Building a Clinically-Focused Problem List From Medical Notes (2022)

Abstract: Clinical notes often contain useful information not documented in structured data, but their unstructured nature can lead to critical patient-related information being missed. To increase the likelihood that this valuable information is utilized for patient care, algorithms that summarize notes into a problem list have been proposed. Focused on identifying medically-relevant entities in the free-form text, these solutions are often detached from a canonical ontology and do not allow downstream use of the detected text-spans. Mitigating these issues, we present here a system for generating a canonical problem list from medical notes, consisting of two major stages. At the first stage, annotation, we use a transformer model to detect all clinical conditions which are mentioned in a single note. These clinical conditions are then grounded to a predefined ontology, and are linked to spans in the text. At the second stage, summarization, we develop a novel algorithm that aggregates over the set of clinical conditions detected on all of the patient’s notes, and produce a concise patient summary that organizes their most important conditions.

## introduction the pervasiveness of free-text narrative in electronic health records (ehr) is both a blessing and a curse. it allows much more nuanced information about patients' conditions being saved and documented @xcite @xcite @xcite . however, the unstructured nature of the data can also impede care givers' understanding of patient conditions @xcite . to allow care providers to better understand their patients' condition from medical notes, many machine learning (ml) models have been proposed @xcite @xcite , 2020). these algorithms often solve a named-entity recognition (ner) task over the clinical notes, identifying text spans that correspond to clinical problems @xcite . while performance on such task has improved in the last decade @xcite , these models often do not link the identified entities to an ontology and are therefore sensitive to abbreviations, spelling errors and language ambiguity @xcite @xcite . moreover, these solutions operate at the note level, and are not able to aggregate a patient's overall medical problem list @xcite . both of these limitations decrease the utility of deploying these models in the real-world. another important limitation of many existing solutions is that they are built on top of recurrent neural networks designed for solving ner tasks which often do not fully utilize the nuanced linguistic signal @xcite . these approaches were shown to produce very good results on deidentification tasks on clinical notes @xcite , but can fail when presented with tasks that demand better understanding of context @xcite . the recent transition of the entire nlp community to pre-trained transformer-based models @xcite thus offers an opportunity to further improve on existing condition extraction methods @xcite . in this paper, we take on the task of addressing these problems and limitations, and describe how to build an end-to-end system that is robust and trustworthy. concretely, given a set of notes describing a single patient, our goal is to output a clinicallyfocused problem list. our system consists of two major stages: (1) annotation ( §4): operating at the level of a single medical note, we detect all clinical conditions which are mentioned in the text. these clinical conditions are then grounded to a predefined set of entities, and are linked to text spans. more formally, the output of the annotation stage is a set of tuples, where each tuple is a clinical condition identifier, a character span and context metadata (e.g., the acuity and presence of the condition). ## related work identifying patient-related information in medical notes is long recognized as a core task in clinical-nlp. as such, there exist standardized datasets and competitions @xcite @xcite @xcite . the task of identifying medical concepts in clinical notes was organized as a competition in i2b2 2010 @xcite . in i2b2 and in subsequent work, this task was defined as a named entity recognition (ner) task @xcite , where individual words are classified as to whether they contain medical problems. subsequently, a named entity normalization (nen) task, where entities are standardized into known medical concepts, was later added to the i2b2 (now n2c2) competitions @xcite . solutions to the problem consequently followed the conventional nlp approaches to solving ner tasks. recent approaches harness the transformer architecture, solving a token-level binary classification task @xcite @xcite . to connect identified text spans to an ontology, a common solution is to look for the most similar entity in a given knowledge graph. knowledge graphs use a graph-structured data model to integrate data @xcite . they are often used to store interlinked descriptions of entities-objects, events, situations or abstract concepts-while also encoding the semantics underlying the used terminology. they were shown to be very useful in the medical domain and are often used to encode medical knowledge @xcite @xcite . specifically, in the context of free-form text, as that in the clinical notes, graph structured data models can be used to map many alternative descriptions of the same condition into one canonical definition @xcite . finally, the task of aggregating patient-related information across multiple documents into one problem list in a single system was not, to the best of our knowledge, published in prior research. the focus of our work is building an end-to-end system that connects the text annotation with the summarization stage. ## ontology our system is based on a universe of entities (ontology). the ontology captures the clinical knowledge required for our system to provide a concise and clinically-focused problem list. this knowledge improves both the detection of clinical conditions in medical notes ( §4), and the subsequent bucketing of related conditions ( §5). on the detection side, it is necessary for our algorithm to be aware of the ways in which clinical conditions may appear in medical notes. for example, "iddm" ("insulin dependent diabetes mellitus") is an alternative phrasing of "diabetes mellitus type 1", and "miller" may refer to "miller fisher syndrome". on the bucketing side, it is nec-essary to have knowledge about related conditions (e.g., "biventricular congestive heart failure" is related to "right heart failure") and about possible complications of certain conditions (e.g., "diabetic nephropathy" is a complication of "diabetes mellitus"). failure to capture this knowledge may increase the redundancy at the problem list level, and might cause dilution of signals and features, which in turn results in poor quality. the ontology is therefore a fundamental building block that is being used across all the system stages, and the way it is created has critical quality implications. in this section, we describe the creation of our ontology. instead of creating a full ontology end-to-end, we have opted to base our ontology on pre-existing datasets. we collected a set of ground truth problem list, which were curated by clinicians, and examined the properties of each dataset against this ground truth. a useful ontology should demonstrate the following properties: we considered multiple data sources, including: snomed-ct @xcite , icd-10-cm (organization, 2015), and umls @xcite . ## annotation the annotation stage is performed at the level of a single clinical note. at the end of this stage each mention of a condition in the text is exported in the form of (conditionid, (start, end), contextinf o) tuple (where start, end refer to a char offset from the beginning of the note), conditionid is a unique entry in the ontology described in §3, and contextinf o includes extracted information about the condition, such as acuity, presence, etc. we start this section by describing our detection ( §4.1), and mapping ( §4.2) algorithms. ## summarization the annotation phase ( §4) outputs the mentions of clinical conditions in the medical notes. the goal of the summarization phase is to take all the mentions across all the notes and generate a comprehensive and coherent problem list, optimized for the needs of clinicians who care for the patient. in addition to the mentions themselves, the summarization phase can use additional information in the patient's chart ## conclusion in this work, we present an end-to-end system for summarizing a patient's problem list directly from their entire collection of medical notes. this system aggregates over identified conditions in each note, producing a concise list mapped to a canonical ontology and without duplicated conditions. building on recent improvements in natural language understanding models, especially encoderonly transformers, we show how nlp models can be used as part of an holistic system. we hope that our work will spur more research on how to utilize nlp for better, more robust and trustworthy, health informatics systems.