The Quality Application of Deep Learning in Clinical Outcome Predictions Using Electronic Health Record Data: A Systematic Review

Introduction: Electronic Health Record (EHR) is a significant source of medical data that can be used to develop predictive modelling with therapeutically useful outcomes. Predictive modelling using EHR data has been increasingly utilized in healthcare, achieving outstanding performance and improving healthcare outcomes. Objectives: The main goal of this review study is to examine different deep learning approaches and techniques used to EHR data processing. Methods: To find possibly pertinent articles that have used deep learning on EHR data, the PubMed database was searched. Using EHR data, we assessed and summarized deep learning performance in a number of clinical applications that focus on making specific predictions about clinical outcomes, and we compared the outcomes with those of conventional machine learning models. Results: For this study, a total of 57 papers were chosen. There have been five identified clinical outcome predictions: illness (n=33), intervention (n=6), mortality (n=5), Hospital readmission (n=7), and duration of stay (n=1). The majority of research (39 out of 57) used structured EHR data. RNNs were used as deep learning models the most frequently (LSTM: 17 studies, GRU: 6 research). The analysis shows that deep learning models have excelled when applied to a variety of clinical outcome predictions. While deep learning's application to EHR data has advanced rapidly, it's crucial that these models remain reliable, offering critical insights to assist clinicians in making informed decision. Conclusions: The findings demonstrate that deep learning can outperform classic machine learning techniques since it has the advantage of utilizing extensive and sophisticated datasets, such as longitudinal data seen in EHR. We think that deep learning will keep expanding because it has been quite successful in enhancing healthcare outcomes utilizing EHR data

Artificial Intelligence for Emerging Technology in Surgery: Systematic Review and Validation

Surgery is a high-risk procedure of therapy and is associated to post trauma complications of longer hospital stay, estimated blood loss and long duration of surgeries. Reports have suggested that over 2.5% patients die during and post operation. This paper is aimed at systematic review of previous research on artificial intelligence (AI) in surgery, analyzing their results with suitable software to validate their research by obtaining same or contrary results. Six published research articles have been reviewed across three continents. These articles have been re-validated using software including SPSS and MedCalc to obtain the statistical features such as the mean, standard deviation, significant level, and standard error. From the significant values, the experiments are then classified according to the null (p0.05) hypotheses. The results obtained from the analysis have suggested significant difference in operating time, docking time, staging time, and estimated blood loss but show no significant difference in length of hospital stay, recovery time and lymph nodes harvested between robotic assisted surgery using AI and normal conventional surgery. From the evaluations, this research suggests that AI-assisted surgery improves over the conventional surgery as safer and more efficient system of surgery with minimal or no complications

