Original: On the Importance of Clinical Notes in Multi-modal Learning for EHR Data
Revised: On the Importance of Clinical Notes in Multi-modal Learning for EHR Data

Original: AffiliationAddress

Original: Abstract
Revised: Abstract

Original: Understanding deep learning model behavior is critical to accepting machinelearning-based decision support systems in the medical community.
Revised: Understanding deep learning model behavior is critical to accepting machine learning-based decision support systems in the medical community.

Original: Previous workshave shown that jointly using clinical notes with electronic health record (EHR)data improved predictive performance for patient monitoring in the intensive careunit (ICU).
Revised: Previous research has shown that jointly using clinical notes with electronic health record (EHR) data improved predictive performance for patient monitoring in the intensive care unit (ICU).

Original: In this work, we explore the underlying reasons for these improvements.
Revised: In this work, we explore the underlying reasons for these improvements.

Original: While relying on a basic attention-based model to allow for interpretability, wefirst confirm that performance significantly improves over state-of-the-art EHRdata models when combining EHR data and clinical notes.
Revised: While relying on a basic attention-based model to allow for interpretability, we first confirm that performance significantly improves over state-of-the-art EHR data models when combining EHR data and clinical notes.

Original: We then provide ananalysis showing improvements arise almost exclusively from a subset of notescontaining broader context on patient state rather than clinician notes.
Revised: We then provide an analysis showing improvements arise almost exclusively from a subset of notes containing broader context on patient state rather than clinician notes.

Original: We believesuch findings highlight deep learning models for EHR data to be more limited bypartially-descriptive data than by modeling choice, motivating a more data-centricapproach in the field
Revised: We believe such findings highlight deep learning models for EHR data to be more limited by partially-descriptive data than by modeling choice, motivating a more data-centric approach in the field.

Original:  Introduction
Revised:  Introduction

Original: Recently, researchers have explored using deep neural networks for clinical decision support, particularly for Intensive Care Unit (ICU) prediction tasks.
Revised: Recently, researchers have explored using deep neural networks for clinical decision support, particularly for Intensive Care Unit (ICU) prediction tasks.

Original: Most existing works focused on usingeither available electronic health records [1, 2, 3, 4, 5, 6, 7] (EHR) or clinical notes [8, 9, 10, 11]individually.
Revised: Most existing literature focused on using either available electronic health records [1, 2, 3, 4, 5, 6, 7] (EHR) or clinical notes [8, 9, 10, 11] individually.

Original: However, more recent works show that combining both data types in a cross-modalfashion improves performance [12, 13] on a popular suite of MIMIC-III [14] benchmark tasks [15].
Revised: However, more recent research shows that combining both data types in a cross-modal fashion improves performance [12, 13] on a popular suite of MIMIC-III [14] benchmark tasks [15].

Original: Ultimately,  such improvement in the field can help better plan the resource usage in ICU [12, 16].
Revised: Ultimately, the goal of such improvement in the field can help better plan the resource usage in ICU [12, 16].

Original: Though, as for any real-world application, acceptance by clinicians will require an understanding ofthe behavior of these new cross-modal models.
Revised: Though, as for any real-world application, acceptance by clinicians will require an understanding of the behavior of these new cross-modal models.

Original: Some papers provided some analysis for uni-modalmodels using EHR data [4] or clinical notes [8].
Revised: Other studies provided analyses for uni-modal models using EHR data [4] or clinical notes [8].

Original: To the best of our knowledge, similar work whencombining both modalities to monitor patients in ICU does not exist.
Revised: To the best of our knowledge, similar work investigating the cross-modal interactions when combining both modalities to monitor patients in ICU does not exist.

Original: We aim to fill that gap by exploring the underlying reasons for performance improvements obtainedfrom jointly using EHR data and clinical notes.
Revised: We aim to fill that gap by exploring the underlying reasons for performance improvements obtained from jointly using EHR data and clinical notes.

Original: Like Horn et al.
Revised: Like Horn et al.

Original: [4], which analyzed the performancesof EHR-only models, we rely on a transformer-based [17] model, as attention weights provide adirect explanation of the deep learning model behavior.
Revised: [4], which analyzed the performances of EHR-only models, we rely on a transformer-based [17] model, as attention weights provide a direct explanation of the deep learning model behavior.

Original: Because of the multi-modal nature of thedata, we adopt a multi-modal extension [18] to this original transformer architecture.
Revised: Due to the multi-modal nature of the data, we adopt a multi-modal extension [18] to this original transformer architecture.

Original: In this work,first, we confirm that the most basic cross-modal transformer model combining EHR data and clinicalnotes performs significantly better than state-of-the-art uni-modal approaches on all classificationtasks from the MIMIC-III benchmark [15].
Revised: In this work, first, we confirm that the most basic cross-modal transformer model combining EHR data and clinical notes performs significantly better than state-of-the-art uni-modal approaches on all classification tasks from the MIMIC-III benchmark [15].

Original: Then, by performing a two-step analysis, we show thatthe performance gain compared to EHR-only models arises exclusively from a subset of nurses’notes and radiology reports, which provide additional context on patients’ states.
Revised: Second, by performing a two-step analysis, we show that the performance gain compared to EHR-only models arises exclusively from a subset of nurses’ notes and radiology reports. These notes provide additional context on patients’ states.

Original: Moreover, we alsoshow that using clinicians’ notes, which summarize EHR contents rather than containing additionalinformation, does not provide any performance gain, refuting the hypothesis that current architectures poorly represent EHR information.
Revised: Moreover, we also show that using clinicians’ notes, which summarize EHR contents rather than containing additional information, does not provide any performance gain. Thereby, we refute the hypothesis that current architectures poorly represent EHR information.

Original: We believe our findings motivate more data-centric approaches inthe field, focusing on leveraging more descriptive data to model patients’ states in the ICU.
Revised: We believe our findings motivate more data-centric approaches in the field, focusing on leveraging more descriptive data to model patients’ states in the ICU.

Original:  Related work
Revised:  Related work

Original: Deep Learning for ICU patient monitoring.
Revised: Deep Learning for ICU patient monitoring.

Original: As previously mentioned, we can categorize worksusing deep learning to monitor patients’ evolution in the ICU according to the modality used.
Revised: As previously mentioned, we can categorize existing research using deep learning to monitor patients’ evolution in the ICU according to the modality used.

Original: First,the most common line of work relies on the EHR, viewed as a multivariate time series.
Revised: First, the most common line of work relies on the EHR, viewed as a multivariate time series.

Original: A large groupof works tackles this problem with various architectures in both supervised [1, 2, 3, 4, 5, 6, 7] andunsupervised fashion [19, 20].
Revised: A large group of related literature tackles this problem with various architectures in both supervised [1, 2, 3, 4, 5, 6, 7] and unsupervised fashion [19, 20].

