Genel Bilgi Konu Başlıkları Yeterlilik İlişkisi Değerlendirme Sistemi Öğrenme Çıktıları Özgü Ölçüt Matrisi

Hafta	Teori Konu Başlıkları
1	Klinik Karar Destek Sistemlerine Giriş; Kural-tabanlı vs ML-tabanlı CDS; Klinik akıl yürütme süreçleri
2	Klinik veri kaynakları; EHR, HL7, FHIR; verinin kalitesi ve veride eksiklik
3	Özellik çıkarımı; klinik zaman serileri; biyobelirteçler; temporal modelleme
4	ML, DL ve nedensel çıkarım; EHR tabanlı tahmin modelleri
5	Açıklanabilir Yapay Zekâ (XAI); Güvenilir AI; Klinik yorumlanabilirlik
6	HCI, bilişsel informatik; klinik iş akışı; alert fatigue
7	VIZE HAFTASI
8	Etik, yasa, regülasyon; FDA, EU AI Act; adalet ve yanlılık
9	Siber güvenlik; adversarial ML; gizlilik
10	Model değerlendirme; kalibrasyon; klinik fayda; karar eğrileri
11	VR/AR, dijital ikiz, robotik; yeni nesil CDS teknolojileri
12	Uygulama bilimi; değişim yönetimi; hastane entegrasyonu
13	Final sunumlar; makale üretimi; araştırma çıktılarının yayılımı
14	Final sunumlar; makale üretimi; araştırma çıktılarının yayılımı

Hafta	Uygulama Konu Başlıkları
1	1. Sutton et al. (2020) Clinical decision support. https://doi.org/10.1016/j.ijmedinf.2019.104039 2. Wright et al. (2015) CDS review. https://doi.org/10.1093/jamia/ocu017 3. Berner (2007) Clinical decision making errors. https://doi.org/10.1016/j.jbi.2006.07.001 4. Sim et al. (2001) Decision models. https://doi.org/10.1093/jamia/8.5.435 5. Roshanov et al. (2011) CDS effectiveness. https://doi.org/10.1136/bmj.d5888
2	1. Dixon et al. (2022). EHR data quality framework. https://doi.org/10.1016/j.jbi.2022.104117 2. Weiskopf & Weng (2013). Data quality dimensions. https://doi.org/10.1136/amiajnl-2013-001884 3. Liaw et al. (2019). International data standards. https://doi.org/10.1186/s12911-019-0784-4 4. Shilo et al. (2020). Data readiness for AI. https://doi.org/10.1038/s41746-020-00323-0 5. Jiang et al. (2021). Real-world clinical data challenges. https://doi.org/10.1177/20552076211019528
3	1. Payrovnaziri et al. (2020) ML in clinical prediction. https://doi.org/10.1016/j.ijmedinf.2020.104133 2. Futoma et al. (2017) Temporal modeling. https://doi.org/10.1145/3121278.3121307 3. Lipton et al. (2016) LSTM for clinical data. https://doi.org/10.48550/arXiv.1511.03677 4. Che et al. (2018) Explainable temporal models. https://doi.org/10.48550/arXiv.1511.04112 5. Suresh & Guttag (2017) Time-series biases. https://doi.org/10.48550/arXiv.1706.09138
4	1. Rajkomar et al. (2018) DL for EHR. https://doi.org/10.1038/s41746-018-0029-1 2. Miotto et al. (2016) Deep Patient. https://doi.org/10.1038/srep26094 3. Shickel et al. (2018) Deep learning in ICU. https://doi.org/10.1016/j.bspc.2017.12.001 4. Lundberg et al. (2020) Explainable ML. https://doi.org/10.1038/s42256-020-00236-6 5. Pearl (2019) Causal inference overview. https://doi.org/10.1093/pnasnex/pgz206
5	1. Ghassemi et al. (2021) Beware the black box. https://doi.org/10.1073/pnas.2011036118 2. Ribeiro et al. (2016) LIME. https://doi.org/10.1145/2939672.2939778 3. Lundberg et al. (2017) SHAP. https://doi.org/10.48550/arXiv.1705.07874 4. Holzinger et al. (2019) XAI in medicine. https://doi.org/10.1007/s13218-019-00627-4 5. Caruana et al. (2015) Interpretable models. https://doi.org/10.1145/2783258.2788613
