Update app.py

app.py

@@ -51,23 +51,10 @@ app = hy.HydraApp(title='Biomedical Epidemiology NER App',
 def my_home():
     hy.markdown("<h3 style='text-align: center; color: black;'>Biomedical Epidemiology Named Entity Recognition System </h3>", unsafe_allow_html=True)
 
-    st.write("""There are a few challenges related to the task of biomedical named
-    entity recognition, which are: the existing methods consider a fewer
-    number of biomedical entities (e.g., disease, symptom, proteins,
-    genes); and these methods do not consider the social determinants
-    of health (age, gender, employment, race), which are the non-
-    medical factors related to patients’ health. We propose a machine
-    learning pipeline that improves on previous efforts in the following
-    ways: first, it recognizes many biomedical entity types other than
-    the standard ones; second, it considers non-clinical factors related
-    to patient’s health. This pipeline also consists of stages, such as pre-
-    processing, tokenization, mapping embedding lookup and named
-    entity recognition task to extract biomedical named entities from
-    the free texts. We present a new dataset that we prepare by curating
-    the COVID-19 case reports. The proposed approach outperforms
-    the baseline methods on five benchmark datasets with macro-and
-    micro-average F1 scores around 90, as well as our dataset with a
-    macro-and micro-average F1 score of 95.25 and 93.18 respectively""")
+    st.write("""This application presents a generalizable ML pipeline capable of identifying and recognizing many biomedical named entities in texts. In three significant ways, this pipeline improves on previous efforts. First, it can recognize over 50 different entity types, including clinical entities (disease, symptoms, risks, effects, drugs, diabetes, respiration, vital signs, and others), as well as non-clinical entities, such as event-based data, social factors that are not clinical factors but are related to health outcomes. Second, with no code changes, this pipeline is simple to use and adaptable to individual methods for a given data type, task, or domain of application. Third, this pipeline can take any free texts, for example, in the form of text or PDF files and parse them for scientific texts. We hope that this application will provide a more transparent and customizable solution for the healthcare industry, helping to educate and encourage more rigorous applications of ML to biomedical analyses.
+    
+    The implications of this application in the context of healthcare are multi-facet. For example, these biomedical entity types can help doctors, nurses, and other healthcare professionals align symptoms to diagnosis, treatment, and follow-up. There are also opportunities for policymakers to understand the value that is within electronic and clinical medical records to understand the cost-effectiveness and cost-saving planning. For example, knowing the number of clinically informative, human diagnoses within population groups can assist learning health systems in planning strategies. Tracking social determinants can lead to reducing biases in the health data. This research can also be used to translate the clinical data into knowledge, evidence, and clinical impact.
+""")
     hy.image("Epidemiologist.jpeg")
 
 @app.addapp(title='Entity Recognizer', icon="far fa-copy",)