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PRODID:https://github.com/derhansen/sf_event_mgt
METHOD:PUBLISH
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UID:13-16@hms-data.de
CLASS: PUBLIC
SUMMARY:PHUSE EU Connect 2023
DESCRIPTION:PHUSE EU Connect 2023 provided a platform for professionals fro
 m life sciences, clinical research, and data analytics to exchange ideas on
  current developments, emerging technologies, and regulatory challenges in 
 the pharmaceutical industry.\n\nHMS was also there, sharing insights and ex
 perience from our project work. The event offered plenty of opportunities f
 or professional exchange, new perspectives, and discussions on how innovati
 ve technologies are transforming the way data is used in the pharmaceutical
  industry.\n\n\n\nOur contribution to PHUSE\n\nTalk\n\nMachine-Learning App
 roaches to Survival Analysis of Clinical Data\n\nDr. Dorothee Childs, Chris
 toph Bergen\n\nThis talk explores machine learning methods for survival and
  time-to-event analysis of clinical data. Starting with the classic Cox pro
 portional hazards model, it compares established methods with newer machine
  learning approaches, including random survival forests, survival support v
 ector machines, and boosted trees.\n\nUsing the Python package scikit-survi
 val, the talk demonstrates how these models can be combined with model sele
 ction, cross-validation, and performance metrics. It also explores the use 
 of SHAP to improve the interpretability of more complex models – a particul
 arly important aspect in clinical applications.\n\nDownload the presentatio
 n now Download the paper now
LOCATION:International Convention Center (ICC)
DTSTAMP:20261008T133140Z
DTSTART:20231105T080000Z
DTEND:20231109T160000Z
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