Publications by authors named "S Rathnam"
Importance: While abundant work has examined patient-level differences in antidepressant treatment outcomes, little is known about the extent of clinician-level differences. Understanding these differences may be important in the development of risk models, precision treatment strategies, and more efficient systems of care.
Objective: To characterize differences between outpatient clinicians in treatment selection and outcomes for their patients diagnosed with major depressive disorder across academic medical centers, community hospitals, and affiliated clinics.
Peptides and proteins have recently emerged as efficient therapeutic alternatives to conventional therapies. Although they emerged a few decades back, extensive exploration of various ailments or disorders began recently. The drawbacks of current chemotherapies and irradiation treatments, such as drug resistance and damage to healthy tissues, have enabled the rise of peptides in the quest for better prospects.
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- The paper discusses discount regularization, a method used in planning for Markov Decision Processes (MDPs) that simplifies the optimization process by ignoring delayed effects, which is especially useful with sparse data.! -
- The authors introduce a new perspective on discount regularization, showing that using a lower discount factor leads to an optimal policy that behaves similarly to stronger regularization applied unevenly based on the amount of data for each state-action pair.! -
- They present a solution by establishing a new approach for setting regularization parameters specifically for each state-action pair, with supporting examples highlighting the shortcomings of traditional discount regularization and the advantages of their proposed method in practical applications, including a medical simulation.!