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[Series R] Medical RL : ã³ãŒã¹ã®å šäœå

第0éšïŒå»çMDPã®å®åŒå â äºæž¬ãããä»å ¥ããž
| ID | ã¿ã€ãã« | æŠèŠã»ããŒã¯ãŒã |
|---|---|---|
| R01 | R01ïŒäºæž¬ã¢ãã« vs æ¹çã¢ãã« | æåž«ããåŠç¿ (SL), ä»å ¥å¹æ, åäºå® (Counterfactual), 亀絡å å, æ¹ç (π), äºæž¬èª€å·® vs æææ±ºå®èª€å·® |
| R02 | R02ïŒå»çMDPã®å®çŸ© | ãã«ã³ã決å®éçš (MDP), ç¶æ 空é (S), è¡å空é (A), å ±é ¬é¢æ° (R), å²åŒç (γ), ãã©ã€ãŸã³ (H), è»è·¡ (τ) |
| R03 | R03ïŒéšåèŠ³æž¬æ§ (POMDP) | POMDP, ä¿¡å¿µç¶æ (b), é ãç¶æ , èŠ³æž¬é¢æ° (O), ç¶æ 衚çŸåŠç¿, ãã€ãªããŒã«ãŒã®äžç¢ºå®æ§ |
| R04 | R04ïŒäžèŠåæç³»åã®åŠç | äžèŠåæç³»å, æçãªæ¬ 枬 (Informative Missingness), æ¬ æå€è£å®, RNN/LSTM, Neural ODE, é»åã«ã«ãååŠç |
| R05 | R05ïŒãã³ãã£ããåé¡ãšèšåºè©Šéš | å€è ãã³ãã£ãã (MAB), æèä»ããã³ãã£ãã, ãªã°ã¬ããæå°å, ã©ã³ãã 忝èŒè©Šéš (RCT), é©å¿çã©ã³ãã å, LinUCB |
| R06 | R06ïŒå ææšè«ãšRL | å ææšè«, DoæŒç®å (do(x)), ã·ã³ããœã³ã®ãã©ããã¯ã¹, åŸåã¹ã³ã¢, é確çéã¿ä»ã (IPW), æªèŠ³æž¬ã®äº€çµ¡å å |
| R07 | R07ïŒæ¢çŽ¢ãšæŽ»çšã®ãžã¬ã³ã (å»çç) | æ¢çŽ¢ãšæŽ»çš, ε-Greedyæ³, UCB (ä¿¡é Œåºéäžé), ãã³ããœã³ãµã³ããªã³ã°, å®å šæ§å¶çŽ, å«ççèª²é¡ |
| R08 | R08ïŒã¢ãã«ããŒã¹ vs ã¢ãã«ããªãŒ | ã¢ãã«ããŒã¹RL, ã¢ãã«ããªãŒRL, ãã©ã³ãã³ã°, åçèšç»æ³, ãµã³ãã«å¹ç, ççåŠçäºåç¥è |
| R09 | R09ïŒç°å¢æ§ç¯ (Gymnasium) | Gymnasium API, Env.step(), Env.reset(), 芳枬空é, è¡å空é, å ±é ¬ã·ã§ã€ãã³ã°, çãªå ±é ¬ (Sparse Reward) |
| R10 | R10ïŒ[æŒç¿] ããŒã¿ã»ããã®æŽå | MIMIC-IV, eICU, ã³ããŒãéžæ, SQL, Pandas, ç¶æ ã»è¡åãã¢äœæ, ããŒã¿ã¯ãªãŒãã³ã° |
第IéšïŒOffline RL â ãæ¢çŽ¢ã§ããªããçŸå®ãšã®éã
ã éå»ã®èšºçãã°ïŒãŽãã®å±±ïŒãããæåæïŒå®ç³ïŒãèŠã€ãã ã
| ID | ã¿ã€ãã« | æŠèŠã»ããŒã¯ãŒã |
|---|---|---|