Original: Other researchers focus exclusively on clinical notes [8, 9, 10, 11].
Revised: Other researchers focus exclusively on clinical notes [8, 9, 10, 11].

Original: At last, motivating our work, more recent works explored fusing both modalaties [12, 13, 21, 22],yielding significant improvement over previous uni-modal approaches.
Revised: At last, motivating our work, more recent research explored fusing both modalities [12, 13, 21, 22], yielding significant improvement over previous uni-modal approaches.

Original: Unfortunately, in this lastbody of work, the authors work with independent representations for each modality forbiddingretrieving cross-modal interaction, thus limiting interpretability.
Revised: Unfortunately, in this last body of work, the authors work with independent representations for each modality making it difficult to retrieve cross-modal interaction, thus limiting interpretability.

Original: Interpretability in deep learning.
Revised: Interpretability in deep learning.

Original: Widely used methods in deep learning interpretability relyon saliency-map-based methods like LIME [23], GRAD-CAM [24], and SHAP [25].
Revised: Widely used methods in deep learning interpretability rely on saliency-map-based methods like LIME [23], GRAD-CAM [24], and SHAP [25].

Original: Though,these methods are limited [26] and are not necessarily compatible with sequence data.
Revised: Though, these methods are limited [26] and are not necessarily compatible with sequence data.

Original: Thus, webase our analysis on attention-based methods, a more common approach for sequence modeling.
Revised: Thus, we base our analysis on attention-based methods, a more common approach for sequence modeling.

Original: Various works have investigated the use of attention weights as a potential post hoc interpretabilitymechanism [27, 28, 29, 30].
Revised: Various papers have investigated the use of attention weights as a potential post hoc interpretability mechanism [27, 28, 29, 30].

Original: In addition, other works further developed methods beyond attentionweights, such as attention flow and rollout [31].
Revised: In addition, other literature further developed methods beyond attention weights, such as attention flow and rollout [31].

Original: In the case of attention flow, [32] proved thatattention flows are layer-wise Shapley values.
Revised: In the case of attention flow, [32] proved that attention flows are layer-wise Shapley values.

Original: Attention rollout is often employed in computer visionto provide attention heatmaps over the input [33, 34].
Revised: Attention rollout is often employed in computer vision to provide attention heatmaps over the input [33, 34].

Original: In the context of ICU data, similarly to us,
Revised: In the context of ICU data, similarly to us,

Original: Horn et al.
Revised: Horn et al.

Original: [4] also based their uni-modal model behavior analysis on attention weights.
Revised: [4] also based their uni-modal model behavior analysis on attention weights.

Original:  Method
Revised:  Method

Original: In this section, we introduce our interpretable model to handle both clinical notes and EHR data.
Revised: In this section, we introduce our interpretable model to handle both clinical notes and EHR data.

Original: Wealso describe hyperparameter choice and data pre-processing to ensure a better analysis of clinicalnotes’ importance.
Revised: We also describe hyperparameter choice and data pre-processing to ensure a better analysis of clinical notes’ importance.

Original: [Figure]
Revised: [Figure]

Original: Architecture.
Revised: Architecture.

Original: We consider two modalities
Revised: We consider two modalities

Original: X EHR ∈ R T EHR ×
Revised: X EHR ∈ R T EHR ×

Original: d EHR and X CN ∈
Revised: d EHR and X CN ∈

Original: R T CN × d CN , where T EHR and
Revised: R T CN × d CN , where T EHR and

Original: T CN denote the length of the EHR data and clinical notes duringthe given patient’s stay, and d EHR and d CN the input featuredimensionality.
Revised: T CN denote the length of the EHR data and clinical notes during the given patient’s stay, and d EHR and d CN the input feature dimensionality.

Original: To obtain X CN we use  pre-trained Clinical BERTmodel [35] as a text feature extractor, d CN = 769 .
Revised: To obtain X CN we use the pre-trained Clinical BERT model [35] as a text feature extractor, d CN = 769 .

Original: Note that d CNis greater than the original Clinical BERT output dimension byone.
Revised: Note that d CN is greater than the original Clinical BERT output dimension by one.

Original: It is because clinical notes are not regularly spaced, whichcan’t be accounted for by positional encoding.
Revised: This is because clinical notes are not regularly spaced, which cannot be accounted for by positional encoding.

Original: Thus, we addthe entry time of notes as the last feature.
Revised: Thus, we add the entry time of notes as the last feature.

Original: The dimensionality of EHR data is d EHR = 42 after one-hot encoding of categoricalvariables.
Revised: The dimensionality of EHR data is d EHR = 42 after one-hot encoding of categorical variables.

Original: Following the original paper [18], we pass the inputmodalities through a 1D convolutional layer to embed the textseries and the time series into the same latent space.
Revised: Following the original paper [18], we pass the input modalities through a 1D convolutional layer to embed the text series and the time series into the same latent space.

Original: Then we addpositional encoding to each modality.
Revised: Then we add positional encoding to each modality.

Original: Because we make predictionsat X EHR frequency, we always obtain queries from X EHR and keys and values from X EHR or X CN, respectively, in the self-attention and cross-attention layers.
Revised: Since we make predictions at X EHR frequency, we always obtain queries from X EHR and keys and values from X EHR or X CN in the self-attention and cross-attention layers, respectively.

Original: Figure 1 shows the employedarchitecture.
Revised: Figure 1 shows the employed architecture.

Original: Tasks definition.
Revised: Tasks definition.

Original: Previous works jointly using EHR data and clinical notes [12, 13] relied on
Revised: Previous literature jointly using EHR data and clinical notes [12, 13] relied on

Original: MIMIC-III data [36].
Revised: MIMIC-III data [36].

Original: Like other existing datasets, MIMIC-III consists of physiological measurementsand information about laboratory tests.
Revised: Like other existing datasets, MIMIC-III consists of physiological measurements and information about laboratory tests.

Original: However, contrary to others, in MIMIC-III, a significantnumber of patients have clinical notes written by various medical personnel allowing for a multimodal approach.
Revised: However, contrary to others, in MIMIC-III, a significant number of patients have clinical notes written by various medical personnel allowing for a multimodal approach.

Original: Thus, for our experiments, we follow previous work by analyzing clinical notes’importance on Harutyunyan et al.
Revised: Thus, for our experiments, we follow previous research by analyzing clinical notes’ importance on Harutyunyan et al.

Original: [15]’s benchmark tasks.
Revised: [15]’s benchmark tasks.

Original: In particular, we focus our work on threetasks.
Revised: In particular, we focus our work on three tasks.

Original: First, decompensation , an hourly binary prediction task on whether the patient dies in the next 
Revised: First, decompensation , an hourly binary prediction task on whether the patient dies in the next 24 hours.

