Jon Patrick

The AMIA 2022 AI Showcase has been devised as a 3-stage submission process where participants will be assessed at submissions 1 was 2 for their progression to the 2nd and 3rd presentations. The stages coincide with the three AMIA conferences:

Informatics Summit, March 21-24, 2022; Clinical Informatics Conference (CIF), May24-26 2022; and, the Annual Symposium, November 5-9, 2022.

Submission requirements for each stage are:

Stage 1. System description, results from a study of algorithm performance, and an outline of the methods of the full evaluation plan

Stage 2. Submission to address usability and workflow aspects of the AI system, including prototype usability or satisfaction, algorithm explainability, implementation lessons, and/or system use in context.

Stage 3. Submissions to summarize the comprehensive evaluation to include research from prior submissions with new research results that measure the impact of the tool.

Now that we have the abstracts for the Stage 1 conference we can analyse the extent to which the community of practice could satisfy those criteria. As well we can identify how the abstracts might fall short of the submission requirements and so aid the authors in closing the gap between their submission and the putative requirements. As well they will be able to ensure their poster or presentation fill some of the gaps where they have the information.

There is also the open question of what effect the “call to arms” in the Showcase was effective and how it might be improved in the future.

A succinct definition of the desirable contents of an Abstract right well be:

1. What was done?
2. Why was it done?
3. What are the results?
4. What is the practical significance?
5. What is the theoretical significance?

The Clinical AI Showcase has now provided a set of abstracts for the presentations and posters so how might we rate the informativeness of the abstracts against these criteria.

A review of the 22 abstracts has been conducted and a summary based on these 5 content attributes with 4&5 being merged has been provided in Table 2. The summaries have been designated by their key themes and collated into Table 1.

Using the publicised criteria for each of the three Stages of the AI Showcase it would appear that only 9 of the submissions are conformant, i.e. the papers categorised as Extended ML model, Development of ML model and Developed and Deployed. It is an open question as to whether the abstracts classed as Comparison of ML models fulfils Stage 1 criteria. The authors would do well to clarify the coverage of their work in their full presentation/poster.

The other categories appear to exist in a certain type of limbo. The Methods study only group appears out of place given the objectives of the AI Showcase. The Design only group would appear to be stepping out somewhat prematurely, although the broader advertising for the Showcase certainly encouraged early stage entries. As mentioned in my previous blog (blood reference) it would be exceedingly difficult for teams to meet the purported content for deadlines for the Stages 1 and 2 if the ideas for the project are in an embryonic design stage.

Teams with nearly or fully completed projects were able to submit works that also fulfilled many of the criteria for Stage 2 of the Showcase. The Developed and Deployed group showed progression in their projects that had reached deployment but in no case reported usability or workflow aspects with the exception of one paper that claimed their solution was installed at the bedside.

Two abstracts did not describe clinical applications of their ML but rather secondary use and these papers were doing NLP.

Good Abstract Writing

Most abstracts provided reasonable descriptions of the work they had done or intended to do. It was rare for abstracts to describe their results or the significance of their work, this undoubtably can be corrected in Stages 2 or 3 of the Showcase where they are required to report on their assessment of their tools practical use. Only one paper provided information on all four desirable abstract content items.

What can the Showcase Learn and do better

This Showcase has the admirable objective of encouraging researchers and clinical teams to perform AI projects to a better quality and in a more conclusive manner. However its Stages cover a cornucopia of objectives set out in a timeline that is unrealistic for projects just starting and poorly co-ordinated for projects near to or at completion. This is well evidenced by the some 40+ ML projects included in the Conference programme that are not part of the Showcase. If the Showcase is to continue, as it should, then a more considered approach to staged objectives, encouragement of appropriate teams, and more thoughtful timing would be a great spur to its future success.

Might I Humbly Suggest (MIHS) that a more refined definition of the stages be spelled out so that

            a. groups just starting ML projects are provided with more systematic guidelines and milestones, and;

            b. that groups in the middle of projects can ensure that they have planned for an appropriate level of completeness to their work.

