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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Zheng JPathInfo2015 6.jpg|220px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Berezin PLoSCompBio23 19-12.png|240px]]</div>
'''"[[Journal:Support patient search on pathology reports with interactive online learning based data extraction|Support patient search on pathology reports with interactive online learning based data extraction]]"'''
'''"[[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]"'''


Structural reporting enables semantic understanding and prompt retrieval of clinical findings about patients. While [[LIS feature#Synoptic reporting|synoptic pathology reporting]] provides templates for data entries, information in [[Clinical pathology|pathology]] reports remains primarily in narrative free text form. Extracting data of interest from narrative pathology reports could significantly improve the representation of the information and enable complex structured queries. However, manual extraction is tedious and error-prone, and automated tools are often constructed with a fixed training dataset and not easily adaptable. Our goal is to extract data from pathology reports to support advanced patient search with a highly adaptable semi-automated data extraction system, which can adjust and self-improve by learning from a user's interaction with minimal human effort.
[[Information]] is the cornerstone of [[research]], from experimental data/[[metadata]] and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging [[laboratory information management system]]s (LIMS) to transform this large information load into useful scientific findings. The development of [[mathematical model]]s that can predict the properties of biological systems is the holy grail of [[computational biology]]. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... ('''[[Journal:Ten simple rules for managing laboratory information|Full article...]]''')<br />


We have developed an online machine learning based information extraction system called IDEAL-X. With its graphical user interface, the system's data extraction engine automatically annotates values for users to review upon loading each report text. The system analyzes users' corrections regarding these annotations with online machine learning, and incrementally enhances and refines the learning model as reports are processed. ('''[[Journal:Support patient search on pathology reports with interactive online learning based data extraction|Full article...]]''')<br />
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Revision as of 18:03, 10 June 2024

Fig2 Berezin PLoSCompBio23 19-12.png

"Ten simple rules for managing laboratory information"

Information is the cornerstone of research, from experimental data/metadata and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging laboratory information management systems (LIMS) to transform this large information load into useful scientific findings. The development of mathematical models that can predict the properties of biological systems is the holy grail of computational biology. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... (Full article...)

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