Original: [Table] 24 hours or not. Second, in-hospital mortality (IHM), a  single binary prediction task after 48 hours ofobserved ICU data.
Revised: Second, in-hospital mortality (IHM), a similar task with a single binary prediction task after 48 hours of observed ICU data on whether the patient dies in the ICU.

Original: Finally, phenotyping , a multi-class classification problem where we predict whichconditions, out of 25 possibles outcomes, the patient experienced during his stay.
Revised: Finally, phenotyping , a multi-class classification problem where one predicts which acute care conditions were present during a patient’s stay out of 25 care conditions.

Original: The benchmarkcontains another task, hourly predicting the remaining length of stay.
Revised: The benchmark contains another task, hourly predicting the remaining length of stay.

Original: We do not consider this taskdue to its lower clinical relevance and the poor performance of all existing deep learning models onthe task.
Revised: We do not consider this task due to its lower clinical relevance and the poor performance of all existing deep learning models on the task.

Original: We further discuss the data and its processing in Appendix A.
Revised: We further discuss the data and its processing in Appendix A.

Original: Hyperparameter tuning.
Revised: Hyperparameter tuning.

Original: Increasing the number of heads or layers in transformers usually improves performances but also complexifies the models’ analysis.
Revised: Increasing the number of heads or layers in transformers usually improves performance but also makes the models’ analysis more complex.

Original: As we already observed significantimprovement over state-of-the-art uni-modal models using a single layer and head, we fixed thesetwo hyperparameters to 1 in all experiments.
Revised: Since we already observed significant improvement over state-of-the-art uni-modal models using a single layer and head, we fixed these two hyperparameters to 1 in all experiments.

Original: Doing so allowed us to preserve a more explainablemodel at the cost of a slight loss in performance.
Revised: Doing so allowed us to preserve a more explainable model at the cost of a slight loss in performance.

Original: All other hyperparameters were selected using a gridsearch on  validation performance of decompensation .
Revised: All other hyperparameters were selected using a grid search on the validation performance of decompensation .

Original: For the convolutional layer, we use 64 filterswith a size equal to the input space.
Revised: For the convolutional layer, we use 64 filters with a size equal to the input space.

Original: In the transformer, we use a latent space of size 64 everywhereand a dropout rate of 0 . 2 .
Revised: In the transformer, we use a latent space of sizeeverywhere and a dropout rate of 0 . 2 .

Original: We trained all models with a batch size of 16 patients, a learning rate of1 e − 5 , and Adam optimizer [37].
Revised: We trained all models with a batch size of 16 patient stays, a learning rate of 1 e − 5 , and Adam optimizer [37].

Original: Data processing.
Revised: Data processing.

Original: As it is common for MIMIC-III benchmark data [15], we used forward imputationand standard scaling as pre-processing steps.
Revised: As it is common for MIMIC-III benchmark data [15], we used forward imputation and standard scaling as pre-processing steps.

Original: More importantly, we mask out the last note in alltasks to avoid label leaks through direct death mentions.
Revised: More importantly, we mask out the last note in all tasks to avoid label leaks through direct death mentions.

Original: It is one simple way to reduce informationleaking.
Revised: This is a simple way to reduce information leaking.

Original: One can go further by filtering samples using keyword search as [11].
Revised: Though, it is possible to tackle this more in-depth by filtering samples using keyword search as [11].

Original: As for the choice ofhyperparameters, masking the last note might reduce absolute performance compared to previousworks not following the same procedure [12, 13], but ensure a fairer analysis of the role of the clinicalnotes.
Revised: As for the choice of hyperparameters, masking the last note might reduce absolute performance compared to previous research not following the same procedure [12, 13], but ensure a fairer analysis of the role of the clinical notes.

Original:  Experiments
Revised:  Experiments

Original: In this section, we first show that even with hard constraints on model complexity, jointly using EHRdata and clinical notes outperforms existing uni-modal models on the considered tasks.
Revised: In this section, we first show that even with hard constraints on model complexity, jointly using EHR data and clinical notes outperforms existing uni-modal models on the considered tasks.

Original: We thenperform an ablation study on note types and analyze cross-modal attention weights highlighting theimportance of either “nurse” or “radiology” notes in the cross-modal model decision-making.
Revised: We then perform an ablation study on note types and analyze cross-modal attention weights highlighting the importance of either “nurse” or “radiology” notes for cross-modal model decision-making.

Original: Allresults reported are from the test set and obtained over 5 random seeds.
Revised: All results reported are from the test set and obtained over 5 random seeds.

Original: Overall performance.
Revised: Overall performance.

Original: As shown in Table 1, while only using a single cross-attention layer with asingle head and not attending to the last note, using a joint model surpasses previous work using either EHR data [15] or clinical notes [12].
Revised: As shown in Table 1, while only employing a single cross-attention layer with a single head and not attending to the last note, using a joint model surpasses previous literature’s performance utilizing either EHR data [15] or clinical notes [12].

Original: This observation confirms prior findings on the addition broughtby clinical notes in patient monitoring [12, 13] and justifies the need to understand the underlyingreason for such improvement.
Revised: This observation confirms prior findings on the addition brought by clinical notes in patient monitoring [12, 13] and justifies the need to understand the underlying reason for such improvement.

Original: Interestingly, we also observe that our model performs on par with thebest cross-modal model on both decompensation and phenotyping tasks despite  constraints.
Revised: Interestingly, we also observe that our model performs on par with the best cross-modal model on both decompensation and phenotyping tasks despite our constraints.

Original: Note type ablation.
Revised: Note type ablation.

Original: To understand which note types are important to the decision-making ina cross-modal approach, we carry out ablation studies where we cumulatively add note types byincreasing or by decreasing frequency (see Appendix B).
Revised: To understand which note types are important to the decision-making in our cross-modal model, we carry out ablation studies where we cumulatively add note types by increasing or by decreasing frequency (see Appendix B).

Original: Figure 2 shows the ablation studies forthe decompensation and IHM tasks by increasing frequency.
Revised: Figure 2 shows the ablation studies for the decompensation and IHM tasks by increasing frequency.

Original: We draw two conclusions.
Revised: We draw two conclusions.

Original: On the onehand, adding doctors (“Physicians”) notes does not improve performance.
Revised: On the one hand, adding doctors’ (“Physicians”) notes does not improve performance.

Original: This suggests that currentrepresentations obtained from the EHR data alone are sufficient.
Revised: This suggests that current representations obtained from the EHR data alone are sufficient.

Original: On the other hand, adding nurses’notes and radiology reports for IHM significantly improves AUPRC on both tasks.
Revised: On the other hand, adding nurses’ notes and radiology reports for IHM significantly improves AUPRC on both tasks.

Original: Similar resultscan be found for phenotyping in Appendix B. These findings show that, when using clinical notes, I C U + O t h e r + R
Revised: Similar results can be found for phenotyping in Appendix B. These findings show that, when using clinical notes, deep learning models benefit from additional textual information rather than a possibly better representation of similar information in doctors’ notes summarizing the patient’s state.