Stage 1. What is the intended deliverable and Why it is necessary – Which clinical community has agreed to deployment and assessment.

Stage 2. What was done in the development – How the deliverable was created and what bench top verifications were conducted.

Stage 3. Deployment and Clinical Assessment – What were the issues in deployment. What were the methods and results of the clinical assessment. What arrangements have been made for the maintenance and improvement of the deliverable.

This definition excludes groups performing ML projects purely for their own investigative interest but without a specific participating clinical community. The place for their work is within the general programme of AMIA conferences. It also means that strictly speaking only 3 of the current acceptances would fit this definition for Stage 1, although 3 of the others could be contracted to fit this definition.

A concerning factor in the current timeline design is the short 2-month span between the deliverables for Stages 1 and 2. A group would pretty much have to have completed Stage 2 to submit to Stage 1 and be ready to submit to Stage 2 in 2 months.

Lastly the cost of attending 3 AMIA conferences in the one year would be excessively taxing especially to many younger scholars in the field. AMIA should provide a two-thirds discount to each conference to those team representatives who commit to entering the Showcase. This would be a great encouragement to get more teams involved.

Paper Topic	N
Design only	3
Comparison of ML models	5
Extended ML model	1
Development of ML model	5
Developed and Deployed – No operational assessment	3
Methods study only	1
Abstract Unavailable	2
TOTAL	20