Music and musicality in brain surgery:The effect on delirium and language

Delirium is a neuropsychiatric clinical syndrome with overlapping symptoms withthe neurologic primary disease. This is why delirium is such a difficult and underexposedtopic in neurosurgical literature. Delirium is a complication which mightaffect recovery after brain surgery, hence we describe in Chapter 2 a systematicreview which focuses on how delirium is defined in the neurosurgical literature.We included twenty-four studies (5589 patients) and found no validation studiesof screening instruments in neurosurgical papers. Delirium screening instruments,validated in other cohorts, were used in 70% of the studies, consisting of theConfusion Assessment Method (- Intensive Care Unit) (45%), Delirium ObservationScreening Scale (5%), Intensive Care Delirium Screening Checklist (10%), Neelonand Champagne Confusion Scale (5%), and Nursing Delirium Screening Scale (5%).Incidence of post-operative delirium after intracranial surgery was 19%, ranging from12 – 26% caused by variation in clinical features and delirium assessment methods.Our review highlighted the need of future research on delirium in neurosurgery,which should focus on optimizing diagnosis, and assessing prognostic significanceand management.It is unclear what the impact of delirium is on the recovery after brain surgery,as delirium is often a self-limiting and temporary complication. In Chapter 3 wetherefore investigated the impact of delirium, by means of incidence and healthoutcomes, and identified independent risk factors by including 2901 intracranialsurgical procedures. We found that delirium was present in 19.4% with an averageonset (mean/SD) within 2.62/1.22 days and associated with more Intensive CareUnit (ICU) admissions and more discharge towards residential care. These numbersconfirm the impact of delirium with its incidence rates, which were in line with ourprevious systematic review, and significant health-related outcomes. We identifiedseveral independent non-modifiable risk factors such as age, pre-existing memoryproblems, emergency operations, and modifiable risk factors such as low preoperativepotassium and opioid and dexamethasone administration, which shed lighton the pathophysiologic mechanisms of POD in this cohort and could be targetedfor future intervention studies.10As listening to recorded music has been proven to lower delirium-eliciting factors inthe surgical population, such as pain, we were interested in the size of analgesic effectand its underlying mechanism before applying this into our clinical setting. In Chapter4 we describe the results of a two-armed experimental randomized controlled trial inwhich 70 participants received increasing electric stimuli through their non-dominantindex finger. This study was conducted within a unique pain model as participantswere blinded for the outcome. Participants in the music group received a 20-minutemusic intervention and participants in the control group a 20-minute resting period.Although the effect of the music intervention on pain endurance was not statisticallysignificant in our intention-to-treat analysis (p = 0.482, CI -0.85; 1.79), the subgroupanalyses revealed an increase in pain endurance in the music group after correcting fortechnical uncertainties (p = 0.013, CI 0.35; 2.85). This effect on pain endurance couldbe attributed to increased parasympathetic activation, as an increased Heart RateVariability (HRV) was observed in the music vs. the control group (p=0.008;0.032).As our prior chapters increased our knowledge on the significance of delirium on thepost-operative recovery after brain surgery and the possible beneficial effects of music,we decided to design a randomized controlled trial. In Chapter 5 we describe theprotocol and in Chapter 6 we describe the results of this single-centered randomizedcontrolled trial. In this trial we included 189 patients undergoing craniotomy andcompared the effects of music administered before, during and after craniotomy withstandard of clinical care. The primary endpoint delirium was assessed by the deliriumobservation screening scale (DOSS) and confirmed by a psychiatrist accordingto DSM-5 criteria. A variety of secondary outcomes were assessed to substantiatethe effects of music on delirium and its clinical implications. Our results supportthe efficacy of music in preventing delirium after craniotomy, as found with DOSS(OR:0.49, p=0.048) but not after DSM-5 confirmation (OR:0.47, p=0.342). Thispossible beneficial effect is substantiated by the effect of music on pre-operativeautonomic tone, measured with HRV (p=0.021;0.025), and depth of anesthesia(p=&lt;0.001;0.022). Our results fit well within the current literature and support theimplementation of music for the prevention of delirium within the neurosurgicalpopulation. However, delirium screening tools should be validated and the long-termimplications should be evaluated after craniotomy to assess the true impact of musicafter brain surgery.Musicality and language in awake brain surgeryIn the second part of this thesis, the focus swifts towards maintaining musicality andlanguage functions around awake craniotomy. Intra-operative mapping of languagedoes not ensure complete maintenance which mostly deteriorates after tumor resection.Most patients recover to their baseline whereas other remain to suffer from aphasiaaffecting their quality of life. The level of musical training might affect the speed andextend of postoperative language recovery, as increased white matter connectivity inthe corpus callosum is described in musicians compared to non-musicians. Hence,in Chapter 7 we evaluate the effect of musicality on language recovery after awakeglioma surgery in a cohort study of forty-six patients. We divided the patients intothree groups based on the musicality and compared the language scores between thesegroups. With the first study on this topic, we support that musicality protects againstlanguage decline after awake glioma surgery, as a trend towards less deterioration oflanguage was observed within the first three months on the phonological domain (p= 0.04). This seemed plausible as phonology shares a common hierarchical structurebetween language and singing. Moreover, our results support the hypothesis ofmusicality induced contralateral compensation in the (sub-) acute phase through thecorpus callosum as the largest difference of size was found in the anterior corpuscallosum in non- musicians compared to trained musicians (p = 0.02).In Chapter 8 we addressed musicality as a sole brain function and whether it canbe protected during awake craniotomy in a systematic review consisting of tenstudies and fourteen patients. Isolated music disruption, defined as disruption duringmusic tasks with intact language/speech and/or motor functions, was identified intwo patients in the right superior temporal gyrus, one patient in the right and onepatient in the left middle frontal gyrus and one patient in the left medial temporalgyrus. Pre-operative functional MRI confirmed these localizations in three patients.Assessment of post-operative musical function, only conducted in seven patients bymeans of standardized (57%) and non-standardized (43%) tools, report no loss ofmusical function. With these results we concluded that mapping music is feasibleduring awake craniotomy. Moreover, we identified certain brain regions relevant formusic production and detected no decline during follow-up, suggesting an addedvalue of mapping musicality during awake craniotomy. A systematic approach to mapmusicality should be implemented, to improve current knowledge on the added valueof mapping musicality during awake craniotomy.<br/