Original: e s p

Original: i r a t o r y

Original: + E c h o + D i s c
Revised: [Table]

Original: h

Original: a r g e + E C G + P
Revised: a r g e + E C G + P

Original: h
Revised: h

Original: y s i
Revised: y s i

Original: c
Revised: c

Original: i a n + R a d i o l o g y + N u r s e
Revised: i a n + R a d i o l o g y + N u r s e

Original: Data used 30.
Revised: Data used 30.

Original: A U P R C
Revised: A U P R C

Original: I C U + O t
Revised: I C U + O t

Original: h e r + R
Revised: h e r + R

Original: e s p
Revised: e s p

Original: i r a t o r y
Revised: i r a t o r y

Original: + E c
Revised: + E c

Original: h o
Revised: h o

Original: + D
Revised: + D

Original: i s c
Revised: i s c

Original: h
Revised: h

Original: a r g e + E C G
Revised: a r g e + E C G

Original: + P
Revised: + P

Original: h
Revised: h

Original: y s i
Revised: y s i

Original: c
Revised: c

Original: i a n + R a d i o l o g y + N u r s e
Revised: i a n + R a d i o l o g y + N u r s e

Original: Data used
Revised: [Figure]

Original: A U P R C deep learning models benefit from additional information rather than a possibly better representationof similar information in doctors’ notes.
Revised: [Figure] U [Figure]

Original: Cross-modal attention weights analysis.
Revised: Cross-modal attention weights analysis.

Original: For all tasks, we observed that notes among the two mostfrequent types, nurses’ and radiology notes, lead to the greatest improvements.
Revised: For all tasks, we observed that notes among the two most frequent types, nurses’ and radiology notes, lead to the greatest improvements.

Original: Thus, one could arguethat the note frequency explains the increase in performance over the note type itself.
Revised: Thus, one could argue that the note frequency explains the increase in performance over the note type itself.

Original: To answer thisquestion, we perform an analysis of cross-modal attention weights.
Revised: To answer this question, we perform an analysis of cross-modal attention weights.

Original: As shown with an illustrativeexample in Figure 3, attention weights are very salient, making each EHR timestep attend to only asubset of notes.
Revised: As shown with an illustrative example in Figure 3, attention weights are very salient, making each EHR timestep attend to only a subset of notes.

Original: In addition, also shown in Figure 3, highly attended notes, such as the 7th note inthe example, also impact the model’s final prediction.
Revised: In addition, also shown in Figure 3, highly attended notes, such as the 7th note in the example, also impact the model’s final prediction.

Original: Indeed, when it obtains this note at hour 45,the cross-modal model’s predictions diverge from the EHR-only model ones.
Revised: Indeed, when it obtains this note at hour 45, the cross-modal model’s predictions diverge from the EHR-only model ones.

Original: This high saliency ofattention weights refutes the hypothesis that the nurses’ and radiology notes are the most relevanttypes due to their high frequency.
Revised: This high saliency of attention weights refutes the hypothesis that the nurses’ and radiology notes are the most relevant types due to their high frequency.

Original: Indeed, in practice, only a subset of these notes are attended toindependently of the notes type frequency.
Revised: Indeed, in practice, only a subset of these notes are attended to independently of the notes type frequency.

Original: We provide additional examples to support this claim in
Revised: We provide additional examples to support this claim in

Original: Appendix C.1.
Revised: Appendix C.

Original: [Figure]
Revised: [Figure]

Original: {+(hours) 
+}
Revised: {+(hours) 
+}

Revised: N o t e

Original:  Conclusion
Revised:  Conclusion

Original: In this paper, we aim to understand the underlying reason behind the significant improvement broughtby jointly using EHR data and clinical notes in a multi-modal fashion in ICU-related tasks.
Revised: In this paper, we aim to understand the underlying reason behind the significant improvement brought by jointly using EHR data and clinical notes in a multi-modal fashion in ICU-related tasks.

Original: To answerthis question, we analyze the  available note types in MIMIC-III and the cross-modal attention weightsin our model.
Revised: To answer this question, we analyze the performance impact of the available note types in MIMIC-III and the cross-modal attention weights in our model.

Original: Based on our experimental results, we conclude that improvements yielded by addingclinical notes come from notes containing additional context on patient states, such as nurses’ notesand radiology reports.
Revised: Based on our experimental results, we conclude that improvements yielded by adding clinical notes come from notes containing additional context on patient states, such as nurses’ notes and radiology reports.

Original: We also find that clinicians’ notes, which often summarize EHR content, donot improve performance, suggesting that representations learned from EHR data alone preserve theinput information.
Revised: We also find that doctors’ notes, which often summarize EHR content, do not improve performance, suggesting that representations learned from EHR data alone preserve the input information.

Original: We believe these findings show how EHR data alone is only a narrow view ofpatient states in the ICU, motivating more data-centric approaches in the field.
Revised: We believe these findings show how EHR data alone is only a narrow view of patient states in the ICU, motivating more data-centric approaches in the field.

Original: A DatasetA.1 Pre-processing
Revised: A Dataset A.1 Pre-processing

Original: We first run the pre-processing step provided by Harutyunyan et al.
Revised: We first run the pre-processing step provided by Harutyunyan et al.

Original: [15] for EHR data.
Revised: [15] for EHR data.

Original: Next, weextract all patients with notes.
Revised: Next, we extract all patients with notes.

Original: Echo, electrocardiogram (ECG), and discharge summary notes have no CHARTTIME , the exact time at which the note is entered in the system.
Revised: Echo, electrocardiogram (ECG), and discharge summary notes have no CHARTTIME , the exact time at which the note is entered in the system.

Original: However, these notes mightcontain valuable medical information.
Revised: However, these notes might contain valuable medical information.

Original: Thus, we set their CHARTTIME to the end of the day of their CHARTDATE , which contains only the day at which the note was entered.
Revised: Thus, we set their CHARTTIME to the end of the day of their CHARTDATE , which contains only the day on which the note was entered.

Original: It ensures no temporal leakof information.
Revised: It ensures no temporal leak of information.

Original: Once this is done, following previous work [12], we then exclude patients withoutany notes.
Revised: Following previous work [12], we then exclude patients without any notes.

Original: From the benchmark task with 6281 ICU stays in the test set, we exclude 27 patients andend up with 6254 ICU stays.
Revised: From the benchmark task with 6281 ICU stays in the test set, we exclude 27 patients and end up with 6254 ICU stays.

Original: For some of these stays, only phenotyping task labels exist, hence, forthe decompensation task, we end up with 6215 labeled ICU stays in the test set.
Revised: For some of these stays, only phenotyping task labels exist, hence, for the decompensation task, we end up with 6215 labeled ICU stays in the test set.