Paper	What was done	Why was it done	What are their results	What is the significance	Comments	Category
Overgaard – CDS Tool for asthma – Presentation	Design of desirable features of AI solution.	to make review of the patient record more efficient.	Unspecified	Unknown – no clinical deployment.	Paper is about the putative design for a risk assessment tool and data extraction from the EHR.	Design only
Rossi – 90-day Mortality Prediction Model – Presentation	Deployed 90-day mortality prediction model. **	to align of patient preferences for advance care directives with therapeutic delivery, and improve rates of hospice admission and length of stay.	Unspecified	Unknown – clinical deployment planned.	Model is partially implemented with operationally endorsed workflows.	Development of ML model – Planned Deployment
Estiri – Unrecognised Bias in COVID Prediction Models – Presentation	Investigation of four COVID-19 prediction models for bias using an AI evaluation framework. **	AI algorithm biases could exacerbate health inequalities	Unspecified	Unknown – no clinical deployment.	Two bias topics are defined : (a) if the developed models show biases; (b) has the bias changed over time for patients not used in the development.	Comparison of ML models
Liu – Explainable ML to predict ICU Delerium – Presentation	A range of features were identified and a variety of MLs evaluated. Three prediction models were evaluated 6,12,24 hours. ****	To more accurately predict the onset of delirium	Described but numerics not provided	Implied due to described implementation design	Paper describes some aspect of all characteristics but not always completely.	Comparison of ML models
Patel – Explainable ML to predict periodontal disease – Presentation	“new” variables added to existing models revealed new associations.	Discover new information about risk factors.	Described but associations not provided	Unknown. No clinical assessment of the discovered associations, no clinical deployment.	AI methods not described.	Extended ML model
Liang – Wait time prediction for ER/ED – Virtual	Develop ML classifier (Tensorflow) to predict ED/ER wait times. Training and test sets described.	No explanation	Unspecified	unknown -clinical deployment status unclear	Focus is on the ML processes with little other information	Development of ML model
Patrick – Deep Understanding for coding pathology reports – Virtual	Built a system to identify cancer pathology reports and code them for 5 data items (Site, Histology, Grade, Behaviour, laterality)	California Cancer registry requested an automated NLP pipeline to improve production line efficiencies.	Various accuracies provided	Improvements over manual case identification and coding provided.	The work of this blog author.	Developed and Deployed – no operational assessment – Not clinical application
Eftekhari/Carlin – ML sepsis prediction for hematopoietic cell transplant recipients – Poster	Deployed an early warning system that used EMR data for HCT patients. Pipeline of processing extending to clinal workflows.	Sepsis in HCT patients has a different manifestation to sepsis in other settings.	Only specified results is deployment	Unknown – no clinical assessment.	Deployment is described showing its complexity. No evaluations.	Developed and Deployed – no operational assessment
Luo – Evaluation of Deep Phenotype concept recognitions on external EHR datasets – Poster	Recognises human phenotype Ontology concepts in biomedical al texts	No explanation	Unspecified	Unknown – no clinical deployment.	Abstract is the least informative. One sentence only.	Development of ML model – Not clinical application
Pillai – Quality Assurance for ML in Radiation Oncology – Poster	Five ML models were built and voting system devised to decide if a radiology treatment plan was Difficult or No Difficult. Feature extraction was provided.	To improve clinical staffs scrutiny of difficult plans to reduce errors downstream. Feature extraction to improve interpretability and transparency. ****	Unspecified	System planned to be integrated into clinical workflow.	Mostly about ML approach but shows some forethought into downstream adoption.	Comparison of ML models – Deployment planned
Chen – Validation of prediction of Age-related macular degeneration – Poster	ML Model to predict later AMD degeneration using 80K images from 3K patients.	to predict the risk of progression to vision-threatening late AMD in subsequent years	Unspecified	Unknown – no clinical deployment.	Focus is on the ML processes with little other information.	Development of ML model
Saleh – Comparison of predictive models for paediatric deterioration – Poster	Plan to develop and implement ML model to augment prediction of paediatric clinical deterioration within the clinical workflow.	Detecting deterioration in paediatric cases is effective at only 41% using existing tools.	Unspecified – planning stage only	System planned to be integrated into clinical workflow.	Early conceptualisation stage. Well framed objective and attentive to clinical acceptability. No framing of datasets, variables and ML methods.	Design only
Shah – ML for medical education simulation of chest radiography – Poster	not available
Mathur – Translational aspects of AI – Poster	Evaluation of the TEHAI framework compared to other frameworks for AI with emphasis on translational and ethical features of model development and its deployment.	A lack of standard training data and the clinal barriers to introducing AI into the workplace warrant the development of a AI evaluation framework.	Qualitative assessment of 25 features by reviewers.	No in vitro evaluation – only qualitative assessment	This is an attempt to improve the evaluation criteria we should be suing on AI systems. it fails to make convincing case it isa better method than alternatives.	Methods study only
Yu – Evaluating Pediatric sepsis predictive model – Poster	not available
Tsui – ML prediction for clinical deterioration and intervention – Poster	Built a ML for an Intensive care warning system for deterioration events and pharmacy interventions. It uses bedside monitor, and EHR data providing results in real-time.	No explanation	Unspecified	Unknown – no clinical assessment.	Operational system. Only a description of the deliverables – no evaluations	Developed and Deployed – no operational assessment
Rasmy – Evaluation of DL model for COVID-19 outcomes – Poster	A DL algorithm developed to predict for COVID-19 cases on admission: in-hospital mortality, need for mechanical ventilation, and long hospitalization.	No explanation	Unspecified – no numerics supplied	unknown – no clinical deployment	Seems to concentrate solely on the DL modelling.	Development of ML model
Wu – ML for predicting 30-day cancer readmissions – Poster	ML models built to identify 30-day unplanned admissions for cancer patients.	Unplanned dance readmissions have significantly poorly outcomes so the aim is to reduce them.	No Results but promised in the poster/presentation	Unknown – no clinical assessment.	No ML details just a justification in Abstract	Comparison of ML models
Mao – DL model for Vancomycin monitoring – Poster	A DL pharmacokinetic model for Vancomycin was compared to a Bayesian model	To provide a more accurate model of Vancomycin monitoring.	The DL model performed better than the Bayesian model.. No numerics provided.	Unknown – no clinical assessment.	Focus is on the ML processes with little other information.	Comparison of ML models
Ramnarine – Policy for Mass Casualty Trauma triage – Poster	Design of a strategy to build an ML for ER/ED triage and retriage categorisation for mass casualty incidents	To fill a void in national standards for triage and retriage	Unspecified – design proposal only	Unknown – no clinical assessment of practicality of acceptance.	This is a proposal with no concrete strategy for implementation and what would be used in the investigation from either a data source of ML strategy type.	Design only

Modern language processing technologies have arisen from two separate pathways in algorithm history.