Music and musicality in brain surgery:The effect on delirium and language

Delirium is a neuropsychiatric clinical syndrome with overlapping symptoms withthe neurologic primary disease. This is why delirium is such a difficult and underexposedtopic in neurosurgical literature. Delirium is a complication which mightaffect recovery after brain surgery, hence we describe in Chapter 2 a systematicreview which focuses on how delirium is defined in the neurosurgical literature.We included twenty-four studies (5589 patients) and found no validation studiesof screening instruments in neurosurgical papers. Delirium screening instruments,validated in other cohorts, were used in 70% of the studies, consisting of theConfusion Assessment Method (- Intensive Care Unit) (45%), Delirium ObservationScreening Scale (5%), Intensive Care Delirium Screening Checklist (10%), Neelonand Champagne Confusion Scale (5%), and Nursing Delirium Screening Scale (5%).Incidence of post-operative delirium after intracranial surgery was 19%, ranging from12 – 26% caused by variation in clinical features and delirium assessment methods.Our review highlighted the need of future research on delirium in neurosurgery,which should focus on optimizing diagnosis, and assessing prognostic significanceand management.It is unclear what the impact of delirium is on the recovery after brain surgery,as delirium is often a self-limiting and temporary complication. In Chapter 3 wetherefore investigated the impact of delirium, by means of incidence and healthoutcomes, and identified independent risk factors by including 2901 intracranialsurgical procedures. We found that delirium was present in 19.4% with an averageonset (mean/SD) within 2.62/1.22 days and associated with more Intensive CareUnit (ICU) admissions and more discharge towards residential care. These numbersconfirm the impact of delirium with its incidence rates, which were in line with ourprevious systematic review, and significant health-related outcomes. We identifiedseveral independent non-modifiable risk factors such as age, pre-existing memoryproblems, emergency operations, and modifiable risk factors such as low preoperativepotassium and opioid and dexamethasone administration, which shed lighton the pathophysiologic mechanisms of POD in this cohort and could be targetedfor future intervention studies.10As listening to recorded music has been proven to lower delirium-eliciting factors inthe surgical population, such as pain, we were interested in the size of analgesic effectand its underlying mechanism before applying this into our clinical setting. In Chapter4 we describe the results of a two-armed experimental randomized controlled trial inwhich 70 participants received increasing electric stimuli through their non-dominantindex finger. This study was conducted within a unique pain model as participantswere blinded for the outcome. Participants in the music group received a 20-minutemusic intervention and participants in the control group a 20-minute resting period.Although the effect of the music intervention on pain endurance was not statisticallysignificant in our intention-to-treat analysis (p = 0.482, CI -0.85; 1.79), the subgroupanalyses revealed an increase in pain endurance in the music group after correcting fortechnical uncertainties (p = 0.013, CI 0.35; 2.85). This effect on pain endurance couldbe attributed to increased parasympathetic activation, as an increased Heart RateVariability (HRV) was observed in the music vs. the control group (p=0.008;0.032).As our prior chapters increased our knowledge on the significance of delirium on thepost-operative recovery after brain surgery and the possible beneficial effects of music,we decided to design a randomized controlled trial. In Chapter 5 we describe theprotocol and in Chapter 6 we describe the results of this single-centered randomizedcontrolled trial. In this trial we included 189 patients undergoing craniotomy andcompared the effects of music administered before, during and after craniotomy withstandard of clinical care. The primary endpoint delirium was assessed by the deliriumobservation screening scale (DOSS) and confirmed by a psychiatrist accordingto DSM-5 criteria. A variety of secondary outcomes were assessed to substantiatethe effects of music on delirium and its clinical implications. Our results supportthe efficacy of music in preventing delirium after craniotomy, as found with DOSS(OR:0.49, p=0.048) but not after DSM-5 confirmation (OR:0.47, p=0.342). Thispossible beneficial effect is substantiated by the effect of music on pre-operativeautonomic tone, measured with HRV (p=0.021;0.025), and depth of anesthesia(p=&lt;0.001;0.022). Our results fit well within the current literature and support theimplementation of music for the prevention of delirium within the neurosurgicalpopulation. However, delirium screening tools should be validated and the long-termimplications should be evaluated after craniotomy to assess the true impact of musicafter brain surgery.Musicality and language in awake brain surgeryIn the second part of this thesis, the focus swifts towards maintaining musicality andlanguage functions around awake craniotomy. Intra-operative mapping of languagedoes not ensure complete maintenance which mostly deteriorates after tumor resection.Most patients recover to their baseline whereas other remain to suffer from aphasiaaffecting their quality of life. The level of musical training might affect the speed andextend of postoperative language recovery, as increased white matter connectivity inthe corpus callosum is described in musicians compared to non-musicians. Hence,in Chapter 7 we evaluate the effect of musicality on language recovery after awakeglioma surgery in a cohort study of forty-six patients. We divided the patients intothree groups based on the musicality and compared the language scores between thesegroups. With the first study on this topic, we support that musicality protects againstlanguage decline after awake glioma surgery, as a trend towards less deterioration oflanguage was observed within the first three months on the phonological domain (p= 0.04). This seemed plausible as phonology shares a common hierarchical structurebetween language and singing. Moreover, our results support the hypothesis ofmusicality induced contralateral compensation in the (sub-) acute phase through thecorpus callosum as the largest difference of size was found in the anterior corpuscallosum in non- musicians compared to trained musicians (p = 0.02).In Chapter 8 we addressed musicality as a sole brain function and whether it canbe protected during awake craniotomy in a systematic review consisting of tenstudies and fourteen patients. Isolated music disruption, defined as disruption duringmusic tasks with intact language/speech and/or motor functions, was identified intwo patients in the right superior temporal gyrus, one patient in the right and onepatient in the left middle frontal gyrus and one patient in the left medial temporalgyrus. Pre-operative functional MRI confirmed these localizations in three patients.Assessment of post-operative musical function, only conducted in seven patients bymeans of standardized (57%) and non-standardized (43%) tools, report no loss ofmusical function. With these results we concluded that mapping music is feasibleduring awake craniotomy. Moreover, we identified certain brain regions relevant formusic production and detected no decline during follow-up, suggesting an addedvalue of mapping musicality during awake craniotomy. A systematic approach to mapmusicality should be implemented, to improve current knowledge on the added valueof mapping musicality during awake craniotomy.<br/