| R11 | Offline RLã®å¿ ç¶æ§ | ãªãã©ã€ã³åŒ·ååŠç¿ (Batch RL), éçããŒã¿ã»ãã, æåæ¹ç (πβ), ã¿ãŒã²ããæ¹ç (π), çžäºäœçšãªãã®åŠç¿ |
| R12 | ååžã·ãã (Distribution Shift) | ååžã·ãã, ååžå€ (OOD), 倿¿èª€å·®, é倧è©äŸ¡ãã€ã¢ã¹, ãµããŒãå¶çŽ, å ±å€éã·ãã |
| R13 | è¡åæš¡å£ (Behavior Cloning) | è¡åæš¡å£ (BC), æåž«ããåŠç¿, 亀差ãšã³ããããŒæå€±, æš¡å£åŠç¿, ãšãã¹ããŒãã®ãã¢ã³ã¹ãã¬ãŒã·ã§ã³ |
| R14 | BCã®éçãšè€å誀差 | è€å誀差, ç¶æ ååžã®äžäžèŽ, Dagger (åè), ã€ã³ã¿ã©ã¯ãã£ããªå°éå®¶, æºæé©ãªããŒã¿ |
| R15 | ä¿å®çQåŠç¿ (CQL) | Conservative Q-Learning (CQL), Qå€ã®æ£åå, æ²èŠ³çæŽæ°, äžçæå€§å, ãã©ã¡ãŒã¿ α ã®èª¿æŽ |
| R16 | IQL (Implicit Q-Learning) | Implicit Q-Learning (IQL), ãšã¯ã¹ãã¯ã¿ã€ã«ååž°, SARSA圢åŒã®æŽæ°, ã€ã³ãµã³ãã«åŠç¿, ã¢ããã³ããŒãžéã¿ä»ãååž° (AWR) |
| R17 | Decision Transformer (DT) | Decision Transformer, ç³»åã¢ããªã³ã°, Transformer, Return-to-Go (RTG), æ³šææ©æ§, ããã³ããã£ã³ã° |
| R18 | Trajectory Transformer | Trajectory Transformer (TT), ããŒã ãµãŒã, 颿£å, Transformerã«ããèšç», é·æäŸåæ§ |
| R19 | äžç¢ºå®æ§ã®å®éå | èªèè«çäžç¢ºå®æ§, å¶ç¶çäžç¢ºå®æ§, ã¢ã³ãµã³ãã«åŠç¿, ããããã¢ãŠã, ãã€ãºãã¥ãŒã©ã«ãããã¯ãŒã¯, OODæ€ç¥ |
| R20 | [æŒç¿] æè¡çæ²»çæ¹çã®åŠç¿ | d3rlpy, ãªãã©ã€ã³RLãã€ãã©ã€ã³, æè¡çæ²»ç, æå§å€, 茞液, è¡åã®é¢æ£å |
第IIéšïŒOPE & Safety â çµ±èšçè©äŸ¡ãšãªã¹ã¯ç®¡ç
ã 宿пå
¥åã«ãå®å
šæ§ããæ°åŠçã«ä¿èšŒãã ã
| ID | ã¿ã€ãã« | æŠèŠã»ããŒã¯ãŒã |
|---|---|---|
| R21 | ãªãæ¹çè©äŸ¡ (OPE) æŠè« | ãªãæ¹çè©äŸ¡ (OPE), ã¿ãŒã²ããæ¹çã®äŸ¡å€, æšå®åš, åäºå®çè©äŸ¡, å®å šæ§ä¿èšŒ |
| R22 | éç¹ãµã³ããªã³ã° (IS / WIS) | éç¹ãµã³ããªã³ã° (IS), å ééç¹ãµã³ããªã³ã° (WIS), éç¹æ¯ (å¯åºŠæ¯), é«ã忣 |
| R23 | Doubly Robust (DR) æ³ | Doubly Robust (DR), å¶åŸ¡å€æ°, åæ£äœæž, ã¢ãã«ãã€ã¢ã¹, çŽæ¥æ³ (DM) |