Original: We follow the sameapproach for the training set, validation set, and test set.
Revised: We follow the same approach for the training set, validation set, and test set.

Original: A.2 Statistics
Revised: A.2 Statistics

Original: For further details, we provide in Table 2 the test set statistics for the decompensation and IHM tasks.
Revised: For further details, we provide in Table 2 the test set statistics for the decompensation and IHM tasks.

Original: In addition, Table 3 shows the note type counts in the training and test set.
Revised: In addition, Table 3 shows the note type counts in the training and test set.

Original: We ran the ablation studiesaccording to the note type frequency in the training set.
Revised: We ran the ablation studies according to the note type frequency in the training set.

Original: [Table] [Table]
Revised: [Table] [Table]

Original: B Ablation Studies
Revised: B Ablation Studies

Original: In this section [Table] we provide further results regarding notes type ablations.
Revised: In this section, we provide further results regarding notes type ablations.

Original: [Table]
Revised: [Table]

Original: B.1 Increasing Frequency
Revised: B.1 Increasing Frequency

Original: [Table]

Original: We first summarize result from the ablation by increasing frequency from Figure 2 in Table 4.
Revised: We first summarize results from the ablation by increasing frequency from Figure 2 in Table 4.

Original: Wefind, as previously mentioned, that for all tasks, nurses’ or radiology notes contribute to improvedperformance.
Revised: We find, as previously mentioned, that for all tasks, nurses’ or radiology notes contribute to improved performance.

Original: For the phenotyping task, we see that also other note types help.
Revised: For the phenotyping task, we see that also other note types help.

Original: It may be because thesetypes contain information for specific illnesses not necessarily present in the other notes, thus alsofrom some additional context on patients’ states.
Revised: It may be because these types contain information for specific illnesses not necessarily present in the other notes, thus also from some additional context on patients’ states.

Original: Nonetheless, the main contribution to performanceremains from radiology reports.
Revised: Nonetheless, the main contribution to performance remains from radiology reports.

Original: I C U + O t
Revised: I C U + O t

Original: h e r + R
Revised: h e r + R

Original: e s p
Revised: e s p

Original: i r a t o r y
Revised: i r a t o r y

Original: + E c
Revised: + E c

Original: h o
Revised: h o

Original: [Figure]
Revised: + D i s c

Original: D
Revised: h

Revised: a r g

Original: [Figure]
Revised: [Figure]

Original: c h a r g
Revised: E C G

Original: [Figure]
Revised: [Figure]

Original: C G
Revised: P h y s i c i a n

Original: [Figure]
Revised: [Figure]

Original: n
Revised: s

Original: [Figure]
Revised: [Figure]

Original: B.2 Decreasing Frequency
Revised: B.2 Decreasing Frequency

Original: [Figure]

Original: We also provide a similar ablation study on note types by decreasing frequency, as a sanity check, in
Revised: We also provide an ablation study on note types by decreasing frequency, as a sanity check, in

Original: Figure 5 and Table 5.
Revised: Figure 5 and Table 5.

Original: We draw the same conclusions on note types importance.
Revised: We draw the same conclusions on note types importance.

Original: [Figure]
Revised: [Figure]

Original:  e
Revised: C U + N u r s e

Original: [Figure] d
Revised: + R a d

Original: [Figure]
Revised: i

Original: o
Revised: o

Original: [Figure]
Revised: l

Original: o g y + P
Revised: o g y + P

Original: h
Revised: h

Original: y s i
Revised: y s i

Original: c i a n + E C G
Revised: c i a n + E C G

Original: [Figure]
Revised: + D i s c

Original: h
Revised: h

Original: [Figure] g e 
Revised: a r g e + E c h

Original: [Figure]
Revised: [Figure]

Original: h
Revised: t

Original: [Figure]
Revised: [Figure]

Original: y
Revised: y

Original: [Figure]
Revised: [Figure]

Revised: I C U + N u r s e + R a d i o l o g y

Original:  P[Figure]
Revised: + P

Original: i
Revised: h

Original: [Figure]

Original:  ia n
Revised: y s i

Original: [Figure] E C G
Revised: c i a n + E C G

Original: [Figure]
Revised: + D

Original: D i s c
Revised: i s c

Original: h
Revised: h

Original: a r g e + E
Revised: a r g e + E

Original: c
Revised: c

Original: h
Revised: h

Original: o + R
Revised: o + R

Original: e s p
Revised: e s p

Original: i r a 
Revised: i r a t o r y + O t h e

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: C Interpretability results
Revised: C Interpretability results

Original: In this section
Revised: In this section

Original: [Table]
Revised: [Table]

Original: we describe our attempt to analyze notes importance with a finer grain throughattention rollout and chunk level representations.
Revised: we describe our attempt to analyze notes importance more in-depth through attention rollout and chunk level representations.

Original: We also provide a similar analysis for anotherillustrative example.
Revised: We also provide a similar analysis for another illustrative example.

Original: C.1 Deeper analysis on main manuscript example
Revised: C.1 Deeper analysis on main manuscript example

Original: In the example from the main manuscript, we found that the 7th note was strongly attended to in
Revised: In the example from the main manuscript, we found that the 7th note was strongly attended to in

Original: Figure 3.
Revised: Figure 3.

Original: To further understand, what lead this note to significantly change the model predictiontoward a imminent death, we ran attention rollout [31] and summed over the words [27] (see Figure 6).
Revised: To further understand, what lead this note to significantly change the model prediction toward a imminent death, we ran attention rollout [31] and summed over the words [27] (see Figure 6).

Original: Additionally, we normalized by the number of words in each chunk when a note does not fit a single
Revised: Additionally, we normalized by the number of words in each chunk when a note does not fit a single

Original: Clinical BERT pass (i.e., it is longer than 128 tokens).
Revised: Clinical BERT pass (i.e., it is longer than 128 tokens).

Original: One interesting observationto make, is howthe dnrword is significant in the overall note representation.
Revised: One interesting observation is how dnr is significant in the overall note representation.

Original: Indeed this stands for “do no resucitate”,indicating a clear change of treatment in case of cardiac or respiratory arrest and thus a more probabledeath.nursing / other nursing progress note 0700 - 1900 ( continued ) plan : goal cvp > 10 , u / o > 30cc .
Revised: Indeed this stands for “do no resucitate”, indicating a clear change of treatment in case of cardiac or respiratory arrest and thus a more probable death. nursing / other nursing progress note 0700 - 1900 ( continued ) plan : goal cvp > 10 , u / o > 30cc .

Original: hr , micu team wanting to give lr boluses for low u / o or low cvp .
Revised: hr , micu team wanting to give lr boluses for low u / o or low cvp .

Original: monitor bp , titrate gtt ’ s accordingly . ?
Revised: monitor bp , titrate gtt ’ s accordingly . ?

Original: change aline over wire later tonoc .
Revised: change aline over wire later tonoc .