6	1. Ancker et al. (2017) Alert fatigue. https://doi.org/10.1097/MLR.0000000000000683 2. Campbell et al. (2007) Workflow impacts. https://doi.org/10.1197/jamia.M2244 3. Russ et al. (2014) Cognitive load. https://doi.org/10.1136/bmjqs-2014-003675 4. Zheng et al. (2011) Usability problems in CDS. https://doi.org/10.1197/jamia.M3191 5. Patterson et al. (2004) Human factors. https://doi.org/10.21965/NE0001
7	MIDTERM EXAM WEEK
8	1. Char et al. (2018) Ethics of medical AI. https://doi.org/10.1056/NEJMp1714229 2. Obermeyer et al. (2019) Algorithmic bias. https://doi.org/10.1126/science.aax2342 3. Goodman & Flaxman (2017) Regulating AI. https://doi.org/10.48550/arXiv.1705.10367 4. Jobin et al. (2019) AI ethics landscape. https://doi.org/10.1038/s42256-019-0088-2 5. FDA CDS Guidance (2023) https://www.fda.gov/regulatory-information
9	1. Finlayson et al. (2019) Adversarial attacks. https://doi.org/10.1126/science.aaw4399 2. Biggio & Roli (2018) Wild patterns. https://doi.org/10.1109/MSP.2018.3111235 3. Carlini & Wagner (2017) Adversarial examples. https://doi.org/10.1109/SP.2017.49 4. Shokri et al. (2017) Membership inference. https://doi.org/10.1109/SP.2017.142 5. Papernot et al. (2016) Distillation defense. https://doi.org/10.1109/SP.2016.41
10	1. Van Calster et al. (2019) Calibration. https://doi.org/10.1186/s12916-019-1466-7 2. Vickers et al. (2007) Decision Curve Analysis. https://doi.org/10.1016/j.urology.2006.07.019 3. Collins et al. (2015) Prediction model validation. https://doi.org/10.1136/bmj.h180 4. Steyerberg et al. (2010) Clinical utility. https://doi.org/10.1016/j.jclinepi.2009.12.001 5. Riley et al. (2020) Validation strategies. https://doi.org/10.1093/ije/dyaa197
11	1. Bruynseels et al. (2018) Digital twins. https://doi.org/10.1007/s11023-018-9450-z 2. Riva et al. (2019) VR in healthcare. https://doi.org/10.3389/fpsyg.2019.02782 3. Carpinella et al. (2017) Robotics in medicine. https://doi.org/10.1145/3025453.3025752 4. Mantovani et al. (2020) XR in clinical training. https://doi.org/10.1007/s10439-020-02453-w 5. Batty et al. (2018) Digital twins for decision support. https://doi.org/10.1016/j.cities.2018.10.005
12	1. Cresswell et al. (2013) Health IT challenges. https://doi.org/10.1136/bmjopen-2013-003693 2. Greenhalgh et al. (2017) Implementation frameworks. https://doi.org/10.1136/bmj.j142 3. Ross et al. (2016) Adoption barriers. https://doi.org/10.1093/jamia/ocv174 4. McGinn et al. (2011) EMR adoption factors. https://doi.org/10.1016/j.ijmedinf.2010.09.005 5. Sittig & Singh (2010) Clinical IT safety. https://doi.org/10.1136/qshc.2009.033175
13	1. Liu et al. (2019). Deep models for CDS. https://doi.org/10.1109/JBHI.2019.2902803 2. Krittanawong et al. (2020). AI framework for healthcare research. https://doi.org/10.1136/bmj.m429 3. Topol (2019). High-level AI clinical outlook. https://doi.org/10.1038/s41591-019-0637-4 4. Beam & Kohane (2018). Integrating ML into clinical workflows. https://doi.org/10.1056/NEJMra1814259 5. Sendak et al. (2020). Real-world ML deployment lessons. https://doi.org/10.1038/s41746-020-0288-5
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