An explainable machine learning framework for lung cancer hospital length of stay prediction

This work introduces a predictive Length of Stay (LOS) framework for lung cancer patients using machine learning (ML) models. The framework proposed to deal with imbalanced datasets for classification-based approaches using electronic healthcare records (EHR). We have utilized supervised ML methods to predict lung cancer inpatients LOS during ICU hospitalization using the MIMIC-III dataset. Random Forest (RF) Model outperformed other models and achieved predicted results during the three framework phases. With clinical significance features selection, over-sampling methods (SMOTE and ADASYN) achieved the highest AUC results (98% with CI 95%: 95.3–100%, and 100% respectively). The combination of Over-sampling and under-sampling achieved the second-highest AUC results (98%, with CI 95%: 95.3–100%, and 97%, CI 95%: 93.7–100% SMOTE-Tomek, and SMOTE-ENN respectively). Under-sampling methods reported the least important AUC results (50%, with CI 95%: 40.2–59.8%) for both (ENN and Tomek- Links). Using ML explainable technique called SHAP, we explained the outcome of the predictive model (RF) with SMOTE class balancing technique to understand the most significant clinical features that contributed to predicting lung cancer LOS with the RF model. Our promising framework allows us to employ ML techniques in-hospital clinical information systems to predict lung cancer admissions into ICU

Artificial Intelligence: Development and Applications in Neurosurgery

The last decade has witnessed a significant increase in the relevance of artificial intelligence (AI) in neuroscience. Gaining notoriety from its potential to revolutionize medical decision making, data analytics, and clinical workflows, AI is poised to be increasingly implemented into neurosurgical practice. However, certain considerations pose significant challenges to its immediate and widespread implementation. Hence, this chapter will explore current developments in AI as it pertains to the field of clinical neuroscience, with a primary focus on neurosurgery. Additionally included is a brief discussion of important economic and ethical considerations related to the feasibility and implementation of AI-based technologies in neurosciences, including future horizons such as the operational integrations of human and non-human capabilities