Original: follow ph closely , may need bicarb gtt restarted .
Revised: follow ph closely , may need bicarb gtt restarted .

Original: pt is [ * * name ( ni ) 2420 * * ] confirmed w / family today - in case of cardiac failure , please allow natural death .
Revised: pt is [ * * name ( ni ) 2420 * * ] confirmed w / family today - in case of cardiac failure , please allow natural death .

Original: discussed do ##butamine , micu team to decide .
Revised: discussed do ##butamine , micu team to decide .

Original: follow cardiac enzymes , may want to heparinize if gib resolves .
Revised: follow cardiac enzymes , may want to heparinize if gib resolves .

Original: continue supportive measures , follow trends and repeat family meeting tomorrow to possibly make cmo if no significant improvement overnoc .
Revised: continue supportive measures , follow trends and repeat family meeting tomorrow to possibly make cmo if no significant improvement overnoc .

Original: dnr .
Revised: dnr .

Original: Chunk level representation of notes.
Revised: Chunk level representation of notes.

Original: In our model, if a note is longer than 128 tokens on which
Revised: In our model, if a note is longer than 128 tokens on which

Original: Clinical BERT was trained, we passed the chunks separately through Clinical BERT and averagedthe global token embedding.
Revised: Clinical BERT was trained, we passed the chunks separately through Clinical BERT and averaged the global token embedding.

Original: For an additional level of interpretability, we use the same modelarchitecture and train using all embedded chunks.
Revised: For an additional level of interpretability, we use the same model architecture and train using all embedded chunks of the same note with the same time appended.

Original: For the same patient, we see that chunk 12th isattended early on, and  25th and 27th chunks are the strongest attended to when the prediction gapincreases.
Revised: For the identical patient, we see that chunk 12 is attended early on, and the 25th and 27th chunks are the strongest attended to when the prediction gap increases.

Original: Figure 8 shows the attention rollout results on the chunks separately.
Revised: Figure 8 shows the attention rollout results on the chunks separately.

Original: Once again in themost attended chunk (see Figure 8c), we see the nurses mentions to “please allow natural death“.
Revised: Once again in the most attended chunk (see Figure 8c), we see the nurses mentions to “please allow natural death“.

Original: C.2 Other Illustrative Example
Revised: C.2 Other Illustrative Example

Original: To further illustrate our conclusions, we also present a second example corresponding to the patientwith ID 12767.
Revised: To further illustrate our conclusions, we also present a second example corresponding to the patient with ID 12767.

Original: Figure 9 shows the predictions and the corresponding cross-attention from ICU
Revised: Figure 9 shows the predictions and the corresponding cross-attention from ICU

Original: 6    60 Timestep (hours) 0.
Revised: 6    60 Timestep (hours) 0.

Original: P r o b a b ili t y 66 8 1012141618202224262830323436384042444648505254565860626466Timestep (hours)

Revised: P r o b a b ili t y 66 8 1012141618202224262830323436384042444648505254565860626466Timestep (hours)

Original: N o t e c hu n k was no insulin gtt x 1 / 2 hr this am .
Revised: N o t e c hu n k was no insulin gtt x 1 / 2 hr this am .

Original: fsbs increased this pm , and ?
Revised: fsbs increased this pm , and ?

Original: restart of insulin gtt .
Revised: restart of insulin gtt .

Original: soc ) pt ’ s family at bedside and has been updated throughout the day .
Revised: soc ) pt ’ s family at bedside and has been updated throughout the day .

Original: pt was made dnr as of 18 : 00 , per pt ’ s hcp [ * * name ( ni ) * * ] [ * * name ( ni ) * * ] ) .
Revised: pt was made dnr as of 18 : 00 , per pt ’ s hcp [ * * name ( ni ) * * ] [ * * name ( ni ) * * ] ) .

Original: [Figure]
Revised: [Figure]

Original: ##ative skin care done , frequent turns .
Revised: ##ative skin care done , frequent turns .

Original: access : r ij , l sc central lines in place , patent .
Revised: access : r ij , l sc central lines in place , patent .

Original: l radial aline not functioning .
Revised: l radial aline not functioning .

Original: micu team aware .
Revised: micu team aware .

Original: social : son , daughter and in laws here for family meeting todday w / micu team .
Revised: social : son , daughter and in laws here for family meeting todday w / micu team .

Original: discussed very poor prognosis and likely long complicated recovery if he does survive this episode of severe sepsis and cardiogenci compromise .
Revised: discussed very poor prognosis and likely long complicated recovery if he does survive this episode of severe sepsis and cardiogenci compromise .

Original: plan is to give pt 24hrs to follow labs and trends .
Revised: plan is to give pt 24hrs to follow labs and trends .

Original: will re - meet tomorrow at 3 or 4 to discuss goals of care .
Revised: will re - meet tomorrow at 3 or 4 to discuss goals of care .

Original: family clearly expressed that pt would not want trac
Revised: family clearly expressed that pt would not want trac

Original: [Figure]
Revised: [Figure]

Original: nursing / other nursing progress note 0700 - 1900 ( continued ) plan : goal cvp > 10 , u / o > 30cc .
Revised: nursing / other nursing progress note 0700 - 1900 ( continued ) plan : goal cvp > 10 , u / o > 30cc .

Original: hr , micu team wanting to give lr boluses for low u / o or low cvp .
Revised: hr , micu team wanting to give lr boluses for low u / o or low cvp .

Original: monitor bp , titrate gtt ’ s accordingly . ?
Revised: monitor bp , titrate gtt ’ s accordingly . ?

Original: change aline over wire later tonoc .
Revised: change aline over wire later tonoc .

Original: follow ph closely , may need bicarb gtt restarted .
Revised: follow ph closely , may need bicarb gtt restarted .

Original: pt is [ * * name ( ni ) 2420 * * ] confirmed w / family today - in case of cardiac failure , please allow natural death .
Revised: pt is [ * * name ( ni ) 2420 * * ] confirmed w / family today - in case of cardiac failure , please allow natural death .

Original: discussed do
Revised: discussed do

Original: [Figure]
Revised: [Figure]

Original: time series to text series over time.
Revised: time series to text series over time.

Original: We observe that at the 63rd timestep when the multi-modaltransformer’s predictionagain deviates from the EHR-only transformer’s predictionwhen it attendsto the newly available 7th note.
Revised: We observe that at the 63rd timestep when the multi-modal transformer’s prediction deviates from the EHR-only transformer’s prediction it attends to the newly available 8th note.

Original: As in the previous example, we compute the attention rollout from this particular note in Figure 9.
Revised: As in the previous example, we compute the attention rollout from this particular note in Figure 9.

Original: This short note states that the patient is most likely to be made DNR/DNI.
Revised: This short note states that the patient is most likely to be made DNR/DNI.