The first language processing efforts in the 1960’s were conducted by linguists using early computers to process linguistic structures. The linguists built algorithms that identified parts of speech and parsed sentences according to language structure. As this field developed it became known as Computational Linguistics and continues today as a professional research community.

In 1990’s the computer scientists introduced machine learning methods into the field of computational linguistics following the spirit of corpus linguistics where they studied the statistical characteristics of corpora. These methods  known as Statistical Natural Language Processing became an  important subfield to what had also become known as Natural Language Processing (NLP).

In the second half of the ‘90s the rise of Google spurred the adoption of Text Mining, an approach that ignored the linguistics character of language and worked on just the text strings. This has become a fashionable approach due to the ease with which programmers can become engaged as enthusiasts in the processing strategies without the need to engage in the field of linguistics. This approach has become so popular it has assumed the title of NLP so today NLP is a title that is a conflation of two methodological paradigms.

Google has also fuelled a new approach since the early 2010s with its development of neural net machine learning methods called Deep Learning. This fits with their general approach of using extremely large data sets to characterize language texts. Whilst this approach has shown some useful features it is not universally applicable and some of its limitations are now beginning to emerge.

The two heritages of language processing are now Computational Linguistics and Text Mining each having separate origins and algorithmic philosophies and each now claiming the rubric of Natural Language Processing.

However algorithmic processing of text is not an end in itself but rather part of a journey to a functionality that is of value to users. The two most dominant user needs are to recognize the nature of a document (aka document classification), and, identify entities of some interest within the text (aka Semantic Entity Recognition). But the meaningfulness of this processing comes about from how the NLP analysis is then utilized, not the NLP of itself. It is this use of the NLP analysis, whether it be from the computational linguistics paradigm or the text mining paradigm, that creates the value for the user.

• DEEP UNDERSTANDING takes its heritage from computational linguistics and for processing clinical documents moves beyond text mining methods to bring to bear the full value of:
• coalescing grammatical understanding, semantic entity recognition, extensive clinical knowledge stores, and machine learning algorithmic power,
• so as to produce some transform of the native textual prose into some meaningful conceptual realm of interest that mimics the work of people who have the responsibility to interpret the clinical texts,
• at an accuracy level equal to or better than the human experts who do the task, and,
• knows its own limitations and passes the task back to the human processor when it can’t complete the task accurately enough, and
• provides an automatic feedback mechanism to identify potential errors to improve performance as the body of processing materials grows, so as to sustain Continuous Process Improvement.

An example is the work we do for cancer registries that need to translate cancer pathology reports into an agreed set of codes according to an international coding system known as the International Classification of Diseases – Oncology Version 3 (ICD-O-3).

Our Deep Understanding pipeline consists of 4 major components as shown in the diagram:

1. A machine learning document classifier which determines if a pathology report is identified as a cancer report or not.

2. A clinical entity recognizer that is a machine learner that identifies forty two different semantic classes of clinical content needed for correctly coding the report.

3. A coding inductive inference engine which holds the knowledge of the ICD-O-3 codes and many hundreds of prescribed rules for coding. It receives access to the text annotated for clinical entities and applies computational linguistics algorithms to parse the text and map the entities to the classification content to arrive at ICD-O-3 codes. The task is complicated by needing to code for 5 semantic objects:

Semantic Object	Number of Codes
Body site for the location of the cancer in the body	300+
Disease histology	800+
Behaviour of the cancer	5
Grade or severity of the disease	20+
Laterality, or side of the body where the disease is located, if a bilateral organ	6

4. A process to cycle knowledge learnt from the operational use of the pipeline process back to the machine learning NLP components to maintain Continuous Process Improvement (CPI) or Continuous Learning.