Integrated Neuromusculoskeletal Modeling within a Finite Element Framework to Investigate Mechanisms and Treatment of Neurodegenerative Conditions

Neurodegenerative and neurodevelopmental disorders are a group of conditions that stem from irregularities in the nervous system that lead to complications in function and movement. The goal of this work is to develop computational tools that: (1) measure the accuracy of surgical interventions in neurodegenerative and neurodevelopmental conditions, and (2) integrate neural and musculoskeletal frameworks to provide a platform to better investigate neurodegenerative and neurodevelopmental disorders. Parkinson’s disease (PD) is a neurodegenerative condition projected to affect over 1.2 million people by 2030 in the US. It is caused by atypical firing patterns in the basal ganglia region of the brain that leads to primary motor symptoms of tremor, slowness of movement, and rigidity. A potential treatment for PD is deep brain stimulation (DBS). DBS involves implanting electrodes into central brain structures to regulate the pathological signaling. Electrode placement accuracy is a key metric that helps to determine patient outcomes postoperatively. An automated measurement system was developed to quantify electrode placement accuracy in robot-assisted asleep DBS procedures (Chapter 2). This measurement system allows for precise metrics without human bias in large cohorts of patients. This measurement system was later modified to measure screw placement accuracy in spinal fusion procedures for the treatment of degenerative musculoskeletal conditions (Chapter 3). DBS is an effective treatment for PD, but it is not a cure for the cause of the disease itself. To cure neurodegenerative and neurodevelopmental diseases, the underlying disease mechanisms must be better understood. A major limitation in studying neural conditions is the infeasibility of performing in vivo experiments, particularly in humans due to ethical considerations. Computational modeling, specifically fully predictive neuromusculoskeletal (NMS) models, can help to accumulate additional knowledge about neural pathways that cannot be determined experimentally. NMS models typically include complexity in either the neuromuscular or musculoskeletal system, but not both, making it difficult or infeasible to investigate the relationship between neural signaling and musculoskeletal function. To overcome this, a fully predictive NMS model was developed by integrating NEURON software within Abaqus, a finite element (FE) environment (Chapter 4). The neural model consisted of a pool of motor neurons innervating the soleus muscle in a FE human ankle model. Software integration was verified against previously published data, and the neuronal network was verified for motor unit recruitment and rate coding, which are the two principles required for in vivo muscle generation. To demonstrate the applicability of the model to study neurodegenerative and neurodevelopmental diseases, a fully predictive mouse hindlimb NMS model was developed using the integrated framework to investigate Rett syndrome (RS) (Chapter 5). RS is a neurodevelopmental disorder caused by a mutation of the Mecp2 gene with hallmark motor symptoms of a loss of purposeful hand movement, changes in muscle tone, and a loss of speech. Recent experimental analysis has found that the axon initial segment (AIS) in mice that model RS has torsional morphology compared to wildtype littermate controls. The effects these neural morphological changes have on joint motion will be studied using the mouse NMS model. This work encompasses a range of research that uses computational models to study the underlying mechanisms and design targeted treatment options for neurodegenerative and neurodevelopmental disorders. The outcomes of this work have quantified the accuracy at which surgical interventions for these conditions can be performed and have resulted in a neuromusculoskeletal model that can be applied to understand how neural morphology, and associated changes due to these disorders, affects musculoskeletal function

Fusion, 2023

https://hsrc.himmelfarb.gwu.edu/smhs_fusion/1015/thumbnail.jp

The Convergence of Human and Artificial Intelligence on Clinical Care - Part I

This edited book contains twelve studies, large and pilots, in five main categories: (i) adaptive imputation to increase the density of clinical data for improving downstream modeling; (ii) machine-learning-empowered diagnosis models; (iii) machine learning models for outcome prediction; (iv) innovative use of AI to improve our understanding of the public view; and (v) understanding of the attitude of providers in trusting insights from AI for complex cases. This collection is an excellent example of how technology can add value in healthcare settings and hints at some of the pressing challenges in the field. Artificial intelligence is gradually becoming a go-to technology in clinical care; therefore, it is important to work collaboratively and to shift from performance-driven outcomes to risk-sensitive model optimization, improved transparency, and better patient representation, to ensure more equitable healthcare for all