Original: We see that besides the
Revised: We see that besides the

Original: DNR status, attention is seemingly strongly placed also on unresponsive and tylenol .
Revised: DNR status, attention is seemingly strongly placed also on unresponsive and tylenol .

Original: Interestingly,we observe that the last note is highly attended to.
Revised: Interestingly, we observe that the last note is highly attended to.

Original: Indeed, we see that the note already discusses thepatient’s death, highlighting the existing leakage of information.
Revised: Indeed, we see that the note already discusses the patient’s death, highlighting the existing leakage of information.

Original: Hence, it is clear why the model isabove 90% certain that the patient will die within the next 24 hours.
Revised: Hence, it is clear why the model is above 90% certain that the patient will die within the next 24 hours.

Original: We show this last note attentionrollout in Figure 11.
Revised: We show this last note attention rollout in Figure 11.

Original: Interestingly, not necessarily expired is focused in contrast to mortem .
Revised: Interestingly, not necessarily expired is focused in contrast to mortem .

Original: Webelieve this is because the former is a clinical term, thus, more frequent in the corpus used to trainclinical BERT.
Revised: We believe this is because the former is a clinical term, thus, more frequent in the corpus used to train clinical BERT.

Original: [Figure]
Revised: [Figure]

Original: N o t e nursing / other nursing progress notes : pt remains [ * * name ( ni ) * * ] / vented 40 % 5ps and 5peep .
Revised: N o t e nursing / other nursing progress notes : pt remains [ * * name ( ni ) * * ] / vented 40 % 5ps and 5peep .

Original: awaiting arrival of one more son to add in decision process of code status and extubation .
Revised: awaiting arrival of one more son to add in decision process of code status and extubation .

Original: lung sounds remain clear , sx for minimal amt ’ s of thick tan secretions .
Revised: lung sounds remain clear , sx for minimal amt ’ s of thick tan secretions .

Original: 02 sat ’ s cont in high 90 ’ s .
Revised: 02 sat ’ s cont in high 90 ’ s .

Original: cv : hr 80 ’ s to 90 ’ s nsr no ectopy .
Revised: cv : hr 80 ’ s to 90 ’ s nsr no ectopy .

Original: bp stable with lopressor see careview for data .
Revised: bp stable with lopressor see careview for data .

Original: temp max 101 . tylenol given .
Revised: temp max 101 . tylenol given .

Original: gi : foley patent draining mod amt ’ s of clear yellow urine .
Revised: gi : foley patent draining mod amt ’ s of clear yellow urine .

Original: gu : ngt clamped .
Revised: gu : ngt clamped .

Original: npo .
Revised: npo .

Original: no stool .
Revised: no stool .

Original: abd soft distended , hypoactive bowel sounds .
Revised: abd soft distended , hypoactive bowel sounds .

Original: neuro :
Revised: neuro :

Original: no chg in neuro status .
Revised: no chg in neuro status .

Original: cont to be unresponsive , does not open eyes .
Revised: cont to be unresponsive , does not open eyes .

Original: moving right arm and right leg , no movement on left side .
Revised: moving right arm and right leg , no movement on left side .

Original: pupils 2mm react briskly .
Revised: pupils 2mm react briskly .

Original: iv fluid cont at 40 / hr .
Revised: iv fluid cont at 40 / hr .

Original: pt waiting till morning to most likely be made dnr / di .
Revised: pt waiting till morning to most likely be made dnr / di .

Original: As for the previous examples, we also explore the chunks of the notes directly.
Revised: As for the previous examples, we also explore the chunks of the notes directly.

Original: Figure 12 showsthe ICU to nurse note chunks cross-attention over time.
Revised: Figure 12 shows the ICU to nurse note chunks cross-attention over time.

Original: For this patient  8th, 17th, 23rd, 25th, and33rd are strongly attended to over time.
Revised: For this patient the 8th, 17th, 23rd, 25th, and 33rd chunks are strongly attended to over time.

Original: In Figure 13 we present the corresponding attention rolloutresults.
Revised: In Figure 13 we present the corresponding attention rollout results.

Original: In chunk 7, we find medically relevant observations like obtundation or decerebrate likeposturing attended to.
Revised: In chunk 7, we find medically relevant observations like obtundation or decerebrate like posturing attended to.

Original: Obtundation describes the status when the patient has a reduced level ofconsciousness and alertness.
Revised: Obtundation describes the status when the patient has a reduced level of consciousness and alertness.

Original: Decerebrate posture is a body pose indicating severe brain damage.
Revised: Decerebrate posture is a body pose indicating severe brain damage.

Original: Inthe other chunks, the DNR status is again highlighted.
Revised: In the other chunks, the DNR status is again highlighted.

Original: Though, the cross-modal network actuallydoe not have a higher likelihood of death for this patient at the time of this note.
Revised: Though, the cross-modal network actually does not have a higher likelihood of death for this patient at the time of this note.

Original: Hence, it might nothave been able to learn about such medical knowledge yet.
Revised: Hence, it might not have been able to learn about such medical knowledge yet.

Original: Interestingly the last note chunk stronglyindicating that death is near shows attention on the namepatterns and not the patient being placed oncomfort measures and that they were extubated (cf.
Revised: Interestingly the last note chunk, which strongly indicates that death is near, shows attention on the namepatterns and not the patient being placed on comfort measures and that they were extubated (cf.

Original: Figure 13e).
Revised: Figure 13e).

Original: Since we did not train end-to-enddue to resource constraints, it is possible that the model was unable to capture the expected humanlyrelevant rationales correctly and represented this observation differently instead.
Revised: Since we did not train end-to-end due to resource constraints, it is possible that the model was unable to capture the expected humanly relevant rationales correctly and represented this observation differently instead.

Original: nursing / other pt expired mr [ * * known lastname 5307 * * ] had been made cmo yesterday .
Revised: nursing / other pt expired mr [ * * known lastname 5307 * * ] had been made cmo yesterday .

Original: comfort maintained today using morphine .
Revised: comfort maintained today using morphine .

Original: pt repositioned .
Revised: pt repositioned .

Original: vs monitored and family updated q2 - 3 hours on pt condition .
Revised: vs monitored and family updated q2 - 3 hours on pt condition .

Original: their desire was to be updated if there was a change .
Revised: their desire was to be updated if there was a change .

Original: rn shaving patient at 1615 and his hr dropped from 115 to the 60 ’ s .
Revised: rn shaving patient at 1615 and his hr dropped from 115 to the 60 ’ s .

Original: and rr decr to 9 from rate of 30 .
Revised: and rr decr to 9 from rate of 30 .

Original: family called immediately .
Revised: family called immediately .

