In the realm of digital health, access to precise clinical data is fundamental to improving medical care, accelerating research, and optimizing hospital management. However, a significant portion of this information is embedded within unstructured free-text clinical notes, where diagnoses, treatments, and patient histories are recorded without standardized formats.

Natural Language Processing (NLP) plays a critical role in addressing this challenge by enabling the extraction of relevant medical information from unstructured text. IOMED specializes in the development of advanced NLP techniques designed to systematically identify and analyze mentions of pathologies within medical records.

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The Challenge of Detecting Missed Mentions

Natural Language Processing techniques harness state-of-the art machine learning models to find mentions in text together with their context. For instance, in an effort to identify all hospital patients diagnosed with psoriasis, a model could be employed to detect mentions of the condition, discarding those that correspond to family members (“mother with psoriasis”) or are negated.

Despite recent advancements in NLP, no system is infallible. Typographical errors, medical abbreviations, or less common terminologies can lead to either identifying terms incorrectly as psoriasis or, on the other side, missing the detection of certain mentions. The effectiveness of clinical NLP systems is typically assessed by measuring the frequency of each type of error using metrics such as True Positives (TP—correctly identified entities), False Positives (FP—incorrectly detected entities), and False Negatives (FN—entities that were not identified).

While false positives are clear errors, reducing false negatives is a primary objective to ensure comprehensive data capture. As an example, a NLP model may fail to detect psoriasis cases where clinicians use the abbreviation "PSO.", overlooking  a subset of relevant cases. Beyond patient identification, addressing undetected entities is essential for evaluating model performance and enhancing its ability to recognize previously missed cases.

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Methodology: An Optimized Approach to False Negative Detection

Traditional NLP model evaluation necessitates manual annotation of a representative sample of clinical documents to determine true and false positives. However, given the extensive volume of clinical text—often comprising tens of millions of records—this method proves inadequate for estimating false negatives at scale. A systematic approach is required to identify instances where the model may have overlooked key mentions.

To address this, IOMED employs a contextual similarity approach. If "psoriasis" and "PSO" denote the same medical concept, they are likely used within similar contexts in clinical notes. By leveraging this insight, it becomes possible to identify documents containing "PSO" by analyzing contextual patterns in records where "psoriasis" has already been detected. This significantly reduces the manual effort required to locate false negatives and enhances overall detection accuracy.

Rather than attempting to identify all false negatives directly, the proposed methodology focuses on automating the retrieval of records with a high probability of containing undetected mentions. The approach consists of three key steps:

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• Text Vectorization: Transform clinical notes into numerical representations using vectorization algorithms. Techniques such as TF-IDF, word embeddings, and Transformer-based models are utilized to capture semantic relationships.
• ‍Query Vector Creation: Identify notes where the target entity has been previously detected and compute an aggregated numerical representation, forming a "query vector."
• ‍Note Classification and Validation: Rank remaining clinical notes based on their cosine similarity to the query vector. Documents exhibiting higher similarity scores are prioritized for review, increasing the likelihood of detecting omitted pathology mentions.

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Key Advantages: Advancing Clinical Data Accuracy with IOMED

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• Enhanced Accuracy and Reliability: Minimizing false negatives ensures a more comprehensive and precise representation of clinical information.
• Optimized Patient Identification: Improved detection facilitates more accurate patient selection for clinical studies and research initiatives.
• Continuous Model Improvement: Iterative learning enables NLP models to refine performance and adapt to new linguistic patterns over time.

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IOMED is committed to advancing NLP methodologies for clinical research, ensuring greater precision in pathology detection while minimizing manual intervention. This innovative approach not only enhances medical research and hospital management but also contributes to improved patient care by providing more reliable clinical insights.

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