| R24 | Fitted Q Evaluation (FQE) | Fitted Q Evaluation (FQE), Q颿°ã®ååž°, Neural FQE, ããããã¯ã¹æé©å, æ€èšŒç²ŸåºŠ |
| R25 | OPEã®ä¿¡é Œåºé | ä¿¡é Œåºé (CI), ããŒãã¹ãã©ããæ³, äžéå€, ãããã£ã³ã°ã®äžçåŒ, tæ€å®, ãªã¹ã¯è©äŸ¡ |
| R26 | å¶çŽä»ãMDP (CMDP) | å¶çŽä»ãMDP (CMDP), ã³ã¹ãä¿¡å· (C), ã©ã°ã©ã³ãžã¥ä¹æ°, äž»å察æé©å, å®å šæ§äºç® |
| R27 | CPO (Constrained Policy Optimization) | Constrained Policy Optimization (CPO), ä¿¡é Œé å, å®è¡å¯èœé å, çŽç·æ¢çŽ¢, å¶çŽã®è¿äŒŒå è¶³ |
| R28 | ãªã¹ã¯æåæ§RL | ãªã¹ã¯æåæ§RL, CVaR (æ¡ä»¶ä»ãããªã¥ãŒã»ã¢ããã»ãªã¹ã¯), VaR, ååžå匷ååŠç¿, ææªã±ãŒã¹æ§èœ |
| R29 | è§£éå¯èœæ§ (XRL) | 説æå¯èœRL (XRL), SHAP, ãµãªãšã³ã·ãŒããã, æ±ºå®æšãžã®èžç, åå®ä»®æ³ç説æ |
| R30 | [æŒç¿] OPEã«ããèšåºçåŠ¥åœæ§æ€èšŒ | ope-tools, ã¹ã³ãŒã, 仮説æ€å®, æåºŠåæ, å»åž«ã®æ¹çãšã®æ¯èŒ |
第IIIéšïŒSimulation & Control â çäœã¢ãã«ãšé£ç¶å¶åŸ¡
ã ããžã¿ã«ãã€ã³ïŒçäœã·ãã¥ã¬ãŒã¿ïŒãçšãã粟å¯å¶åŸ¡ ã
| ID | ã¿ã€ãã« | æŠèŠã»ããŒã¯ãŒã |
|---|---|---|
| R31 | ççåŠçã·ãã¥ã¬ãŒã·ã§ã³ (PK/PD) | åžžåŸ®åæ¹çšåŒ (ODE), PK/PDã¢ããªã³ã°, ã³ã³ããŒãã¡ã³ãã¢ãã«, è¬ç©åæ åŠ, ä»®æ³æ£è |
| R32 | é£ç¶å€å¶åŸ¡ã¢ã«ãŽãªãºã | é£ç¶å¶åŸ¡, PPO (Proximal Policy Optimization), SAC (Soft Actor-Critic), ãšã³ããããŒæ£åå, ã¬ãŠã¹æ¹ç |
| R33 | ããæ²»çã®æé©å | è «ç墿®ã¢ãã«, ãŽã³ãã«ã墿®, ååŠçæ³ã¹ã±ãžã¥ãŒã«, å€ç®çæé©å, ãã¬ãŒãããã³ãã£ã¢ |
| R34 | éº»é æ·±åºŠã®éã«ãŒãå¶åŸ¡ | éã«ãŒãå¶åŸ¡, PIDå¶åŸ¡, TCI (ç®æšå¶åŸ¡æ³šå ¥), ããããã©ãŒã«, BISã¢ãã¿ãŒ, é å»¶ã·ã¹ãã |
| R35 | 人工åŒåžåšã®å¶åŸ¡ | 人工åŒåžç®¡ç, ARDS, PEEPæé©å, FiO2, åŒåžããšã®å¶åŸ¡, éå調 (Asynchrony) |
| R36 | Sim-to-Real 転移 | Sim-to-Real, ãªã¢ãªãã£ã®ã£ãã, ã·ã¹ãã åå®, ãã£ãªãã¬ãŒã·ã§ã³, ããžã¿ã«ãã€ã³ |
