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An Overview of Regulatory Affairs, Causal Inference, Safe and Effective

Health Care in Machine Learning for Bio-Statistical Services

Dr. Nancy Agens, Head,

Technical Operations, Pubrica

sales@pubrica.com

In Brief

Over the past few years, the magnitude of

machine learning in the field of healthcare

delivery setting becomes plentiful and

captivating. Many regulatory sectors

noticing these developments and the FDA

has been appealing to provide bet machine

learning services with safe and productive

use. Despite having the limitations in

software-driven products, FDA leads to

giving a significant benefit of causal

inference for the development of machine

learning. FDA is giving suggestions to

provide well equipped regulated products.

Pubrica is here to help you with the

regulated for Bio-statistical consulting

services.

Keywords: Clinical biostatistics services,

biostatistics consulting services,

biostatistics CRO, Statistical

Programming Services, Bio-statistical

Services, biostatistics consulting firms,

Biostatistics for clinical research, statistics

in clinical trials, biostatistics in clinical

trials, biomedical studies, Biostatistics

Support Service, machine learning

services, healthcare services, machine

learning analysis.

I. INTRODUCTION

The significance of machine learning has

evolved globally, especially in th field of

medical and healthcare sectors. Many tools

are significant for various purposes likes

diagnosis, software tools for many clinical

findings in multiple areas. The machine

learning paves an easier way to clinical Bio-

statistical services using many software

tools. It creates an excellent standard on

radiology and cardiology and improves the

patient’s medical issues rapidly, more

comfortable decision making in clinical

trials. All these maintained by drafting a set

of regulations by various government

sectors around the world.

II. REGULATIONS FOR SAFE AND

EFFECTIVE HEALTH CARE MACHINE

LEARNING

FDA (food and Drug Administration)

FDA is a regulatory organization there to

perform the quality of any medical or

clinical testing equipment, medicines, or any

food-related products. FDA is looking to

provide the best facilities in health care

sectors through machine-learning artificial

intelligence services for the statistical

programming services. Though it is not an

urgent need for ML-driven tools, there are

few benefits of using ML-driven tools in

medical fields, says FDA

Applications

Instrumental usage

Machine implementation

Invitro reagents implantation

technology

Diagnostic kit

Copyright © 2020 pubrica. All rights reserved 2

Treatment for humans and animals.

FDA definition

The usage of ML can provide both physical

equipment and software tools. This software

device is known as SiMD (software in a

medical device). International medical

device regulators verify these software-

driven tools.

Challenges in SiMD

Cybersecurity

Management of data

Collection of data

Protecting information

To create opportunities in patient’s care

Limitations:

For some reasons, the FDA does not

regulate two applications of ML systems.

They are

Clinical design support software(CDS)

Laboratory developed tests.

The actual reason for exempting these uses

are CDS provide instance decision making,

which may affect in the future. On the other

side Laboratory, developed tests can access

only one available health care. FDA cannot

regulate these type of software.

Last year FDA released a paper after

conducting a serious discussion with the

regulatory members and proposed

“Regulatory Framework for Modifications

to Artificial Intelligence/Machine Learning

(AI/ML)-based Software as a Medical

Device.” for statistics in clinical research. It

includes some premarket research products

approval procedures that would delay the

ML process. Many Bio-statistical firms

raised few critics against it.

The objective of the proposal is to give

access to real-world data using ML

products more efficiently with some

regulatory barriers. It also includes some

real-world affirmations. Many people could

not be able to recognize this proposal. To

overcome this, the FDA officials spoke to

the public to create awareness about the

“approach of regulating algorithms”.

Regardless of all benefits and limitations,

ML is facing challenges in the development

of the safe and efficient product. Some of

the challenges are

ML identifications

ML predictions

ML recommendations

ML algorithms for diagnostic tools

To overcome this, Subbaswamy and Saria

provide some potential remedies by

discussing the statistical foundations in the

Bio-statistical analysis. Data curation of

individual patient’s health raises questions

for request algorithms to give a more

specific context.

Transfer learning

The process of learning a task from the

already-completed job through knowledge

transfer is called transfer learning.

However, this process is complicated. The

datasets can affect the algorithms, resulting

in the false provisional services in health

care analysis. This process is not allowed in

the medical sectors.

Biomarkers in FDA

In the process of validation of a biomedical

tool, biomarker validation is mandatory in

the clinical research services. There are so

many parameters for qualifying a

biomarker. The casual inference is a novel

digital biomarker validation.

An ML algorithm that detects the patient’s

therapy benefits may not be relevant unless

a casual inference tool access in that

biomarker. Some make a precise diagnosis

and treatment recommendations to

understand the factors in ML algorithms.

The production of digital biomarkers facing

more challenges to incentivizing parties in

health care sectors. R&D validated provide

significance in delivery of healthcare

services. Studies say that statistician’s tool

kit has grown fast, and various technical

tools have a development for causal

inference of machine learning in biomedical

investigations and reviews.

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III. CONCLUSION

Wrapping up, in a complex environment,

the role of regulatory affairs in biomedical

studies for machine learning is essential.

One of the easiest ways to support the

regulators is the usage of biomarkers in

healthcare tools. These regulations help to

provide better healthcare services under the

guidance of pubrica.

REFERENCES

1. Stern, A. D., & Price, W. N. (2020). Regulatory

oversight, causal inference, and safe and effective

health care machine learning. Biostatistics, 21(2),

363-367.

2. Cleland-Huang, J., Czauderna, A., Gibiec, M.,

&Emenecker, J. (2010, May). A machine learning

approach for tracing regulatory codes to product-

specific requirements. In Proceedings of the 32nd

ACM/IEEE International Conference on Software

Engineering-Volume 1 (pp. 155-164).

3. Hwang, T. J., Kesselheim, A. S., &Vokinger, K. N.

(2019). Lifecycle Regulation of Artificial

Intelligence–and Machine Learning-Based Software

Devices in Medicine. Jama, 322(23), 2285-2286.

4. Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu,

V. X., Doshi-Velez, F., ...&Ossorio, P. N. (2019).

Not harm: a roadmap for responsible machine

learning for health care. Nature medicine, 25(9),

1337-1340.