This Patient Looks Like That Patient: Prototypical Networks for Interpretable Diagnosis Prediction from Clinical Text (2022)

Abstract: The use of deep neural models for diagnosis prediction from clinical text has shown promising results. However, in clinical practice such models must not only be accurate, but provide doctors with interpretable and helpful results. We introduce ProtoPatient, a novel method based on prototypical networks and label-wise attention with both of these abilities. ProtoPatient makes predictions based on parts of the text that are similar to prototypical patients—providing justifications that doctors understand. We evaluate the model on two publicly available clinical datasets and show that it outperforms existing baselines. Quantitative and qualitative evaluations with medical doctors further demonstrate that the model provides valuable explanations for clinical decision support.

## introduction medical professionals are faced with a large amount of textual patient information every day. clinical decision support systems (cdss) aim to help clinicians in the process of decision-making based on such data. we specifically look at a subtask of cdss, namely the prediction of clinical diagnosis from patient admission notes. when clinicians approach the task of diagnosis prediction, they usually take similar patients into account (from their own experience, clinic databases or by talking to their colleagues) who presented with typical or atypical signs of a disease. they then compare the patient at hand with these previous encounters and determine the patient's risk of having the same condition. in this work, we propose protopatient, a deep neural approach that imitates this reasoning process of clinicians: our model learns prototypical characteristics of diagnoses from previous patients and present illness: complained headache of his life since yd present illness: 62yo male patient presented with worst of her life. transferred to hospital upoon neepisodes xt icu where she had episodes problems keeping food iin and of nausea transferred for a ct which showed signs of a large [...] chief complaint: headaches present illness: 62yo male patient presented with severe headaches. he had nausea and vomited multiple times. medication on admission: levoxil, spirvia, pempril, multi-vit, trazadone family history: fhx significant for dmii & htn, father with [...] this patient newly admitted to the icu that patient with prototypical signs of intracerebral hemorrhage looks like ... severe headaches. he felt nauseous and vomited multiple times... ... complained of worst headache of her life ... ... episodes of nausea and problems keeping food down ... bases its prediction for a current patient on the similarity to these prototypes. this results in a model that is both inherently interpretable and provides clinicians with pointers to previous prototypical patients. our approach is motivated by @xcite who introduced prototypical part networks (ppns) for image classification. ppns learn prototypical parts for image classes and base their classification on the similarity to these prototypical parts. we transfer this work into the text domain and apply it to the extreme multi-label classification task of diagnosis prediction. for this transfer, we apply an additional label-wise attention mechanism that further improves the interpretability of our method by highlighting the most relevant parts of a clinical note regarding a diagnosis. while deep neural models have been widely applied to outcome prediction tasks in the past @xcite , their black-box nature remains a large obstacle for clinical application @xcite . we argue that decision support is only possible when model predictions are accompanied by justifications that enable clinicians to follow a lead or to potentially discard predictions. with protopatient we introduce an architecture that allows such decision support. our evaluation on publicly available data shows that the model can further improve state-of-the-art performance on predicting clinical outcomes. contributions we summarize the contributions of this work as follows: 1. we introduce a novel model architecture based on prototypical networks and label-wise attention that enables interpretable diagnosis prediction. the system learns relevant parts in the text and points towards prototypical patients that have led to a certain decision. 2. we compare our model against several stateof-the-art baselines and show that it outperforms earlier approaches. performance gains are especially visible in rare diagnoses. 3. we further evaluate the explanations provided by our model. the quantitative results indicate that our model produces explanations that are more faithful to its inner working than post-hoc explanations. a manual analysis conducted by medical doctors further shows the helpfulness of prototypical patients during clinical decision-making. 4. we release the code for the model and experiments for reproducibility. 1 ## task: diagnosis prediction from admission notes the task of outcome prediction from admission notes was introduced by van @xcite and assumes the following situation: a new patient p gets admitted to the hospital. information about the patient is written into an admission note a p . the goal of the decision support system is to identify risk factors in the text and to communicate these risks to the medical professional in charge. for outcome diagnosis prediction in particular, the underlying model determines these risks by predicting the likelihood of a set of diagnoses c being assigned to the patient at discharge. ## method: protopatient to address the challenges above, we propose a novel model architecture called protopatient, which adapts the concept of prototypical networks @xcite to the extreme multi-label scenario by using label-wise attention and dimensionality reduction. figure 3 presents a schematic overview. we further show how our model can be efficiently initialized to improve both speed and performance. ## evaluating interpretability we evaluate the interpretability of protopatient with quantitative and qualitative analyses as follows. quantitative study on faithfulness faithfulness describes how explanations correspond to the inner workings of a model, a property essential to their usefulness. we apply the explainability benchmark introduced by @xcite to compare the faithfulness of protopatient's token highlights to post-hoc explanation methods. following the benchmark, faithfulness is measured by incrementally masking highlighted tokens, expecting a steep drop in model performance if the tokens are indeed relevant to the model prediction. see b.1 for details. due to the high computational costs of the evaluation, we limit our analyses to three diagnoses with a high severity to the icu: sepsis, intracerebral hemorrhage and pneumonia. we compare against four common post-hoc explanation methods, namely @xcite , occlusion (zeiler and fergus, 2014), inputxgradient @xcite , and gradient backpropagation @xcite , which we apply to the pubmedbert baseline. figure 5 shows the results, for which lower scores mean more faithful explanations (i.e. a steeper drop in model performance). we see that protopatient's explanations reach the lowest scores for all three labels, proving that they are more faithful than the post-hoc explanations. this is a result of the interpretable structure of protopatient, in which model decisions are directly based on the highlighted parts. we show these parts, i.e. the tokens that are most frequently highlighted by the model for the three analyzed diagnoses, in b.2. ## conclusion and future work in this work, we present protopatient which enables interpretable outcome diagnosis prediction from text. our approach enhances existing methods in their prediction capability-especially for rare classes-and presents benefits to doctors by highlighting relevant parts in the text and pointing towards prototypical patients. the modularity of prototypical networks can be explored in future research. one promising approach is to introduce multiple prototypes per diagnosis, corresponding to the multiple ways diseases can present in a patient. prototypes could also be added manually by medical professionals based on patients they consider prototypical. another approach would be to initialize prototypes from medical literature and compare them to those learned from patients.