| R37 | ãã¡ã€ã³ã©ã³ãã å (DR) | ãã¡ã€ã³ã©ã³ãã å, ããã¹ãæ§, ãã©ã¡ãŒã¿æå, èŠèŠçã©ã³ãã å, ãã€ããã¯ã¹ã©ã³ãã å |
| R38 | ã¢ãã«ããŒã¹RL (MBRL) | ã¢ãã«ããŒã¹RL, äžçã¢ãã« (World Models), DreamerV3, RSSM (ååž°ç¶æ 空éã¢ãã«), æœåšãã€ããã¯ã¹, æœåšç©ºéã§ã®èšç» |
| R39 | éå±€å匷ååŠç¿ (HRL) | éå±€åRL, ãªãã·ã§ã³ãã¬ãŒã ã¯ãŒã¯, æéçæœè±¡å, ãããŒãžã£ãŒã»ã¯ãŒã«ãŒæ§é , ãµããŽãŒã«, Semi-MDP |
| R40 | [æŒç¿] ããååŠçæ³ã®ã¬ãžã¡ã³æé©å | Stable-Baselines3, ã«ã¹ã¿ã ç°å¢ (Gym), å ±é ¬é¢æ°èšèš, ãã€ããŒãã©ã¡ãŒã¿èª¿æŽ, ã·ãã¥ã¬ãŒã·ã§ã³åæ |
第IVéšïŒLLM & Reasoning â èšèªã¢ãã«ã«ããæææ±ºå®
ã ãæ°å€ãã ãã§ãªããè«çãã§æšè«ãããšãŒãžã§ã³ã ã
| ID | ã¿ã€ãã« | æŠèŠã»ããŒã¯ãŒã |
|---|---|---|
| R41 | LLM Agentã®åºç€ | LLMãšãŒãžã§ã³ã, ReAct (Reason+Act), ããŒã«å©çš, 颿°åŒã³åºã, ãŒãã·ã§ããèšç», ã³ã³ããã¹ããŠã£ã³ã㊠|
| R42 | RLHF (Reinforcement Learning from Human Feedback) | RLHF, å ±é ¬ã¢ãã« (RM), ã€ãã¬ãŒãã£ã³ã°, Bradley-Terryã¢ãã«, KLãã€ããŒãžã§ã³ã¹ããã«ãã£, PPO for LLM |
| R43 | DPO (Direct Preference Optimization) | DPO, æé»ã®å ±é ¬, åç §ã¢ãã«, éžå¥œããŒã¿, å®å®ããåŠç¿, 察æ°ç¢ºç |
| R44 | æèã®é£é (CoT) ã®åŒ·ååŠç¿ | æèã®é£é (CoT), æšè«ã®çè·¡, DeepSeek-R1, GRPO (Group Relative Policy Optimization), è«ççäžè²«æ§ |
| R45 | ããã»ã¹å ±é ¬ã¢ãã« (PRM) | ããã»ã¹å ±é ¬ã¢ãã« (PRM), çµæå ±é ¬ã¢ãã« (ORM), ã¹ãããããšã®æ€èšŒ, MCTS (ã¢ã³ãã«ã«ãæšæ¢çŽ¢), æ°åŠã»è«çæšè« |
| R46 | Med-Agentã®å®è£ | RAG (æ€çŽ¢æ¡åŒµçæ), ãã¯ãã«ããŒã¿ããŒã¹, ãã¬ããžã°ã©ã, 蚺çã¬ã€ãã©ã€ã³, LangChain / LangGraph |
| R47 | ãã«ããšãŒãžã§ã³ãå調 | ãã«ããšãŒãžã§ã³ãRL (MARL), ããŒã«ãã¬ã€ã³ã°, è°è«, åæåœ¢æ, CTDE (éäžåŠç¿ã»åæ£å®è¡), ã³ãã¥ãã±ãŒã·ã§ã³ |
| R48 | èšåºèŠçŽã®çæãšè©äŸ¡ | èšåºèŠçŽ, æ å ±æœåº, NLPã®ããã®åŒ·ååŠç¿, ROUGE / BERTScore, å¹»èŠ (Hallucination) ã®äœæž |