5. Each process has been periodically retrained and tuned over time to attain an accuracy rivalling and on some aspects bettering human coding performance.

While Deep Learning is a new and innovative method for learning the characteristics of a large corpus of texts it can be and usually is only an NLP subsystem of a larger processing objective. If that objective is to compute an understanding of the corpus content to the extent it can intelligently transform the text into higher order conceptual content to an accuracy level achieved by humans, then it is performing DEEP UNDERSTANDING.

There is considerable amount of talk about bias in AI as applied to clinical settings and how to eliminate it. This problem has now attracted the attention of standards bodies with a view to legislating how AI systems should be validated and certified.

AI consists of three types of logic: inductive, deductive and abductive.

However, most modern references to AI are really talking about the inductive type better known within the industry as Machine Learning (ML), which allocates data objects into classes based on the statistical distribution of their features.

Bias in machine learning cannot be eliminated as it is an intrinsic aspect of the method. ML uses a sample of data objects for which it has their features or attribute values and knows their correct classes, and so trains a classification predictive model on this training data. Hence, biases in collecting the sample are intrinsic.

But other biases apart from the data collection process are also introduced along the pathway of developing a working ML predictive classifier.

I don’t believe you can make learning algorithms bias-free. Just like drugs where there is always a percentage of people who have an adverse reaction, so there will be a percentage of people for whom the classifier prediction will be wrong because of:

a. The incompleteness of the learning sets;
b. The conflicting and contradictory content within the training set;
c. The limitations of the learning model to represent the distributions in the training data;
d. The choice of attributes to represent the training examples;
e. The normalisations made of the training data;
f. The time scope of the chosen training data;
g. The limitations of the expertise of those experts deciding on the gold standard values;
h. The use of “large data” sets that are poorly curated, where “good data” is needed.
etc.

If legislators are going to make the AI technology regulation-free, then they should at least require the algorithms to be provided on an accessible web site where “users” can submit their own data to test its validity for their own data sets. Then the users can have confidence or not that the specific tool is appropriate for their setting.

Training set data is typically collected in one of two ways.

1. Curated data collection: careful election and curation of data examples to span a specific range of variables. Its most serious drawback is the effort of manual labour to collate and curate the data collection and hence it is popular for more targeted objectives using smaller training sets.

2. Mass data collection: data is collected en masse from a range of sources and data elements are captured by “deep learning” strategy of compiling large feature vectors for each classification object using an automatic method of collation and compilation.

This approach is popular because it can be highly automated and supports belief in the fallacy that more data means better quality results.

What we don’t need is more “big data”, but rather we need more “good data”.

How do we get GOOD DATA?

The delivery of a machine learning processing system into production means a supply of potential learning material is flowing through the system. Any sensible and well-engineered system will have a mechanism for identifying samples that are too borderline to safely classify. We divert those samples into a Manual processing category.

The challenge with the materials in the Manual class is how to utilise them for improvement in the resident system. A good deal of research has gone into the process of Active Learning, which is the task of selecting new materials to add to the training materials from a large set of untrained samples.

There are two major dimensions to this selection process: Samples that fall near the class boundaries, and samples that represent attribute profiles significantly different to anything in the training materials.

Automated detection of both of these types of samples requires two different analytical methods known as Uncertainty Sampling and Diversity Sampling respectively. An excellent text on these processes is by Robert Munro, Human-in-the-Loop-Machine Learning, MEAP Edition Version 7, 2020, Manning Publications.

GOOD DATA can be accumulated through a strategy of Continuous Process Improvement (CPI) and any worthwhile clinical Machine Learning system will need to have that in place, otherwise the system is trapped in an inescapable universe of self-fulfilling prophecies with no way of learning what it doesn’t know, and should know, to be safe. A clinical ML system without CPI is hobbled by “unknown unknowns” which can lead to errors, both trivial and catastrophic.