345
25) [Dietz et al., 2021]
Dietz, B.W., Winston, L.G., Koehler, J.E., Margaretten, M., 2021. Copycat. New England Journal of Medicine 385, 1797–1802.
https://www.nejm.org/doi/full/10.1056/NEJMcps2108885
26) [Tsai et al., 2017]
Tsai, M.-T., Huang, S.-Y., Cheng, S.-Y., 2017. Lead Poisoning Can Be Easily Misdiagnosed as Acute Porphyria and Nonspecific Abdominal Pain. Case Rep Emerg Med 2017.
https://pubmed.ncbi.nlm.nih.gov/28630774/
27) [Indika et al., 2018)]
Indika, N.L.R., Kesavan, T., Dilanthi, H.W., Jayasena, K.L.S.P.K.M., Chandrasiri, N.D.P.D., Jayasinghe, I.N., Piumika, U.M.T., Vidanapathirana, D.M., Gunarathne, K.D.A.V., Dissanayake, M., Jasinge, E., Arachchi, W.K., Doheny, D., Desnick, R.J., 2018. Many pitfalls in diagnosis of acute intermittent porphyria: A case report. BMC Res Notes 11.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6071335/
28) [Park et al., 2009]
Park, B.J., Wannemuehler, K.A., Marston, B.J., Govender, N., Pappas, P.G., Chiller, T.M., 2009. Estimation of the current global burden of cryptococcal meningitis among persons living with HIV/AIDS. AIDS 23.
https://pubmed.ncbi.nlm.nih.gov/19182676/