Original: [ * * first name11 ( name pattern1 ) 2294 * * ] [ * * initials ( namepattern4 ) * * ] [ * * last name ( namepattern4 ) 2295 * * ] , rn spoke with [ * * name ( ni ) 2489 * * ] [ * * name ( ni ) 5307 * * ] , pt son and told him the family should come in .
Revised: [ * * first name11 ( name pattern1 ) 2294 * * ] [ * * initials ( namepattern4 ) * * ] [ * * last name ( namepattern4 ) 2295 * * ] , rn spoke with [ * * name ( ni ) 2489 * * ] [ * * name ( ni ) 5307 * * ] , pt son and told him the family should come in .

Original: bp droppped to 72 / 20 , hr and rr continued to decr .
Revised: bp droppped to 72 / 20 , hr and rr continued to decr .

Original: rn called son again and told him that they would not make in in time , that pt was dying and rn would stay with him in the room .
Revised: rn called son again and told him that they would not make in in time , that pt was dying and rn would stay with him in the room .

Original: pt expired at##5 with nurse [ * * first name ( titles ) * * ] [ * * last name ( titles ) 64 * * ] .
Revised: pt expired at##5 with nurse [ * * first name ( titles ) * * ] [ * * last name ( titles ) 64 * * ] .

Original: family to arrive within an hour .
Revised: family to arrive within an hour .

Original: neurosurg resident called .
Revised: neurosurg resident called .

Original: post - mortem care to be done and pt will be transported to morgue after family has arrived .
Revised: post - mortem care to be done and pt will be transported to morgue after family has arrived .

Original: [Figure]
Revised: [Figure]

Original: nursing / other night note : micub neuro : unchanged through night .
Revised: nursing / other night note : micub neuro : unchanged through night .

Original: responds to voice intermittently .
Revised: responds to voice intermittently .

Original: able to hold one thumb up on command w / r hand , wiggle toes sl .
Revised: able to hold one thumb up on command w / r hand , wiggle toes sl .

Original: on r .
Revised: on r .

Original: very weak .
Revised: very weak .

Original: no movement on left side .
Revised: no movement on left side .

Original: neuro and neuro sicu in to eval pt .
Revised: neuro and neuro sicu in to eval pt .

Original: pupils [ * * 1 - 21 * * ] and sluggish .
Revised: pupils [ * * 1 - 21 * * ] and sluggish .

Original: periods of deep obtundation to more wake , almost decerebrate like posturing on r , md ’ s in while this was occuring .
Revised: periods of deep obtundation to more wake , almost decerebrate like posturing on r , md ’ s in while this was occuring .

Original: plan for f / u ct scan today
Revised: plan for f / u ct scan today

Original: [Figure]
Revised: [Figure]

Original: doctors .
Revised: doctors .

Original: son in particular seemed upset and eager for info .
Revised: son in particular seemed upset and eager for info .

Original: neuro
Revised: neuro

Original: [ * * doctor first name * * ] team member spoke with family .
Revised: [ * * doctor first name * * ] team member spoke with family .

Original: a - no real change in neuro exam , stable post parietal bleed .
Revised: a - no real change in neuro exam , stable post parietal bleed .

Original: p - will wean nipride as we can once lopressor kicks in , goal bp 150 / .
Revised: p - will wean nipride as we can once lopressor kicks in , goal bp 150 / .

Original: for now will leave intubated until neuro exam brightens .
Revised: for now will leave intubated until neuro exam brightens .

Original: check culture results .
Revised: check culture results .

Original: watch i ’ s and o ’ s , labs . ?
Revised: watch i ’ s and o ’ s , labs . ?

Original: can start tube feeds in am if still intubated .
Revised: can start tube feeds in am if still intubated .

Original: monitor neuro exam for changes .
Revised: monitor neuro exam for changes .

Original: keep family advised as to all
Revised: keep family advised as to all

Original: [Figure]
Revised: [Figure]

Original: gi : foley patent draining mod amt ’ s of clear yellow urine .
Revised: gi : foley patent draining mod amt ’ s of clear yellow urine .

Original: gu : ngt clamped .
Revised: gu : ngt clamped .

Original: npo .
Revised: npo .

Original: no stool .
Revised: no stool .

Original: abd soft distended , hypoactive bowel sounds .
Revised: abd soft distended , hypoactive bowel sounds .

Original: neuro :
Revised: neuro :

Original: no chg in neuro status .
Revised: no chg in neuro status .

Original: cont to be unresponsive , does not open eyes .
Revised: cont to be unresponsive , does not open eyes .

Original: moving right arm and right leg , no movement on left side .
Revised: moving right arm and right leg , no movement on left side .

Original: pupils 2mm react briskly .
Revised: pupils 2mm react briskly .

Original: iv fluid cont at 40 / hr .
Revised: iv fluid cont at 40 / hr .

Original: pt waiting till morning to most likely be made dnr / di .
Revised: pt waiting till morning to most likely be made dnr / di .

Original: [Figure]
Revised: [Figure]

Original: be the furthest rehab he would get to , maybe recognizing family , chair , flacid on left .
Revised: be the furthest rehab he would get to , maybe recognizing family , chair , flacid on left .

Original: wife , son , and daughter want to uphold wishes of pt which is not to have non quality life , as his mother did for 6 yrs in a nsg home , s / p stroke .
Revised: wife , son , and daughter want to uphold wishes of pt which is not to have non quality life , as his mother did for 6 yrs in a nsg home , s / p stroke .

Original: dnr order obtained .
Revised: dnr order obtained .

Original: family waiting for last daughter to arrive from [ * * name ( ni ) 794 * * ] late tonight .
Revised: family waiting for last daughter to arrive from [ * * name ( ni ) 794 * * ] late tonight .

Original: family wishes to give pt .
Revised: family wishes to give pt .

Original: a few days to see if any improvements occur .
Revised: a few days to see if any improvements occur .

Original: dr .
Revised: dr .

Original: [Figure]
Revised: [Figure]

Original: name ( stitle )
Revised: name ( stitle )

Original: [Figure]
Revised: [Figure]

Original: nursing / other nursing note 0700 - 1500 ; neuro ; exam slightly worse per neuro , family meeting held with [ * * first name8 ( namepattern ) * * ] [ * * last name ( namepattern1 ) * * ] md sicu team , [ * * first name4 ( namepattern1 ) * * ] [ * * last name ( namepattern1 ) * * ] social worker .
Revised: nursing / other nursing note 0700 - 1500 ; neuro ; exam slightly worse per neuro , family meeting held with [ * * first name8 ( namepattern ) * * ] [ * * last name ( namepattern1 ) * * ] md sicu team , [ * * first name4 ( namepattern1 ) * * ] [ * * last name ( namepattern1 ) * * ] social worker .

Original: and family in agreement with patients own wishes decided to place patient on comfort measures only .
Revised: and family in agreement with patients own wishes decided to place patient on comfort measures only .

Original: he was extubated to r / a at 12 15 and has
Revised: he was extubated to r / a at 12 15 and has

Original: [Figure]
Revised: [Figure]