| R49 | èªå·±ä¿®æ£ (Self-Correction) | èªå·±ä¿®æ£, èªå·±æŽç·Ž, Constitutional AI, RLAIF (AIãã£ãŒãããã¯ã«ããRL), æ¹è©ãšä¿®æ£ |
| R50 | [æŒç¿] èšºææšè«ãšãŒãžã§ã³ãã®æ§ç¯ | ãã¡ã€ã³ãã¥ãŒãã³ã° (SFT), GRPOå®è£ , å»çQAããŒã¿ã»ãã (PubMedQA/MedQA), è©äŸ¡ææš |
第VéšïŒDeployment â 瀟äŒå®è£
ãšèŠå¶ç§åŠ
ã ç 究宀ããç
é¢ãžãã·ã¹ãã ãšããŠã®çµ±å ã
| ID | ã¿ã€ãã« | æŠèŠã»ããŒã¯ãŒã |
|---|---|---|
| R51 | Human-in-the-loop (HITL) | Human-in-the-Loop, èšåºæææ±ºå®æ¯æŽ (CDS), èªç¥çè² è·, ä¿¡é Œã®èŒæ£, ãªãŒããŒã©ã€ãæ©æ§ |
| R52 | MLOps for RL | MLOps, ç¹åŸŽéã¹ãã¢, ã¢ãã«ã¬ãžã¹ããª, åŠç¿ãã€ãã©ã€ã³, æšè«ãµãŒããŒ, å®éšè¿œè·¡ (MLflow) |
| R53 | ããªããæ€ç¥ãšã¢ãã¿ãªã³ã° | ã³ã³ã»ããããªãã, ããŒã¿ããªãã, å ±å€éã·ããæ€ç¥, ç¶ç¶çåŠç¿ (CL), ç Žæ» çå¿åŽ |
| R54 | å»çæ å ±æšæº (FHIR/HL7) | FHIR (Fast Healthcare Interoperability Resources), HL7, EHR飿º, SMART on FHIR, çžäºéçšæ§ |
| R55 | é£ååŠç¿ (Federated RL) | é£ååŠç¿ (FL), é£åRL, ãã©ã€ãã·ãŒä¿è·AI, å·®åãã©ã€ãã·ãŒ, å®å šãªéçŽ |
| R56 | èŠå¶ç§åŠ (Regulatory Science) | SaMD (å»çæ©åšããã°ã©ã ), FDAã¬ã€ãã©ã€ã³, PCCP (æå®å€æŽå¶åŸ¡èšç»), GMLP, ãªã¹ã¯ç®¡ç |
| R57 | èšåºè©Šéšãã¶ã€ã³ | èšåºè©Šéšãã¶ã€ã³, RCT (ã©ã³ãã 忝èŒè©Šéš), é壿§è©Šéš, äž»èŠ/坿¬¡è©äŸ¡é ç®, ãµã³ãã«ãµã€ãºèšç® |
| R58 | å«çç課é¡ãšãã€ã¢ã¹ | ã¢ã«ãŽãªãºã çå ¬å¹³æ§, é åãã€ã¢ã¹, å»çã®å ¬å¹³æ§, 説æè²¬ä»», éææ§, ãããã³åé¡ |
| R59 | Computational Medicineã®æªæ¥ | AGI (æ±çšäººå·¥ç¥èœ), æ±çšå»çAI, åºç€ã¢ãã«, åå¥åå»ç, ããžã¿ã«ãã€ã³ |
| R60 | [æçµèª²é¡] End-to-End System Design | ã·ã¹ãã ã¢ãŒããã¯ãã£, åé¡å®çŸ©, ã¢ãã«ã©ã€ããµã€ã¯ã«, ROI (æè³å¯Ÿå¹æ), å®è£ èšç» |
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